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This study aimed to investigate the serum plasmacytoma variant translocation 1 (PVT1) level in pregnant women with gestational hypertension and pre-eclampsia and its diagnostic value for diseases and its influence on pregnancy outcome. Serum PVT1 levels in 72 pregnant women with gestational hypertension, 72 pregnant women with pre-eclampsia and 71 healthy pregnant women were evaluated by RT-qPCR, and the diagnostic significance of PVT1 for gestational hypertension was verified by receiver operator characteristic (ROC) curve. The correlation between PVT1 and clinical indicators were evaluated by Pearson correlation coefficient method. Logistic regression analysis evaluated the influencing factors in the development process of gestational hypertension to pre-eclampsia. The effect of PVT1 level on pregnancy outcome was evaluated by prognostic analysis. Results showed that PVT1 level was down-regulated in gestational hypertension group compared with healthy control group, whereas PVT1 level was downregulated more significantly in pre-eclampsia group than in gestational hypertension group. ROC curve showed that PVT1 had high diagnostic accuracy for gestational hypertension. Pearson correlation coefficient and multiple linear regression analysis showed that PVT1 was correlated with systolic blood pressure, diastolic blood pressure, interleukin (IL)-6 and tumor necrosis factor-α. Logistic regression analysis revealed that IL-6 and PVT1 were the influencing factors of gestational hypertension to pre-eclampsia transition. Moreover, prognostic analysis manifested that the incidence of fetal growth restriction in low PVT1 expression group was significantly higher than that in high PVT1 expression group. The expression level of PVT1 has a high diagnostic accuracy for gestational hypertension, and the low PVT1 expression group is more prone to fetal growth restriction. [ABSTRACT FROM AUTHOR]

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m. [ABSTRACT FROM AUTHOR]

Objective: To determine the degree of knowledge in the usage of pulse oximeter as a home assessment tool among the community in Malaysia. Methods: A cross-sectional survey was conducted in November 2021. The questionnaire assessed the knowledge in using pulse oximeters, user experience and opinions in using pulse oximeter as a home assessment tool during the pandemic. Results: A total of 504 respondents were included in the study, and the mean score in knowledge related to application of pulse oximetry was 73.00%, while the mean score in knowledge related to factors affecting pulse oximetry readings was only 38.51%. A total of 90.5% of the respondents recognised normal pulse rate and 88.5% knew the blood oxygen saturation levels of a healthy adult, while 69.4% recognised the definition of silent hypoxia. In addition, the majority of the respondents agreed that factors such as poor blood circulation (71.2%), excessive movements (69.8%), and hand position (60.7%) affected oximetry readings. However, 61.7%, 81.7%, 77.2% and 76.8% of the respondents could not identify nail polish, skin colour, skin thickness and tattoos as factors that may affect oximetry readings respectively. Conclusions: The respondents showed a satisfactory level of knowledge related to application of pulse oximetry, but a poor level of knowledge related to factors affecting pulse oximetry readings among the community in Malaysia. Continuous efforts in educating the community on the correct use of pulse oximeters are crucial for appropriate home assessment and avoiding unnecessary stress. [ABSTRACT FROM AUTHOR]

Correct facade image parsing is essential to the semantic understanding of outdoor scenes. Unfortunately, there are often various occlusions in front of buildings, which fails many existing methods. In this paper, we propose an end-to-end deep network for facade parsing with occlusions. The network learns to decompose an input image into visible and invisible parts by occlusion reasoning. Then, a context aggregation module is proposed to collect nonlocal cues for semantic segmentation of the visible part. In addition, considering the regularity of man-made buildings, a repetitive pattern completion branch is designed to infer the contents in the invisible regions by referring to the visible part. Finally, the parsing map of the input facade image is generated by fusing the results of the visible and invisible results. Experiments on both synthetic and real datasets demonstrate that the proposed method outperforms state-of-the-art methods in parsing facades with occlusions. Moreover, we applied our method in applications of image inpainting and 3D semantic modeling. [ABSTRACT FROM AUTHOR]

Based on economic and social development and ecological environment data, a comprehensive index calculation system based on the pressurestate-response model of Shijiazhuang's ecotourism environmental carrying capacity was constructed, and the analytic hierarchy process was used to determine the weights of various indicators, and finally calculated The three sub-systems of pressure, state and response and the environmental carrying capacity index of Shijiazhuang City's ecotourism. The results show that the comprehensive index of Eco-tourism Environmental Carrying Capacity (ETECC) of Shijiazhuang City from 2011 to 2017 shows an upward trend; the evaluation index of tourism environmental pressure subsystem continues to rise, which plays a key role in improving the carrying capacity of Shijiazhuang's ecological tourism environment; Seven indicators such as income efficiency, number of scenic spots, and total water resources are the key factors restricting the carrying capacity of the ecotourism environment in Shijiazhuang. [ABSTRACT FROM AUTHOR]

The glassy carbon (GC) electrode surface modified with carboxylic multi-walled carbon nanotubes (c-MWCNTs) and poly (N-isopropylacrylamide) (PNIPAM) films were used to investigate multiple switching electrochemical behaviors of rutin, and a 4-input/3-output logic-gate system was constructed. The drug molecule rutin acted as electroactive probe, and displayed triple stimuli-responsive switchable cyclic voltammetry (CV) properties on c-MWCNTs/PNIPAM film electrodes. From pH 4.0 to pH 9.0 at 25°C, the peak currents of rutin were decreasing with the pH value increasing due to the property of rutin itself and the electrostatic interaction between polyacrylamide (PAA) in the film and rutin molecule. When external temperature and Na2SO4 concentration increased, the CV response signals of rutin decreased because the hydrogen bonds between amide group of PNIPAM hydrogel polymer and water molecules were broken gradually, and a collapsed globule structure was formed. According to above results, multiple stimulus-responsive hydrogel thin film sensor was constructed. Then a 4-input/3-output logic-gate system including AND, NAND and INHIBIT gates was fabricated successfully. The construction of logic-gate system based on drug molecule rutin may provide a novel approach and model leading to design the new-style drug sensor, which might be applied to the intelligent medical diagnostics and drug release. [ABSTRACT FROM AUTHOR]

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance. [ABSTRACT FROM AUTHOR]

Background: We report the 12-lead ECG morphology of left posterior fascicular ventricular tachycardia (LPF-VT) and the relationship between His-ventricular (HV) interval and site of origin in LPF-VT.Methods and Results: We studied 41 patients who underwent successful catheter ablation of LPF-VT with HV interval >0 ms (n=8; proximal-LPF group), HV interval 0 to -15 ms (n=15; middle-LPF group), and HV interval <-15 ms (n=18; distal-LPF group). The earliest mapped presystolic potential (PP)-QRS interval was 34.1±4.2, 24.5±3.2, and 19.4±2.8 ms in proximal-, middle-, and distal-LPF groups. The earliest PP ratio (PP-QRS interval during VT/HV interval during sinus rhythm) was 0.59±0.05, 0.45±0.07, and 0.31±0.05 in the proximal-, middle-, and distal-LPF groups. There were statistically significant differences between the 3 groups in earliest PP ratio, and there was close correlation between the HV interval during LPF-VT and earliest PP ratio. The QRS duration in the proximal-LPF group (114±6 ms) was significantly narrower compared with the middle-LPF group (128±5 ms) and distal-LPF group (140±6 ms). In leads I and V6, the ratio of R/S tended to be greater in the proximal-LPF group compared with the other 2 groups. QRS duration, the ratio of R/S in leads V6, and lead I could predict a proximal or distal origin site of LPF-VT with high sensitivity and specificity.Conclusion: The HV interval and 12-lead ECG morphology of LPF-VT may help predict the successful site of origin and prove useful in guiding an effective ablation strategy. [ABSTRACT FROM AUTHOR]

A power-saving mechanism for smartphone devices is developed by analyzing the features of data that are received from Internet of Things (IoT) sensors devices to optimize the data processing policies. In the proposed GreenIoT architecture for power-saving in IoT, the power saving and feedback mechanism are implemented in the IoT middleware. When the GreenIoT application in the power-saving IoT architecture is launched, IoT devices collect the sensor data and send them to the middleware. After the scanning module in the IoT middleware has received the data, the data are analyzed by a feature evaluation module and a threshold analysis module. Based on the analytical results, the policy decision module processes the data in the device or in the cloud computing environment. The feedback mechanism then records the power consumed and, based on the history of these records, dynamically adjusts the threshold value to increase accuracy. Two smart living applications, a biomedical application and a smart building application, are proposed. Comparisons of data processed in the cloud computing environment show that the power-saving mechanism with IoT architecture reduces the power consumed by these applications by 24% and 9.2%. [ABSTRACT FROM AUTHOR]