Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays.

Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users' fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users' head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users' Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users' head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches.

SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction.

OBJECTIVE: A set of cardiac MRI short-axis image dataset is constructed, and an automatic segmentation based on an improved SegNet model is developed to evaluate its performance based on deep learning techniques. METHODS: The Affiliated Hospital of Qingdao University collected 1354 cardiac MRI between 2019 and 2022, and the dataset was divided into four categories: for the diagnosis of cardiac hypertrophy and myocardial infraction and normal control group by manual annotation to establish a cardiac MRI library. On the basis, the training set, validation set and test set were separated. SegNet is a classical deep learning segmentation network, which borrows part of the classical convolutional neural network, that pixelates the region of an object in an image division of levels. Its implementation consists of a convolutional neural network. Aiming at the problems of low accuracy and poor generalization ability of current deep learning frameworks in medical image segmentation, this paper proposes a semantic segmentation method based on deep separable convolutional network to improve the SegNet model, and trains the data set. Tensorflow framework was used to train the model and the experiment detection achieves good results. RESULTS: In the validation experiment, the sensitivity and specificity of the improved SegNet model in the segmentation of left ventricular MRI were 0.889, 0.965, Dice coefficient was 0.878, Jaccard coefficient was 0.955, and Hausdorff distance was 10.163 mm, showing good segmentation effect. CONCLUSION: The segmentation accuracy of the deep learning model developed in this paper can meet the requirements of most clinical medicine applications, and provides technical support for left ventricular identification in cardiac MRI.

Development of a Sensitive and Fast Determination Method for Trace Carbaryl Residues in Food Samples Based on Magnetic COF (TpPa-NH2)@Fe3O4 Nanoparticles and Fluorescence Detection.

Developing a simple and effective method for measuring carbaryl residues in food is urgent due to its widespread use and the associated health risks in agriculture, as well as various defects in existing detection techniques. The COF (TpPa-NH2)@Fe3O4 nanocomposite (amino modification) was synthesized via a two-step method and used as an adsorbent for the extraction of carbaryl from food samples in this study. The results indicated that COF (TpPa-NH2)@Fe3O4 can rapidly and successfully capture carbaryl directly from samples via π-π stacking and hydrophobic interactions, achieving maximum adsorption within 5 min under a small adsorbent quantity using a fluorescence spectrophotometer. Under the optimized conditions, carbaryl exhibited good linearity in the range of 0.2-120 µg·kg-1, and the limit of detection was 0.012 µg·kg-1. The recoveries of the samples were 96.0-107.4%. This method has broad application prospects for the monitoring of carbaryl in food.

Dissecting Latency in 360° Video Camera Sensing Systems.

360° video camera sensing is an increasingly popular technology. Compared with traditional 2D video systems, it is challenging to ensure the viewing experience in 360° video camera sensing because the massive omnidirectional data introduce adverse effects on start-up delay, event-to-eye delay, and frame rate. Therefore, understanding the time consumption of computing tasks in 360° video camera sensing becomes the prerequisite to improving the system's delay performance and viewing experience. Despite the prior measurement studies on 360° video systems, none of them delves into the system pipeline and dissects the latency at the task level. In this paper, we perform the first in-depth measurement study of task-level time consumption for 360° video camera sensing. We start with identifying the subtle relationship between the three delay metrics and the time consumption breakdown across the system computing task. Next, we develop an open research prototype Zeus to characterize this relationship in various realistic usage scenarios. Our measurement of task-level time consumption demonstrates the importance of the camera CPU-GPU transfer and the server initialization, as well as the negligible effect of 360° video stitching on the delay metrics. Finally, we compare Zeus with a commercial system to validate that our results are representative and can be used to improve today's 360° video camera sensing systems.

Relationship Between Length and Curvature of Ascending Aorta and Type a Dissection.

Background: Type A aortic dissection (TAAD) has a rapid onset and high mortality. Currently, aortic diameter is the major criterion for evaluating the risk of TAAD. We attempted to find other aortic morphological indicators to further analyze their relationships with the risk of type A dissection. Methods: We included the imaging and clinical data of 112 patients. The patients were divided into three groups, of which Group 1 had 49 patients with normal aortic diameter, Group 2 had 22 patients with ascending aortic aneurysm, and Group 3 had 41 patients with TAAD. We used AW Server software, version 3.2, to measure aorta-related morphological indicators. Results: First, in Group 1, the univariate analysis results showed that ascending aortic diameter was correlated with patient age (r 2 = 0.35) and ascending aortic length (AAL) (r 2 = 0.43). AAL was correlated with age (r 2 = 0.12) and height (r 2 = 0.11). Further analysis of the aortic morphological indicators among the three groups found that the median aortic diameter was 36.20 mm in Group 1 (Q1-Q3: 33.40-37.70 mm), 42.5 mm in Group 2 (Q1-Q3: 41.52-44.17 mm) and 48.6 mm in Group 3 (Q1-Q3: 42.4-55.3 mm). There was no significant difference between Groups 2 and 3 (P > 0.05). Group 3 had the longest AAL (median: 109.4 mm, Q1-Q3: 118.3-105.3 mm), followed by Group 2 (median: 91.0 mm, Q1-Q3: 95.97-84.12 mm) and Group 1 (81.20 mm, Q1-Q3: 76.90-86.20 mm), and there were statistically significant differences among the three groups (P < 0.05). The Aortic Bending Index (ABI) was 14.95 mm/cm in Group 3 (Q1-Q3: 14.42-15.78 mm/cm), 13.80 mm/cm in Group 2 (Q1-Q3: 13.42-14.42 mm/cm), and 13.29 mm/cm in Group 1 (Q1-Q3: 12.71-13.78 mm/cm), and the difference was statistically significant in comparisons between any two groups (P < 0.05). Regression analysis showed that aortic diameter + AAL + ABI differentiated Group 2 and Group 3 with statistical significance (area under the curve (AUC) = 0.834), which was better than aortic diameter alone (AUC = 0.657; P < 0.05). Conclusions: We introduced the new concept of ABI, which has certain clinical significance in distinguishing patients with aortic dissection and aneurysm. Perhaps the ascending aortic diameter combined with AAL and ABI could be helpful in predicting the occurrence of TAAD.

Upregulation of long non-coding RNA myocardial infarction-associated transcription is correlated with coronary artery stenosis and elevated inflammation in patients with coronary atherosclerotic heart disease.

Coronary atherosclerotic heart disease (CAD) is a chronic disease caused by multiple risk factors. Aberrant expression of long non-coding RNAs (lncRNAs) has been regarded as an independent risk factor of CAD. This study evaluated lncRNA myocardial infarction-associated transcription (MIAT) expression in CAD patients and its clinical significance. Totally, 155 CAD patients and 76 non-CAD controls were enrolled. MIAT expression was detected using reverse transcription quantitative polymerase chain reaction. The clinical diagnostic significance of MIAT was evaluated by plotting the receiver operating characteristic (ROC) curve. The levels of inflammatory cytokines were detected using enzyme-linked immunosorbent assay. microRNA (miR)-29b-3p expression and pregnancy-associated plasma protein A (PAPPA) level were detected. MIAT expression in CAD patients (4.23 [1.22-6.50]) was higher than that in non-CAD controls (1.64 [0.05-2.93]) (p < 0.01) and had an independent correlation with CAD. The area under ROC curve of predicting CAD was calculated as 0.790, the specificity as 71.40%, and the sensitivity as 70.00%. MIAT expression was positively correlated with the C-reactive protein level (r = 0.769, p < 0.0001) and pro-inflammatory cytokines tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), and IL-8 levels, while negatively correlated with the anti-inflammatory cytokine IL-10. MIAT was positively correlated with Gensini score and had an independent correlation with it. LncRNA MIAT sponged miR-29b-3p and miR-29b-3p targeted PAPPA. In conclusion, lncRNA MIAT was upregulated in the peripheral blood of CAD patients and elicited clinical diagnostic significance. MIAT participated in the development of CAD via miR-29b-3p/PAPPA axis. This study provides insights into a potential target for the diagnosis and treatment of CAD.

A Chinese Traditional Therapy for Bleomycin-Induced Pulmonary Fibrosis in Mice.

Pulmonary fibrosis is a chronic and fatal disease of lung tissue with high incidence and mortality in the world. The exploration of effective treatment for pulmonary fibrosis remains an urgent challenge. In our study, Qingfei Xieding was investigated as a novel Chinese traditional patent medicine against pulmonary fibrosis. A pulmonary fibrosis mouse model was constructed by injecting with bleomycin sulfate. Following Qingfei Xieding administration, lung samples were collected to assess pulmonary phenotype changes by analyzing lung coefficient, wet/dry, and histopathologic section. Levels of nitric oxide (NO), hydroxyproline (HYP), malondialdehyde (MDA), and total antioxidant capacity were measured to evaluate the degree of oxidation. A single-cell gel electrophoresis (SCGE) assay was used to evaluate bleomycin-induced DNA damage. Western blotting and real-time quantitative PCR were performed to determine the abundance of inducible nitric oxide synthase (iNOS), connective tissue growth factor (CTGF), alpha smooth muscle actin (α-SMA), and fibronectin (FN). In the present study, Qingfei Xieding administration significantly attenuated bleomycin-induced pulmonary fibrosis in mice by reducing lung coefficient, wet/dry, NO, HYP, and MDA as well as the expression of iNOS, CTGF, α-SMA, FN, and DNA damage. The results indicated that Qingfei Xieding is effective to resist oxidative damage and histopathologic lesion, serving a protection role on bleomycin-induced pulmonary fibrosis.