Revealing two radio active galactic nuclei extremely near PSR J0437−-4715

Newton's gravitational constant $G$ may vary with time at an extremely low level. The time variability of $G$ will affect the orbital motion of a millisecond pulsar in a binary system and cause a tiny difference between the orbital period-dependent measurement of the kinematic distance and the direct measurement of the annual parallax distance. PSR J0437$-$4715 is the nearest millisecond pulsar and the brightest at radio. To explore the feasibility of achieving a parallax distance accuracy of one light-year, comparable to the recent timing result, with the technique of differential astrometry, we searched for compact radio sources quite close to PSR J0437$-$4715. Using existing data from the Very Large Array and the Australia Telescope Compact Array, we detected two sources with flat spectra, relatively stable flux densities of 0.9 and 1.0 mJy at 8.4 GHz and separations of 13 and 45 arcsec. With a network consisting of the Long Baseline Array and the Kunming 40-m radio telescope, we found that both sources have a point-like structure and a brightness temperature of $\geq$10$^7$ K. According to these radio inputs and the absence of counterparts in the other bands, we argue that they are most likely the compact radio cores of extragalactic active galactic nuclei rather than Galactic radio stars. The finding of these two radio active galactic nuclei will enable us to achieve a sub-pc distance accuracy with the in-beam phase-referencing very-long-baseline interferometric observations and provide one of the most stringent constraints on the time variability of $G$ in the near future.Comment: 9 pages, 3 tables, 3 figures. Accepted for publication in MNRA

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%

Fully Photonic Integrated Wearable Optical Interrogator

Wearable technology constitutes a pioneering and leading innovation and a market development platform worldwide for technologies worn close to the body. Wearable optical fiber sensors have the most value for advanced multiparameter sensing in digital health monitoring systems. We demonstrated the first example of a fully integrated optical interrogator. By integrating all the optical components on a silicon photonic chip, we realized a stable, miniaturized and low-cost optical interrogator for the continuous, dynamic, and long-term acquisition of human physiological signals. The interrogator was integrated in a wristband, enabling the detection of body temperature and heart sounds. Our study paves the way for the development of watch-sized integrated wearable optical interrogators with potential applications in health monitoring and can be directly exploited for the customized design of ultraminiaturized optical interrogator systems.H.L. acknowledges the support from the Tianjin Talent Special Support Program. J.D.P.G. acknowledges the support from the Serra Hunter Program, the ICREA Academia Program, and the Tianjin Distinguished University Professor Program. This work was supported by the National Natural Science Foundation of China (no. 61675154), the Tianjin Key Research and Development Program (no. 19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (no. 18ZXJMTG00260), the Tianjin Science and Technology Program (no. 20YDTPJC01380), and the Tianjin Municipal Special Foundation for Key Cultivation of China (no. XB202007)

GRIK3 rs490647 is a Common Genetic Variant between Personality and Subjective Well-being in Chinese Han Population

Personality and subjective well-being (SWB) have been suggested to be strongly related in previous studies. This study was intended to confirm the relationship between personality and SWB and tried to seek out the genetic variants which underlie both personality and SWB. The subjects were 890 participants from Chinese Han population. We evaluated their personality using the Big Five Inventory (BFI) and used the Satisfaction With Life Scale (SWLS) to reflect their SWB. Five single nucleotide polymorphisms (SNPs) were selected from the literature (rs1426371, rs2164273, rs322931, rs3756290, rs490647) and genotyped for genetic association study. We found negative correlations between neuroticism and SWB. On the contrary, extraversion and agreeableness were positively associated with SWB. Three SNPs (rs2164273, rs3756290, rs490647) out of the five were found to connect with personality (extraversion, neuroticism, conscientiousness and openness to experience) and rs490647 variants of GRIK3 was also associated with SWB. Individuals carrying G allele at this site were predisposed to have lower risk to be neuroticism and greater chance to be extraverted, open and satisfied with their life. In summary, our study revealed that rs490647 might be a good candidate genetic variant for personality and SWB in Chinese Han population

SOI waveguide bragg grating photonic sensor for human body temperature measurement based on photonic integrated interrogator

A waveguide Bragg grating (WBG) provides a flexible way for measurement, and it could even be used to measure body temperature like e-skin. We designed and compared three structures of WBG with the grating period, etching depth, and duty cycle. The two-sided WBG was fabricated. An experimental platform based on photonic integrated interrogator was set up and the experiment on the two-sided WBG was performed. Results show that the two-sided WBG can be used to measure temperature changes over the range of 35–42◦C, with a temperature measurement error of 0.1◦C. This approach has the potential to facilitate application of such a silicon-on-insulator (SOI) WBG photonic sensor to wearable technology and realize the measurement of human temperature

Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization

Electrocardiogram (ECG) signals contain a significant amount of subtle information that can be used to detect some types of heart dysfunction. The widespread availability of digital ECG and the algorithmic paradigm of the long short-term memory (LSTM) network present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, the number of hidden units and initial learning rate of an LSTM neural network for ECG classification are currently preset based on prior knowledge, which causes the model to reach a sub-optimal state. In this study, an automated ECG detection and classification method using a bidirectional LSTM (BiLSTM) network modified by Bayesian optimization is developed. Bayesian optimization is used to optimize the two hyperparameters of the BiLSTM network: the initial learning rate and the number of hidden layers. By classifying five ECG signals in the MIT-BIH arrhythmia database, the accuracy of the modified network reaches 99.00%, which is 0.86% higher than that before optimization. The results demonstrate that Bayesian optimization can be an effective approach to improving the quality of classifiers based on deep learning. The presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies, which may have practical applications.Hongqiang Li acknowledges support from the Tianjin Talent Special Support Program. J. D. Prades Garcia acknowledges support from the Serra Hunter Program, the ICREA Academia Program and the Tianjin Distinguished University Professor Program. This work was supported by the National Natural Science Foundation of China (No. 61675154), the Tianjin Key Research and Development Program (19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (18ZXJMTG00260), the Tianjin Science and Technology Program (20YDTPJC01380) and the Tianjin Municipal Special Foundation for Key Cultivation of China (XB202007)

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%. Graphical abstract: [Figure not available: see fulltext.].This work was supported by the Tianjin Key Research and Development Program (No. 19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (No. 18ZXJMTG00260), the Tianjin Science and Technology Program (No. 20YDTPJC01380), and the Tianjin Municipal Special Foundation for Key Cultivation of China (No. XB202007)