A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky–Golay Filter for ECG Denoising

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics

Active Fragment of Veronica ciliata

Excessive amounts of reactive oxygen species (ROS) in the body are a key factor in the development of hepatopathies such as hepatitis. The aim of this study was to assess the antioxidation effect in vitro and hepatoprotective activity of the active fragment of Veronica ciliata Fisch. (VCAF). Antioxidant assays (DPPH, superoxide, and hydroxyl radicals scavenging) were conducted, and hepatoprotective effects through the application of tert-butyl hydroperoxide- (t-BHP-) induced oxidative stress injury in HepG2 cells were evaluated. VCAF had high phenolic and flavonoid contents and strong antioxidant activity. From the perspective of hepatoprotection, VCAF exhibited a significant protective effect on t-BHP-induced HepG2 cell injury, as indicated by reductions in cytotoxicity and the levels of ROS, 8-hydroxydeoxyguanosine (8-OHdG), and protein carbonyls. Further study demonstrated that VCAF attenuated the apoptosis of t-BHP-treated HepG2 cells by suppressing the activation of caspase-3 and caspase-8. Moreover, it significantly decreased the levels of ALT and AST, increased the activities of acetyl cholinesterase (AChE), glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT), and increased total antioxidative capability (T-AOC). Collectively, we concluded that VCAF may be a considerable candidate for protecting against liver injury owing to its excellent antioxidant and antiapoptosis properties

Reduction in the exposure risk of farmer from e-waste recycling site following environmental policy adjustment: a regional scale view of PAHs in paddy fields

Farmland contamination by polycyclic aromatic hydrocarbons (PAHs) has drawn increasing attention across China with enhanced regulations and environmental policies proposed by government to protect soil environment safety. As the informal electronic waste (e-waste) dismantling activities were forbidden under recent environmental regulation, this study compared levels, compositions, spatial distributions, human health risks of PAHs in paddy soil within the vicinity of an e-waste recycling area in southeastern China, with 129 and 150 soil samples collected in 2011 and 2016, respectively. The soil contamination was dominated with high molecular weight PAHs. The mean concentration of EPA 16 PAHs decreased from 590.4 ± 337.2 μg kg in 2011 to 407.3 ± 232.2 μg kg in 2016. Distribution maps of soil PAHs concentration displayed the temporal change in spatial. Principal component analysis together with diagnostic ratios revealed the combustion of biomass and coal in industrial and unregulated e-waste dismantling were the main sources of PAHs in the study area. Both deterministic and probabilistic assessments demonstrated reduced exposure risk for farmers from 2011 to 2016. Sensitivity analysis revealed that exposure frequency (EF) is the most influential parameter for the total variance in the risk assessment model. This study implied that the more stringent environmental policy and regulation can lead reductions in soil contamination with PAHs. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

IκBα polymorphism at promoter region (rs2233408) influences the susceptibility of gastric cancer in Chinese

<p>Abstract</p> <p>Background</p> <p>Nuclear factor of kappa B inhibitor alpha (IκBα) protein is implicated in regulating a variety of cellular process from inflammation to tumorigenesis. The objective of this study was to investigate the susceptibility of rs2233408 T/C genotype in the promoter region of <it>IκBα </it>to gastric cancer and the association of this polymorphism with clinicopathologic variables in gastric cancer patients.</p> <p>Methods</p> <p>A population-based case-control study was conducted between 1999 and 2006 in Guangdong Province, China. A total of 564 gastric cancer patients and 566 healthy controls were enrolled in this study. rs2233408 genotypes in <it>IκBα </it>were analyzed by TaqMan SNP genotyping assay.</p> <p>Results</p> <p>Both rs2233408 T homozygote (TT) and T heterozygotes (TC and TT) had significantly reduced gastric cancer risk (TT: OR = 0.250, 95% CI = 0.069-0.909, <it>P </it>= 0.035; TC and TT: OR = 0.721, 95% CI = 0.530-0.981, <it>P </it>= 0.037), compared with rs2233408 C homozygote (CC). rs2233408 T heterozygotes were significantly associated with reduced risk of intestinal-type gastric cancer with ORs of 0.648 (95% CI = 0.459-0.916, <it>P </it>= 0.014), but not with the diffuse or mix type of gastric cancer. The association between rs2233408 T heterozygotes and gastric cancer appeared more apparent in the older patients (age>40) (OR = 0.674, 95% CI = 0.484-0.939, <it>P </it>= 0.02). rs2233408 T heterozygotes was associated with non-cardiac gastric cancer (OR = 0.594, 95% CI = 0.411-0.859, <it>P </it>= 0.006), but not with cardiac gastric cancer. However, rs2233408 polymorphism was not associated with the prognosis of gastric cancer patients.</p> <p>Conclusions</p> <p><it>IκBα </it>rs2233408 T heterozygotes were associated with reduced risk of gastric cancer, especially for the development of certain subtypes of gastric cancer in Chinese population.</p

Allstory: An Assistive Application for History

When learning history, students have difficulty remembering large amounts of history and don’t know explanations of some proper nouns, which makes them not have an in-depth understanding of history and easy to fail in the test. As an important subject, an auxiliary app is needed to help students digest the knowledge they have learned. It will use different function to break the barrier, allowing students easier to learn and love history

Effect of pressure on the feeding characteristics of ZCuZn16Si4 alloy

The experimental and numerical simulation methods were employed to study the effect of pressure on the feeding characteristics of ZCuZn16Si4 alloy castings. The results proved that different pressures would lead to different feeding distance of riser over a suitable pressure range, and the pressure can be used to greatly improve the feeding characteristics compared with gravity casting. It should be pointed out that current porosity criteria in the numerical simulation codes cannot yet be applied well enough to predict the porosity defects of low-pressure copper alloy castings

Energy-efficient ECG signal compression for user data input in cyber-physical systems by leveraging empirical mode decomposition

Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate

Research on Influence of Different Simulation Methods of Bypass Flow in Thermal Hydraulic Analysis on Temperature Distribution in HTR-10

In pebble-bed high temperature gas-cooled reactor, gaps widely exist between graphite blocks and carbon bricks in the reactor core vessel. The bypass helium flowing through the gaps affects the flow distribution of the core and weakens the effective cooling of the core by helium, which in turn affects the temperature distribution and the safety features of the reactor. In this paper, the thermal hydraulic analysis models of HTR-10 with bypass flow channels simulated at different positions are designed based on the flow distribution scheme of the original core models and combined with the actual position of the core bypass flow. The results show that the bypass coolant flowing through the reflectors enhances the heat transfer of the nearby components efficiently. The temperature of the side reflectors and the carbon bricks is much lower with more side bypass coolant. The temperature distribution of the central region in the pebble bed is affected by the bypass flow positions slightly, while that of the peripheral area is affected significantly. The maximum temperature of the helium, the surface, and center of the fuel elements rises as the bypass flow ratio becomes larger, while the temperature difference between them almost keeps constant. When the flow ratio of each part keeps constant, the maximum temperature almost does not change with different bypass flow positions

Energy-efficient ECG compression in wearable body sensor network by leveraging empirical mode decomposition

Wearable body sensor network (BSN) is widely used in daily monitoring, well-being management, and rehabilitation. Energy efficiency imposes a stringent constraint in wearable BSN, in which wireless transmission is significantly power-demanding. Compressed sensing (CS) provides a good solution to reduce power consumption for data transmission due to the sparsity of signals which can use limited transmitted data to reconstruct original signals. In this study, we develop a new method for non-sparse ECG signal compression by leveraging empirical mode decomposition (EMD) and online dictionary for wearable devices. Comparing to the state-of-the-art of ECG compression which can achieve the compression ratio (CR) of around 25 with the root mean square error (RMSE) around 5%, our method can achieve the CR up to 60 with the same level of RMSE for wearable ECG. In addition, our method also has low computational complexity, which can achieve lower compression energy. The validation experiments are conducted on both clinical data and wearable ECG detected by our BSN in noisy environment. The proposed method shows high feasibility for real CS on board to achieve ultra-low power consumption