4,740 research outputs found

    Boosted output performance of triboelectric nanogenerator via electric double layer effect

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    For existing triboelectric nanogenerators ( TENGs), it is important to explore unique methods to further enhance the output power under realistic environments to speed up their commercialization. We report here a practical TENG composed of three layers, in which the key layer, an electric double layer, is inserted between a top layer, made of Al/polydimethylsiloxane, and a bottom layer, made of Al. The efficient charge separation in the middle layer, based on Volta's electrophorus, results from sequential contact configuration of the TENG and direct electrical connection of the middle layer to the earth. A sustainable and enhanced output performance of 1.22mA and 46.8 mW cm(-2) under low frequency of 3 Hz is produced, giving over 16-fold enhancement in output power and corresponding to energy conversion efficiency of 22.4%. Finally, a portable power-supplying system, which provides enough d.c. power for charging a smart watch or phone battery, is also successfully developed.ope

    Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

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    OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

    Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry.

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    AimsIn patients without obstructive coronary artery disease (CAD), we examined the prognostic value of risk factors and atherosclerotic extent.Methods and resultsPatients from the long-term CONFIRM registry without prior CAD and without obstructive (≥50%) stenosis were included. Within the groups of normal coronary computed tomography angiography (CCTA) (N = 1849) and non-obstructive CAD (N = 1698), the prognostic value of traditional clinical risk factors and atherosclerotic extent (segment involvement score, SIS) was assessed with Cox models. Major adverse cardiac events (MACE) were defined as all-cause mortality, non-fatal myocardial infarction, or late revascularization. In total, 3547 patients were included (age 57.9 ± 12.1 years, 57.8% male), experiencing 460 MACE during 5.4 years of follow-up. Age, body mass index, hypertension, and diabetes were the clinical variables associated with increased MACE risk, but the magnitude of risk was higher for CCTA defined atherosclerotic extent; adjusted hazard ratio (HR) for SIS >5 was 3.4 (95% confidence interval [CI] 2.3-4.9) while HR for diabetes and hypertension were 1.7 (95% CI 1.3-2.2) and 1.4 (95% CI 1.1-1.7), respectively. Exclusion of revascularization as endpoint did not modify the results. In normal CCTA, presence of ≥1 traditional risk factors did not worsen prognosis (log-rank P = 0.248), while it did in non-obstructive CAD (log-rank P = 0.025). Adjusted for SIS, hypertension and diabetes predicted MACE risk in non-obstructive CAD, while diabetes did not increase risk in absence of CAD (P-interaction = 0.004).ConclusionAmong patients without obstructive CAD, the extent of CAD provides more prognostic information for MACE than traditional cardiovascular risk factors. An interaction was observed between risk factors and CAD burden, suggesting synergistic effects of both

    Novel Approaches for the Use of Cardiac/Coronary Computed Tomography Angiography

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    Recent developments in the novel imaging technology of cardiac computed tomography (CT) not only permit detailed assessment of cardiac anatomy but also provide insight into cardiovascular physiology. Foremost, coronary CT angiography (CCTA) enables direct noninvasive examination of both coronary artery stenoses and atherosclerotic plaque characteristics. Calculation of computational fluid dynamics by cardiac CT allows the noninvasive estimation of fractional flow reserve, which increases the diagnostic accuracy for detection of hemodynamically significant coronary artery disease. In addition, a combination of myocardial CT perfusion and CCTA can provide simultaneous anatomical and functional assessment of coronary artery disease. Finally, detailed anatomical evaluation of atrial, ventricular, and valvular anatomy provides diagnostic information and guidance for procedural planning, such as for transcatheter aortic valve replacement. The clinical applications of cardiac CT will be extended with the development of these novel modalities

    Cyclin E and CDK2 Repress the Terminal Differentiation of Quiescent Cells after Asymmetric Division in C. elegans

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    Coordination between cell proliferation and differentiation is important in normal development and oncogenesis. These processes usually have an antagonistic relationship, in that differentiation is blocked in proliferative cells, and terminally differentiated cells do not divide. In some instances, cyclins, cyclin-dependent kinases (CDKs) and their inhibitors (CKIs) play important roles in this antagonistic regulation. However, it is unknown whether CKIs and cyclin/CDKs regulate the uncommitted state in quiescent cells where CDK activities are likely to be low. Here, we show in C. elegans that cye-1/cyclin E and cdk-2/CDK2 repress terminal differentiation in quiescent cells. In cye-1 mutants and cdk-2(RNAi) animals, after asymmetric division, certain quiescent cells adopted their sister cells' phenotype and differentiated at some frequency. In contrast, in cki-1(RNAi) animals, these cells underwent extra divisions, while, in cki-1(RNAi); cdk-2(RNAi) or cki-1(RNAi); cye-1 animals, they remained quiescent or differentiated. Therefore, in wild-type animals, CKI-1/CKI in these cells maintained quiescence by inhibiting CYE-1/CDK-2, while sufficient CYE-1/CDK-2 remained to repress the terminal differentiation. The difference between sister cells is regulated by the Wnt/MAP kinase pathway, which causes asymmetric expression of CYE-1 and CKI-1. Our results suggest that the balance between the levels of CKI and cyclin E determines three distinct cell states: terminally differentiated, quiescent and uncommitted, and proliferating
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