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Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures. [ABSTRACT FROM AUTHOR]

Lung cancer is a malignant tumor with highly heterogeneous characteristics. A classic Chinese medicine, Pinellia ternata (PT), was shown to exert therapeutic effects on lung cancer cells. However, its chemical and pharmacological profiles are not yet understood. In the present study, we aimed to reveal the mechanism of PT in treating lung cancer cells through metabolomics and network pharmacology. Metabolomic analysis of two strains of lung cancer cells treated with Pinellia ternata extracts (PTE) was used to identify differentially abundant metabolites, and the metabolic pathways associated with the DEGs were identified by MetaboAnalyst. Then, network pharmacology was applied to identify potential targets against PTE-induced lung cancer cells. The integrated network of metabolomics and network pharmacology was constructed based on Cytoscape. PTE obviously inhibited the proliferation, migration and invasion of A549 and NCI-H460 cells. The results of the cellular metabolomics analysis showed that 30 metabolites were differentially expressed in the lung cancer cells of the experimental and control groups. Through pathway enrichment analysis, 5 metabolites were found to be involved in purine metabolism, riboflavin metabolism and the pentose phosphate pathway, including D-ribose 5-phosphate, xanthosine, 5-amino-4-imidazolecarboxyamide, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Combined with network pharmacology, 11 bioactive compounds were found in PT, and networks of bioactive compound–target gene–metabolic enzyme–metabolite interactions were constructed. In conclusion, this study revealed the complicated mechanisms of PT against lung cancer. Our work provides a novel paradigm for identifying the potential mechanisms underlying the pharmacological effects of natural compounds. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine. [ABSTRACT FROM AUTHOR]

The source parameters of earthquakes (stress drop, corner frequency, seismic moment, source size, radiant energy, etc.) provide important information about the source features, the state of stress, and the mechanism of earthquake rupture dynamics. Using the digital observation data obtained from a high-density seismic monitoring network deployed in the Baihetan reservoir area of the lower Jinsha River, we obtained Brune source parameters of the 459 earthquakes ranging in magnitude ML 1.50~4.70. The results revealed seismic moments M0 within the range of 2.03 × 1012~1.45 × 1016 N·m, corner frequencies f c between 2.00 and 10.00 Hz, and source dimensions varying from 130.00 to 480.00 m, with stress drops spanning from 0.12 to 61.24 MPa. It is noteworthy that the majority of the earthquakes had stress drops less than 10.00 MPa, with as much as 73.30% of these events having stress drops within the range of 0.10 to 2.00 MPa. We found that stress drop, corner frequency, and source size in the study area exhibited positive correlations with earthquake magnitude. Earthquakes occurring at shallower depths for the same magnitude tended to have smaller stress drops and corner frequencies, but larger rupture scales. During the first 2 years of impoundment with significant water level fluctuation, earthquakes beneath or near the reservoir released higher stress drops relative to pre-reservoir conditions, with average stress drops significantly elevated from 5.52 to 13.562 Mpa for events above ML3 since the impoundment. The radiated energy released by earthquakes with magnitudes below ML3.0 are significantly more than before impoundment, indicating that earthquakes of similar magnitudes in the reservoir area may produce greater intensity and perceptibility following the impoundment. According to our result, the triggered seismicity will continue to be active under annual regulation changes in the water level of the Baihetan Dam at high elevations in future years. [ABSTRACT FROM AUTHOR]

Background: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. Purpose: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. Methods: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Results: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%. Conclusions: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice‐doc.netlify.app) for further studies. [ABSTRACT FROM AUTHOR]

Accurate segmentation of cardiac anatomy is a prerequisite for the diagnosis of cardiovascular disease. However, due to differences in imaging modalities and imaging devices, known as domain shift, the segmentation performance of deep learning models lacks reliability. In this paper, we propose a two-stage progressive unsupervised domain adaptation network (TSP-UDANet) to reduce domain shift when segmenting cardiac images from various sources. We alleviate the domain shift between the feature distribution of the source and target domains by introducing an intermediate domain as a bridge. The TSP-UDANet consists of three sub-networks: a style transfer sub-network, a segmentation sub-network, and a self-training sub-network. We conduct cooperative alignment of different domains at image level, feature level, and output level. Specifically, we transform the appearance of images across domains and enhance domain invariance by adversarial learning in multiple aspects to achieve unsupervised segmentation of the target modality. We validate the TSP-UDANet on the MMWHS (unpaired MRI and CT images), MS-CMRSeg (cross-modality MRI images), and M&Ms (cross-vendor MRI images) datasets. The experimental results demonstrate excellent segmentation performance and generalizability for unlabeled target modality images. [ABSTRACT FROM AUTHOR]

Pedestrian detection is an important task in many computer vision applications. Since multispectral pedestrian detection can alleviate the difficulties of insufficient illumination at night, it has been rapidly developed in recent years. However, the way for effective color-thermal image fusion still needs further research. In this paper, we propose a Feature Aggregation Module (FAM) that can adaptively capture the cross-channel and cross-dimension information interaction of the two modalities. In addition, we develop a Feature Aggregation Network (FANet) that embeds the proposed FAM module into a two-stream network adapted from the YOLOv5. FANet has the advantages that its size is small (15 MB) and it runs fast (8 ms per frame). Extensive experiments on the KAIST dataset show that the proposed method is effective for multispectral pedestrian detection, especially in the night-time condition, for which the Miss Rate is only 8.91%. Moreover, we show that the saliency map computed from the thermal image can be incorporated into FANet to further improve the detection accuracy. In order to verify the generalization ability of the FAM module, we have also conducted experiments on the person re-identification datasets, namely Market1501 and Duke. The performance of our FAM compares favorably against existing feature fusion mechanisms on the two datasets. [ABSTRACT FROM AUTHOR]

Background: Pulmonary embolism is a kind of cardiovascular disease that threatens human life and health. Since pulmonary embolism exists in the pulmonary artery, improving the segmentation accuracy of pulmonary artery is the key to the diagnosis of pulmonary embolism. Traditional medical image segmentation methods have limited effectiveness in pulmonary artery segmentation. In recent years, deep learning methods have been gradually adopted to solve complex problems in the field of medical image segmentation. Purpose: Due to the irregular shape of the pulmonary artery and the adjacent‐complex tissues, the accuracy of the existing pulmonary artery segmentation methods based on deep learning needs to be improved. Therefore, the purpose of this paper is to develop a segmentation network, which can obtain higher segmentation accuracy and further improve the diagnosis effect. Methods: In this study, the pulmonary artery segmentation performance from the network model and loss function is improved, proposing a pulmonary artery segmentation network (PA‐Net) to segment the pulmonary artery region from 2D CT images. Reverse Attention and edge attention are used to enhance the expression ability of the boundary. In addition, to better use feature information, the channel attention module is introduced in the decoder to highlight the important channel features and suppress the unimportant channels. Due to blurred boundaries, pixels near the boundaries of the pulmonary artery may be difficult to segment. Therefore, a new contour loss function based on the active contour model is proposed in this study to segment the target region by assigning dynamic weights to false positive and false negative regions and accurately predict the boundary structure. Results: The experimental results show that the segmentation accuracy of this proposed method is significantly improved in comparison with state‐of‐the‐art segmentation methods, and the Dice coefficient is 0.938 ± 0.035, which is also confirmed from the 3D reconstruction results. Conclusions: Our proposed method can accurately segment pulmonary artery structure. This new development will provide the possibility for further rapid diagnosis of pulmonary artery diseases such as pulmonary embolism. Code is available at https://github.com/Yuanyan19/PA‐Net. [ABSTRACT FROM AUTHOR]

Purpose: Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent and lightweight coronary artery segmentation algorithm that can be deployed in hospital systems to assist clinicians in quantitatively analyzing CHD. Methods: With the multi-level feature fusion, we proposed Dual-Attention Coordination U-Net (DAC-UNet) that achieves automated coronary artery segmentation in 2D CCTA images. The coronary artery occupies a small region, and the foreground and background are extremely unbalanced. For this reason, the more original information can be retained by fusing related features between adjacent layers, which is conducive to recovering the small coronary artery area. The dual-attention coordination mechanism can select valid information and filter redundant information. Moreover, the complementation and coordination of double attention factors can enhance the integrity of features of coronary arteries, reduce the interference of non-coronary arteries, and prevent over-learning. With gradual learning, the balanced character of double attention factors promotes the generalization ability of the model to enhance coronary artery localization and contour detail segmentation. Results: Compared with existing related segmentation methods, our method achieves a certain degree of improvement in 2D CCTA images for the segmentation accuracy of coronary arteries with a mean Dice index of 0.7920. Furthermore, the method can obtain relatively accurate results even in a small sample dataset and is easy to implement and deploy, which is promising. The code is available at: https://github.com/windfly666/Segmentation. Conclusion: Our method can capture the coronary artery structure end-to-end, which can be used as a fundamental means for automatic detection of coronary artery stenosis, blood flow reserve fraction analysis, and assisting clinicians in diagnosing CHD. [ABSTRACT FROM AUTHOR]

We used near-field and regional GNSS (Global Navigation Satellite System) data to quantify the deformation and locking ratio of the Xiaojiang fault (XJF) in southeastern Tibet. The inversion based on the dislocation model shows that the slip rate of the XJF is 9–11 mm/a; the locking depths of the northern, central, and southern segments are 25.5 km, 12 km, and 22.5 km, respectively. The inversion with DEFNODE program shows that the locking of the northern segment is the strongest above a depth of 20 km, while the locking between 20 km and 26 km is intermediate, and the weakest locking is found below 26 km. In the central segment, the depths of the interface are 6 km and 12 km. Additionally, a locked asperity that has the potential of generating an Mw 7.2 earthquake along the northern segment is delineated. The asperity and the shallow locking zone are basically consistent with the rupture area of the 1733 M 7.8 Dongchuan earthquake and the 1833 M 8 Songming earthquake, respectively. Both the activity of the historical strong earthquakes and the seismicity of the microearthquakes recorded over recent years seem to suggest that a potential earthquake is imminent. [ABSTRACT FROM AUTHOR]

Objective: To investigate the possibility of false-negative occurrence of non-specific benign pathological results on CT-guided transthoracic lung core-needle biopsy and identify risk factors for false-negative results. Methods: The clinical, imaging, and surgical data of 403 lung biopsy patients were retrospectively analyzed. Patients were divided into true-negative and false-negative (FN) groups according to the final diagnosis. Univariate analysis was used to compare the variables in two groups for statistical differences, and multivariate analysis was used to clarify the risk factors associated with FN results. Results: Of the 403 lesions, 332 were finally confirmed as benign and 71 to be malignant, with a FN rate of 17.6%. Older patient age (P = 0.01), burr sign (P = 0.00), and pleural traction sign (P = 0.02) were independent risk factors for FN results. The area under the receiver operating characteristic (ROC) curve's area under curve (AUC) was 0.73. Conclusion: CT-guided transthoracic lung core-needle biopsy has a high diagnostic accuracy and low rate of FN results. Older patient age, the burr sign, and the pleural traction sign are independent risk factors for FN results that should be monitored prior to surgery to reduce the risk of FN results. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content‐aware pre‐activated residual UNet (CAPA‐ResUNet), was proposed for segmenting COVID‐19 lesions from CT slices. In this network, the pre‐activated residual block was used for down‐sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID‐19 Lung CT Lesion Segmentation Challenge—2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA‐ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content‐aware residual UNet (CARes‐UNet). The code is available at https://github.com/malu108/LungInfectionSeg. [ABSTRACT FROM AUTHOR]

Understanding the stress patterns produced by microbubbles (MB) in blood vessels is important in enhancing the efficacy and safety of ultrasound-assisted therapy, diagnosis, and drug delivery. In this study, the wall stress produced by the non-spherical oscillation of MBs within the lumen of micro-vessels was numerically analyzed using a three-dimensional finite element method. We systematically simulated configurations containing an odd number of bubbles from three to nine, equally spaced along the long axis of the vessel, insonated at an acoustic pressure of 200 kPa. We observed that 3 MBs were sufficient to simulate the stress state of an infinite number of bubbles. As the bubble spacing increased, the interaction between them weakened to the point that they could be considered to act independently. In the relationship between stress and acoustic frequency, there were differences between the single and 3 MB cases. The stress induced by 3 MBs was greater than the single bubble case. When the bubbles were near the wall, the shear stress peak was largely independent of vessel radius, but the circumferential stress peak increased with the radius. This study offers further insight into our understanding of the magnitude and distribution of stresses produced by multiple ultrasonically excited MBs inside capillaries. [ABSTRACT FROM AUTHOR]

Despite their great advancement, current pedestrian detection methods focus on single static images, which fail to employ richer information available from the video sequences. Compared with still images, videos can offer temporal information of objects in the time dimension, thus providing the potential to obtain more robust detection performance. Here, a novel pedestrian detection method based on visible part detection and temporal calibration is proposed. Specifically, a part‐aware module to predict the visible body part of each pedestrian instance, which enables us to obtain precise motion information of partially occluded pedestrians in a video sequence, is first developed. Then, the temporal coherence for each pedestrian instance based on the predicted motion information is constructed. After that, an adaptive temporal calibration method is introduced to effectively calibrate the final detection result. This method on two video pedestrian detection benchmarks, that is, Caltech‐New and MOT17Det, is evaluated. Experimental results show that this method performs favourably against existing pedestrian detection approaches. [ABSTRACT FROM AUTHOR]

The analysis of vascular impedance by monitoring blood pressure and volume pulse waves is an effective addition to the techniques for the early diagnosis and treatment of cardiovascular disease, which should be made at the same site simultaneously for maximum accuracy measurements. However, the current challenge is that there is still a lack of devices that satisfy both sensing functions simultaneously. In this study, we intend to develop a combined resistive and photoelectric sensor that can monitor both types of pulse waves in real time at the same location. A graphene/PDMS rubber composite serves as resistance element for monitoring the pressure pulse waves. By varying the graphene contents of the composites, it is found that the optimal graphene weight fraction was 11% giving a sensitivity of 210.6 and a response time of 18 ms. The tensile test shows high stability after 10,000 cycles with a resistance retention of 91.74%. The photoelectric component of the sensor monitors the blood volume pulse waves using the well-established principle of reflection photoplethysmography. The combined resistance-photoelectric sensor has good stability, high sensitivity and is found to be suitable for monitoring the blood pressure and volume waves at the same time at several anatomical sites close to superficial arteries. We conclude that this bimodal sensor is able to provide the data required to estimate peripheral vascular impedance by analyzing the changes in pressure and blood volume of peripheral arteries, thus adding a novel measure for cardiovascular function to a new generation of wearable electronics. [ABSTRACT FROM AUTHOR]

Background: Many cardiovascular diseases are closely related to the composition of epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable reference for doctors to diagnose the disease. The distribution and composition of EAT often have significant individual differences, and the traditional segmentation methods are not effective. In recent years, deep learning method has been gradually introduced into EAT segmentation task. Purpose: The existing EAT segmentation methods based on deep learning have a large amount of computation and the segmentation accuracy needs to be improved. Therefore, the purpose of this paper is to develop a lightweight EAT segmentation network, which can obtain higher segmentation accuracy with less computation and further alleviate the problem of false‐positive segmentation. Methods: First, the obtained computed tomography was preprocessed. That is, the threshold range of EAT was determined to be −190, −30 HU according to prior knowledge, and the non‐adipose pixels were excluded by threshold segmentation to reduce the difficulty of training. Second, the image obtained after thresholding was input into the lightweight RDU‐Net network to perform the training, validating, and testing process. RDU‐Net uses a residual multi‐scale dilated convolution block in order to extract a wider range of information without changing the current resolution. At the same time, the form of residual connection is adopted to avoid the problem of gradient expansion or gradient explosion caused by too deep network, which also makes the learning easier. In order to optimize the training process, this paper proposes PNDiceLoss, which takes both positive and negative pixels as learning targets, fully considers the class imbalance problem, and appropriately highlights the status of positive pixels. Results: In this paper, 50 CCTA images were randomly selected from the hospital, and the commonly used Dice similarity coefficient (DSC), Jaccard similarity, accuracy (ACC), specificity (SP), precision (PC), and Pearson correlation coefficient are used as evaluation metrics. Bland–Altman analysis results show that the extracted EAT volume is consistent with the actual volume. Compared with the existing methods, the segmentation results show that the proposed method achieves better performance on these metrics, achieving the DSC of 0.9262. The number of false‐positive pixels has been reduced by more than half. Pearson correlation coefficient reached 0.992, and linear regression coefficient reached 0.977 when measuring the volume of EAT obtained. In order to verify the effectiveness of the proposed method, experiments are carried out in the cardiac fat database of VisualLab. On this database, the proposed method also achieved good results, and the DSC value reached 0.927 in the case of only 878 slices. Conclusions: A new method to segment and quantify EAT is proposed. Comprehensive experiments show that compared with some classical segmentation algorithms, the proposed method has the advantages of shorter time‐consuming, less memory required for operations, and higher segmentation accuracy. The code is available at https://github.com/lvanlee/EAT_Seg/tree/main/EAT_seg. [ABSTRACT FROM AUTHOR]

The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research. [ABSTRACT FROM AUTHOR]

Body temperature plays a critical role in rehabilitation, and numerous studies proved that the regulation of body temperature contributes to the sensorimotor recovery of patients with brain diseases such as stroke. The hypothalamus plays a key role in thermoregulation. Ultrasound deep brain stimulation (UDBS) can noninvasively modulate deep brain nuclei and have potential applications in the treatment of Parkinson’s disease, Alzheimer’s disease, and depression, among others. The purpose of this study was to investigate whether ultrasound stimulation of the hypothalamus could regulate body temperature in free-moving mice. Results showed that thermoregulation was related to ultrasonic parameters (pulse repetition frequency (PRF), duty cycle, total time, and acoustic pressure). UDBS of the preoptic area of the anterior hypothalamus at 500 Hz PRF could significantly reduce body temperature ($\text{T}\,\,= -0.25\,\,\pm \,\,0.073\,\,^{\circ }\text{C}$ at t = 5 min, $\text{T}\,\,= -0.51\,\,\pm \,\,0.19\,\,^{\circ }\text{C}$ at t = 10 min, $\text{T}\,\,= -0.84\,\,\pm \,\,0.27\,\,^{\circ }\text{C}$ at t = 15 min). Meanwhile, UDBS of the dorsomedial hypothalamus at 10 Hz PRF triggered a significant increase in body temperature ($\text{T}\,\,=0.5\,\,\pm \,\,0.077\,\,^{\circ }\text{C}$ at t = 5 min, $\text{T}\,\,=1.16\,\,\pm \,\,0.23\,\,^{\circ }\text{C}$ at t = 10 min). These results suggest that UDBS, as a noninvasive neuromodulation tool, may play a key role in the future clinical treatment of malignant hyperthermia and hypothermia. [ABSTRACT FROM AUTHOR]

Purpose: Coronary heart disease is a serious disease that endangers human health and life. In recent years, the incidence and mortality of coronary heart disease have increased rapidly. The quantification of the coronary artery is critical in diagnosing coronary heart disease. Methods: In this paper, we improve the coronary arteries segmentation performance from two aspects of network model and algorithm. We proposed a U-shaped network based on spatio-temporal feature fusion structure to segment coronary arteries from 2D slices of computed tomography angiography (CTA) heart images. The spatio-temporal feature combines features of multiple levels and different receptive fields separately to get more precise boundaries. It is easy to cause over-segmented for the small proportion of coronary arteries in CTA images. For this reason, a combo loss function was designed to deal with the notorious imbalance between inputs and outputs that plague learning models. Input imbalance refers to the class imbalance in the input training samples. The output imbalance refers to the imbalance between the false positive and false negative of the inference model. The two imbalances in training and inference were divided and conquered with our combo loss function. Specifically, a gradient harmonizing mechanism (GHM) loss was employed to balance the gradient contribution of the input samples and at the same time punish false positives/negatives using another sensitivity–precision loss term to learn better model parameters gradually. Results: Compared with some existing methods, our proposed method improves the segmentation accuracy significantly, achieving the mean Dice coefficient of 0.87. In addition, accurate results can be obtained with little data using our method. Code is available at: https://github.com/Ariel97-star/FFNet-CoronaryArtery-Segmentation. Conclusions: Our method can intelligently capture coronary artery structure and achieve accurate flow reserve fraction (FFR) analysis. Through a series of steps such as CPR curved reconstruction, the detection of coronary heart disease can be achieved without affecting the patient's body. In addition, our work can be used as an effective means to assist in the detection of stenosis. In the screening of coronary heart disease among high-risk cardiovascular factors, automatic detection of luminal stenosis can be performed based on the application of later algorithm transformation. [ABSTRACT FROM AUTHOR]

Arterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms−1) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness. [ABSTRACT FROM AUTHOR]

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin's Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar. [ABSTRACT FROM AUTHOR]

Cell fusion is an essential event in many biological processes and has gained increasing attention in the field of biotechnology. In this study, we demonstrate an effective and convenient strategy for cell capture, pairing, and fusion based on oscillating bubbles within an acoustofluidic device. Multirectangular structures of the same size were fabricated at the sidewall of polydimethylsiloxane to generate monodisperse microbubbles. These microbubbles oscillated with a similar amplitude under single-frequency acoustic excitation. Cells were simultaneously captured and paired on the surface of the oscillating bubbles within 40 ms, and the efficiency reached approximately 90%. Homotypic or heterotypic cell membrane fusion was achieved within 15 and 20 min, respectively. More importantly, the homotypic fused cells enabled migration and proliferation at 24 h, indicating that the important biological functions were not altered. [ABSTRACT FROM AUTHOR]

A moment magnitude (M w) 5.0 earthquake hit Qiaojia, Yunnan, China on 18 May 2020. Its hypocentre is only approximately 20 km away from the Baihetan reservoir, the second largest hydropower station in China. The Baihetan Reservoir is located at the junction of multiple fault zones on the eastern boundary of the Sichuan-Yunnan rhombic block, an area with high background seismic activity. The Baihetan Reservoir was planned to be impounded in April 2021 and the M w 5.0 earthquake occurred during its water-retaining. Thus, it is critical to investigate the seismogenesis of the Qiaojia M w 5.0 main shock and evaluate the risk of inducing earthquakes near the Baihetan Reservoir after impoundment. In this study, we built a complete and accurate earthquake catalogue to analyse seismicity in the reservoir area before and after the M w 5.0 Qiaojia earthquake. We adopted a machine learning-based seismic phase picker, PhaseNet, to automatically detect seismic picks from continuous raw seismic data. Seismic phase picks were associated and located using sequential earthquake association and location methods, including REAL, VELEST and hypoDD. We eventually obtained high-precision locations of 1640 earthquakes by the hypoDD. The distribution of earthquake locations indicates that a concealed fault nearly vertical to the surface accommodated the M w 5.0 Qiaojia main shock. The majority of its aftershocks is located within a narrow depth range of 8–13 km, indicating that the stresses in the hypocentral area were concentrated near the hypocentre of the M w 5.0 earthquake. Along with focal mechanism solutions, we suggested that the M w 5.0 Qiaojia earthquake is more likely a tectonic earthquake. However, we cannot exclude the possibility that earthquakes could be induced after the impoundment of Baihetan Reservoir, because the identified concealed fault is located in the middle of many large fault zones and only 20 km away from the Baihetan Reservoir. [ABSTRACT FROM AUTHOR]

Purpose: Myocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals. Methods: For the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads. Results: Ten types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively. Conclusion: When compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Background: NCCN guidelines recommend a dose of 100 μg/kg or a fixed dose of 6 mg pegylated recombinant human granulocyte colony‐stimulating factor (PEG rhG‐CSF) for chemotherapy‐induced neutropenia. However, a single dose of 60 μg/kg or 100 μg/kg produced a similar neutrophil response among patients with chemotherapy‐induced neutropenia (CIN). Thus, this prospective randomized study was designed to investigate the efficacy of 3 mg PEG rhG‐CSF in preventing acute lower respiratory tract infection (ALRTI) after chemotherapy. Methods: Patients with stage IIIB/IVA lung cancer who underwent chemotherapy were randomly divided into a (i) control group, and (ii) treatment group subject to 3 mg PEG rhG‐CSF after chemotherapy. Patients in the control group were administered rhG‐CSF (5 μg/kg) when decreased absolute neutrophil count (ANC) reached grade 3 of adverse events. The primary outcome was incidence of ALRTI, and the secondary outcomes included ANC, febrile neutropenia (FN), incidence of delayed chemotherapy, infection‐related medical expenses and adverse reactions. Results: Compared with the control group, there was a significant decrease in the incidence of ALRTI (9.6% vs. 24.6%, p < 0.01), FN (1.7% vs. 7.3%, p < 0.001) and neutropenia (8.3% vs. 23.3%, p < 0.01) in the PEG‐rhG‐CSF group. The incidence of ALRTI was significantly correlated with the grade of CTCAE on ANC. The main adverse reactions of PEG‐rhG‐CSF were pain and fatigue, among which three cases showed pain of ≥ grade 3. The cost of infection‐associated medical expenditure in the treatment group was greatly reduced compared with the control group (p < 0.001). Conclusions: ALRTI could well be prevented after prophylactic application of PEG‐rhG‐CSF (3 mg), and was related to the reduced neutropenia. [ABSTRACT FROM AUTHOR]

The Tanlu Fault Zone in eastern China stretches more than 2,400 km and is a significant, active, feature in the tectonics of East Asia. Here, we show the results of local earthquake travel‐time tomographical approach based on a comprehensive and systematic set of seismic data collected from 2008 to 2018 along the Central South Segment of the Tanlu Fault Zone. According to the model, the uppermost 0–10 km of the crust shows a close relationship between seismic velocity and the lithological properties of surface geological units. The middle crust at 10–20 km depths exhibits a strong connection between its high‐low velocity transitions and sites of augmented seismic activity. The top mantle at 34 km depths has lower velocities in the western region in comparison to the east, implying that the seismic Moho is deeper in the western area. These characteristics may reflect spatial variations in the destruction of the North China Craton. [ABSTRACT FROM AUTHOR]

Carotid-femoral pulse transit time (cfPTT) is a widely accepted measure of central arterial stiffness. The cfPTT is commonly calculated from two synchronized pressure waves. However, measurement of synchronized pressure waves is technically challenging. In this paper, a method of decomposing the radial pressure wave is proposed for estimating cfPTT. From the radial pressure wave alone, the pressure wave can be decomposed into forward and backward waves by fitting a double triangular flow wave. The first zero point of the second derivative of the radial pressure wave and the peak of the dicrotic segment of radial pressure wave are used as the peaks of the fitted double triangular flow wave. The correlation coefficient between the measured wave and the estimated forward and backward waves based on the decomposition of the radial pressure wave was 0.98 and 0.75, respectively. Then from the backward wave, cfPTT can be estimated. Because it has been verified that the time lag estimation based on of backward wave has strong correlation with the measured cfPTT. The corresponding regression function between the time lag estimation of backward wave and measured cfPTT is y = 0.96x + 5.50 (r = 0.77; p < 0.001). The estimated cfPTT using radial pressure wave decomposition based on the proposed double triangular flow wave is more accurate and convenient than the decomposition of the aortic pressure wave based on the triangular flow wave. The significance of this study is that arterial stiffness can be directly estimated from a noninvasively measured radial pressure wave. [ABSTRACT FROM AUTHOR]

Background: Primary percutaneous coronary intervention is the treatment of choice in ST-segment elevation myocardial infarction and no-reflow phenomenon is still an unsolved problem.Methods: We searched PubMed, EmBase, and Cochrane Central Register of Controlled Trials for relevant randomized controlled trials. The primary endpoint was the incidence of major adverse cardiac events and the secondary endpoint was the incidences of no-reflow phenomenon and complete resolution of ST-segment elevation.Results: Eighteen randomized controlled trials were enrolled. Nicorandil significantly reduced the incidence of no-reflow phenomenon (OR, 0.46; 95% CI, 0.36-0.59; P < 0.001; I2 = 0%) and major adverse cardiac events (OR, 0.42; 95% CI, 0.27-0.64; P < 0.001; I2 = 52%). For every single outcome of major adverse cardiac events, only heart failure and ventricular arrhythmia were significantly improved with no heterogeneity (OR, 0.36; 95% CI, 0.23-0.57, P < 0.001; OR, 0.43; 95% CI, 0.31-0.60, P < 0.001 respectively). A combination of intracoronary and intravenous nicorandil administration significantly reduced the incidence of major adverse cardiac events with no heterogeneity (OR, 0.24; 95% CI, 0.13-0.43, P < 0.001; I2 = 0%), while a single intravenous administration could not (OR, 0.66; 95% CI, 0.40-1.06, P = 0.09; I2 = 52%).Conclusions: Nicorandil can significantly improve no-reflow phenomenon and major adverse cardiac events in patients undergoing primary percutaneous coronary intervention. The beneficial effects on major adverse cardiac events might be driven by the improvements of heart failure and ventricular arrhythmia. A combination of intracoronary and intravenous administration might be an optimal usage of nicorandil. [ABSTRACT FROM AUTHOR]

L-5-Hydroxytryptophan is an important amino acid that is widely used in food and medicine. In this study, L-5-hydroxytryptophan was synthesized by a modified tryptophan synthase. A direct evolution strategy was applied to engineer tryptophan synthase from Escherichia coli to improve the efficiency of L-5-hydroxytryptophan synthesis. Tryptophan synthase was modified by error-prone PCR. A high-activity mutant enzyme (V231A/K382G) was obtained by a high-throughput screening method. The activity of mutant enzyme (V231A/K382G) is 3.79 times higher than that of its parent, and kcat/Km of the mutant enzyme (V231A/K382G) is 4.36 mM−1∙s−1. The mutant enzyme (V231A/K382G) reaction conditions for the production of L-5-hydroxytryptophan were 100 mmol/L L-serine at pH 8.5 and 35°C for 15 h, reaching a yield of L-5-hydroxytryptophan of 86.7%. Directed evolution is an effective strategy to increase the activity of tryptophan synthase. [ABSTRACT FROM AUTHOR]

Objective. Aortic stiffness is associated with risk of cardiovascular events. Carotid-femoral pulse wave velocity (cfPWV) is the current noninvasive gold standard for assessing aortic stiffness. However, the cfPWV measurement is challenging, requiring simultaneous signals at the carotid and femoral sites. Approach. In this study, the aortic PWV is estimated using a single radial pressure waveform and compared with cfPWV. 111 subjects’ aortic PWVs are estimated from the decomposition of the derived central aortic pressure waveform based on three types of reconstructed flow waveform: the peak of triangular flow waveform based on 30% ejection time (Q 30%tri), the peak of triangular flow waveform based on inflection point (Q tri), and averaged flow waveform (Q avg). The central aortic pressure waveform is derived from a radial pressure waveform via a validated transfer function. Main results. The Q avg is used for estimating aortic PWV without the determination of the peak point of the triangular flow waveforms. The estimated aortic PWV shows good agreement with cfPWV. The mean difference ± SD is 0.29 ± 1.50 m sâˆ'1 (r 2 = 0.29, p < 0.001) for the Q 30%tri; 0.27 ± 1.40 m sâˆ'1 (r 2 = 0.38, p  < 0.001) for the Q tri; 0.23 ± 1.39 m sâˆ'1 (r 2 = 0.40, p  < 0.001) for the Q avg. The correlation between estimated aortic PWV based on Q 30%tri and measured cfPWV is weak. The results of Q tri and Q avg show no obvious difference. Significance. The proposed method can be used as a less complex way than conventional measurement of cfPWV to further assess arterial stiffness and predict cardiovascular risks or events. [ABSTRACT FROM AUTHOR]

At present, percutaneous coronary intervention (PCI) is the most effective treatment of coronary artery stenosis. However, in case post-dilation of the stent is needed, the tip of the commonly used post-dilation balloon catheter cannot always pass through the stent smoothly, especially when it is situated in the curved part of the vessel. To improve the performance of traditional post-dilation balloon catheter, a preliminary design of a novel catheter with a spherical-tip is proposed. Since the performance of this spherical-tip catheter is still unclear, in this study, finite element analysis (FEA) and experimental validation of blood vessel with different curvature radii were performed to test and evaluate the performance of the spherical-tip catheter design. The comparative results between the two types of catheters demonstrate that in the simulated post-dilation process, the spherical-tip catheter is easier to pass through the stent placed in the curved vessel without the deformation of the stent strut, and can theoretically reduce the operation time and improve the safety of the operation. Furthermore, the strong consistency between simulation and experiment indicates that the finite element (FE) model can be a helpful tool for future optimization and evaluation of novel catheters, so as to save time and budget in product development and reduce/replace animal studies. [ABSTRACT FROM AUTHOR]

Purpose: Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans. Methods: A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi‐slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT. Results: The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1% ± 0.7% and 96.9% ± 0.6%, respectively. The inter‐observer variability was also assessed, resulting in a Dice index of 97.0% ± 0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4% ± 1.5% and 93.3% ± 1.3%, respectively, and the same measurement between the experts themselves was 93.6% ± 1.9%. The Pearson's correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99. Conclusions: This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg. [ABSTRACT FROM AUTHOR]

Remote photoplethysmography (rPPG) can be used for noncontact and continuous measurement of the heart rate (HR). Currently, the main factors affecting the accuracy and robustness of rPPG-based HR measurement methods are the subject's skin tone, body movement, exercise recovery, and variable or inadequate illumination. In response to these challenges, this study is aimed at investigating a rPPG-based HR measurement method that is effective under a wide range of conditions by only using a webcam. We propose a new approach, which combines joint blind source separation (JBSS) and a projection process based on a skin reflection model, so as to eliminate the interference of background illumination and enhance the extraction of pulse rate information. Three datasets derived from subjects with different skin tones considering six environmental scenarios are used to validate the proposed method against three other state-of-the-art methods. The results show that the proposed method can provide more accurate and robust HR measurement for all three datasets and is therefore more applicable to a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the right ventricle (RV) from cardiac magnetic resonance imaging (MRI) images is an essential step in estimating clinical indices such as stroke volume and ejection fraction. Recently, image segmentation methods based on fully convolutional neural networks (FCN) have drawn much attention and shown promising results. In this paper, a new fully automatic RV segmentation method combining the FC-DenseNet and the level set method (FCDL) is proposed. The FC-DenseNet is efficiently trained end-to-end, using RV images and ground truth masks to make a per-pixel semantic inference. As a result, probability images are produced, followed by the level set method responsible for smoothing and converging contours to improve accuracy. It is noted that the iteration times of the level set method is only 4 times, which is due to the semantic segmentation of the FC-DenseNet for RV. Finally, multi-object detection algorithm is applied to locate the RV. Experimental results (including 45 cases, 15 cases for training, 30 cases for testing) show that the FCDL method outperforms the U-net + level set (UL) and the level set methods that use the same dataset and the cardiac functional parameters are computed robustly by the FCDL method. The results validate the FCDL method as an efficient and satisfactory approach to RV segmentation. [ABSTRACT FROM AUTHOR]

Pedestrian detection is a crucial task in intelligent transportation systems, which can be applied in autonomous vehicles and traffic scene video surveillance systems. The past few years have witnessed much progress on the research of pedestrian detection methods, especially through the successful use of the deep learning based techniques. However, occlusion and large scale variation remain the challenging issues for pedestrian detection. In this work, we propose a Part-Aware Multi-Scale Fully Convolutional Network (PAMS-FCN) to tackle these difficulties. Specifically, we present a part-aware Region-of-Interest (RoI) pooling module to mine body parts with different responses, and select the part with the strongest response via voting. As such, a partially visible pedestrian instance can receive a high detection confidence score, making it less likely to become a missing detection. This module operates in parallel with an instance RoI pooling module to combine local parts and global context information. To handle vast scale variation, we construct a fully convolutional network in which multi-scale feature maps are generated efficiently, and small-scale and large-scale pedestrians are detected separately. By integrating these structures, the proposed detector achieves the state-of-the-art performance on the Caltech, KITTI, INRIA and ETH pedestrian detection datasets. [ABSTRACT FROM AUTHOR]

The hypothalamus plays an important role in the control of aging. Transcranial ultrasound stimulation (TUS) has been reported as a noninvasive method of neuromodulation. However, the effect of TUS of the hypothalamus on aging remains unclear. Therefore, the aim of this study is to verify whether TUS of the hypothalamus could affect the behaviors of aging mice and the expression level of apoptosis factors and inflammatory cytokines. TUS was delivered to the hypothalamus of mice (${n} = {44}$) for 14 days (15 min/day) at a fundamental frequency of 1 MHz, pulse repetition frequency of 1 kHz (US1) or 10 Hz (US2), duty cycle of 10%, and acoustic pressure of 0.13 MPa. The effect of TUS on aging was evaluated by the behavioral tests or Western blotting in different stages. The behavioral results showed that mice in the US2 group improved their movement and learning. In addition, there was a significant improvement in the grip strength after TUS in the second behavioral tests (Sham: 0.0351 ± 0.0020 N/g; US1: 0.0340 ± 0.0023 N/g; US2: 0.0425 ± 0.0029 N/g, ${p} = {0.034}$). Furthermore, the level of inflammation (TNF- $\alpha $ : Sham: 0.69 ± 0.084; US1: 0.39 ± 0.054; US2: 0.49 ± 0.1, ${p} = {0.021}$) and apoptosis (Bax: Sham: 0.47 ± 0.049; US1: 0.42 ± 0.054; US2: 0.18 ± 0.055, ${p} = {0.001}$) was significantly reduced after TUS in this stage. We did not see a long-lasting effect of TUS in the third behavioral tests. In addition, we found that TUS is safe according to hematoxylin and eosin (HE) staining. In conclusion, TUS could effectively modulate the hypothalamus, which may provide a new method for controlling aging. [ABSTRACT FROM AUTHOR]

Purpose: It is commonly believed that central hemodynamics is closely associated with the presence of cardiovascular events. However, controversial data exist on the acute response of competitive sports on central hemodynamics. Moreover, the central hemodynamic response to exercise is too transient to be investigated. Therefore, this study aimed to investigate the central hemodynamic response in young basketball athletes and controls after 1 h recovery after exercise. Methods: Fifteen young basketball athletes and fifteen aged-matched controls were recruited to perform the Bruce test. Central hemodynamics were measured and calculated, including heart rate (HR), aortic systolic, diastolic, and pulse pressure (ASP, ADP, and APP), ejection duration (ED), sub-endocardial viability ratio (SEVR), central augmentation index (AIx), and AIx@HR75. Intra-group and inter-group differences were analyzed by two-way repeated measures ANOVA. Results: ASP significantly decreased at 10 min after exercise in athletes, while it markedly declined at 15 min after exercise in controls (p < 0.01). Additionally, only in the athlete group, ADP significantly decreased at 50 min and at 1 h after exercise. AIx was also significantly reduced at 1–2, 20, 30, and 40 min after exercise (all p < 0.05). Moreover, there were significant differences in the changes of these parameters between the two groups at these measurement points (p < 0.05). SEVR significantly recovered to the baseline level after 30 min, while ED and HR returned to baseline levels at 40 min after exercise in both groups. Conclusion: Sustained decrease of aortic BPs was sooner after the cessation of exercise in athletes than in controls, and changes of aortic stiffness were more evident in athletes than those in controls during the 1 h recovery period. Additionally, SEVR returned to the baseline sooner than ED and HR in athletes. [ABSTRACT FROM AUTHOR]

Objective: For hypertensive individuals, their blood pressure (BP) is often managed by taking medications. However, antihypertensive drugs might cause adverse effects such as congestive heart failure and are ineffective in significant numbers of the hypertensive population. As an alternative method for hypertension management, non-drug devices-based neuromodulation approaches such as functional electrical stimulation (FES) have been proposed. The FES approach requires the implantation of a stimulator into the body. One recently emerging technique, called low-intensity focused ultrasound stimulation (FUS), has been proposed to non-invasively modulate neural activities. In this pilot study, the feasibility of adopting low-intensity FUS neuromodulation for BP regulation was investigated using animal models. Methods: A FUS system was developed for BP modulation in rabbits. For each rabbit, the low-intensity FUS with different acoustic intensities was used to stimulate its exposed left vagus nerve, and the BP waveform was synchronously recorded in its right common carotid artery. The effects of the different FUS intensities on systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MAP), and heart rate (HR) were extensively examined from the BP recordings. Results: The results demonstrated that the proposed FUS method could successfully induce changes in SBP, DBP, MAP, and HR values. When increasing acoustic intensities, the values of SBP, DBP, and MAP would tend to decrease more substantially. Conclusion: The findings of this study suggested that BP could be modulated through the FUS, which might provide a new way for non-invasive and non-drug management of hypertension. [ABSTRACT FROM AUTHOR]

On the basis of the continuous and dense GPS observations covering the northern segment of the Xiaojiang fault zone (n-XJFZ) from March 2012 to March 2016, we present the velocity field, spatiotemporal deformation, slip rate and locking depth of the n-XJFZ. The results provide strong support for achieving a better understanding of the deformation behavior of this fault. The heterogeneity of the GPS velocity field and relatively nonuniform distribution of seismicity suggest that the observational area is fragmented. Shear strain has been accumulating with an almost constant azimuth, which is consistent with the trends of the mapped major faults. The 2014 Ms 6.5 Ludian earthquake produced a sudden change in the dilatational strain, which was almost constant prior to the event, and an increase in the shear strain rate. The near-field deformation of the n-XJFZ estimated with the near-field data was larger than expected, revealing that the n-XJFZ is becoming more locked. These results imply that the seismic risk in the study area is currently rising and that, similar to the 2014 Ms 6.5 Ludian earthquake, future earthquakes will possibly occur away from mapped faults. [ABSTRACT FROM AUTHOR]

The accurate measurement of the arterial pulse wave is beneficial to clinical health assessment and is important for the effective diagnosis of many types of cardiovascular disease. A variety of sensors have been developed for the non-invasive detection of these waves, but the type of sensor has an impact on the measurement results. Therefore, it is necessary to compare and analyze the signals obtained under a range of conditions using various pulse sensors to aid in making an informed choice of the appropriate type. From the available types we have selected four: a piezoresistive strain gauge sensor (PESG) and a piezoelectric Millar tonometer (the former with the ability to measure contact force), a circular film acceleration sensor, and an optical reflection sensor. Pulse wave signals were recorded from the left radial, carotid, femoral, and digital arteries of 60 subjects using these four sensors. Their performance was evaluated by analyzing their susceptibilities to external factors (contact force, measuring site, and ambient light intensity) and by comparing their stability and reproducibility. Under medium contact force, the peak-to-peak amplitude of the signals was higher than that at high and low force levels and the variability of signal waveform was small. The optical sensor was susceptible to ambient light. Analysis of the intra-class correlation coefficients (ICCs) of the pulse wave parameters showed that the tonometer and accelerometer had good stability (ICC > 0.80), and the PESG and optical sensor had moderate stability (0.46 < ICC < 0.86). Intra-observer analysis showed that the tonometer and accelerometer had good reproducibility (ICC > 0.75) and the PESG and optical sensor had moderate reproducibility (0.42 < ICC < 0.91). Inter-observer analysis demonstrated that the accelerometer had good reproducibility (ICC > 0.85) and the three other sensors had moderate reproducibility (0.52 < ICC < 0.96). We conclude that the type of sensor and measurement site affect pulse wave characteristics and the careful selection of appropriate sensor and measurement site are required according to the research and clinical need. Moreover, the influence of external factors such as contact pressure and ambient light should be fully taken into account. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to investigate the feasibility of using a combination of apatinib in the treatment of non‐small cell lung cancer. Apatinib is a tyrosine kinase inhibitor which selectivelyacts on vascular endothelial growth factor receptor 2 (VEGFR‐2) and has shown good efficacy in a variety of malignancies, but the drug resistance is fast in single drug therapy. Methods: The inhibitory effect of apatinib and other drugs on lung cancer cells was determined by CCK‐8 test in vitro, and the IC50 value was determined. To establish a nude mouse xenograft model, observe the inhibitory effect of apatinib combined with other drugs on lung cancer xenografts in nude mice; immunohistochemical staining of tumor microvessel density and Ki67 expression in transplanted tumor tissues; Western blot analysis of related signaling pathways expression; immunohistochemistry was used to detect tumor microvessel density in other organs and to observe its safety. Results: In this study, we found apatinib combined with pemetrexed, the first and third generation of epidermal growth factor receptor tyrosine kinase inhibitor, could synergistically inhibit the proliferation of non‐small cell lung cancer cell (NSCLC) lines, reduce the microvessel density and Ki67 protein levels of three non‐small cell lung cancer xenografts, and enhance anti‐tumor activity by synergistically inhibiting the MAPK‐ERK and PI3K‐AKT‐mTOR signaling pathway. Furthermore, there were no pathological abnormalities in the heart, brain, liver and kidney of each group. Conclusions: The efficacy of apatinib combination is better than that of monotherapy, and there is no significant difference in toxicity of important organs, which suggests the feasibility of a combination of apatinib in the treatment of non‐small cell lung cancer. [ABSTRACT FROM AUTHOR]

This work developed a facile approach to fabricate highly sensitive and flexible polyurethane/graphene platelets composite film for wearable strain sensor. The composite film was fabricated via layer-by-layer laminating method which is simple and cost-effective; it exhibited outstanding electrical conductivity of 1430 ± 50 S/cm and high sensitivity to strain (the gauge factor is up to 150). In the sensor application test, the flexible strain sensor achieves real-time monitoring accurately for five bio-signals such as pulse movement, finger movement, and cheek movement giving a great potential as wearable-sensing device. In addition, the developed strain sensor shows response to pressure and temperature in a certain region. A multifaceted comparison between reported flexible strain sensors and our strain sensor was made highlighting the advantages of the current work in terms of (1) high sensitivity (gauge factor) and flexibility, (2) facile approach of fabrication, and (3) accurate monitoring for body motions. [ABSTRACT FROM AUTHOR]

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to compare differences in cardiorespiratory function between untreated hypertensive subjects (UHS) and healthy subjects (HS) during cardiopulmonary exercise testing (CPET). Additionally, it also aimed to explore the potential mechanisms of different exercise responses in cardiorespiratory function before, during and after CPET. Methods: Thirty subjects (15 UHS and 15 HS) were enrolled. Photoplethysmography (PPG), respiratory signal, and ECG were simultaneously collected while subjects were performing CPET. Fiducial points (a, b, c, d, e) were extracted from the second derivative of the PPG (SDPPG), and the ratios b/a, c/a, d/a, e/a, and (b-c-d-e)/a (named Aging Index, AGI) were calculated as markers of systolic and diastolic function. Additionally, respiratory rate was calculated and analyzed. Results: Before CPET, there were no significant differences in b/a, d/a, and AGI between two groups. However, after CPET, b/a (−0.9 ± 0.19 vs. −1.06 ± 0.19, p -value = 0.03) and AGI (−0.49 ± 0.75 vs. −1.15 ± 0.59, p -value = 0.011) of the UHS group were significantly higher than those of the HS. The d/a (−0.32 ± 0.24 vs. −0.14 ± 0.17, p -value = 0.024), and c/a (−0.33 ± 0.26 vs. −0.07 ± 0.19, p -value = 0.004) were significantly lower in UHS than those in HS. In contrast, before CPET, e/a (0.22 ± 0.11 vs. 0.32 ± 0.09, p -value = 0.007) in UHS was significantly lower than that in HS, while after CPET there was no significant difference between the two groups in this variable. In addition, during CPET, AGI (p -value = 0.003), and respiratory rate (p -value = 0.000) in UHS were significantly higher in comparison with before CPET. Conclusions: Different exercise responses showed the differences of cardiorespiratory function between UHS and HS. These differences not only can highlight the CV risk of UHS, but also can predict the appearance of arterial stiffness in UHS. Additionally, during CPET, significant differences in AGI, autonomic nervous function and respiratory activity assessed by respiratory rate were found between the two groups in comparison with before CPET. [ABSTRACT FROM AUTHOR]

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) images is an essential step for calculation of clinical indices such as stroke volume, ejection fraction. In this paper, a new automatic LV segmentation method combines a Hierarchical Extreme Learning Machine (H-ELM) and a new location method is developed. An H-ELM can achieve more compact and meaningful feature representations and learn the segmentation task from the ground truth. A new automatic LV location method is integrated to improve the accuracy of classification and reduce the cost of segmentation. Experimental results (including 30 cases, 10 cases for training, 20 cases for testing) show that the mean absolute deviation of images segmented by our proposed method is about 67.9, 81.3 and 98.7% of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean maximum absolute deviation of images segmented by our proposed method is about 63.5, 77.3 and 98.0% of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean dice similarity coefficient of images segmented by our proposed method is about 13.7, 9.3 and 0.5% higher than that of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean speed of our proposed method is about 38.3, 6.7 and 23.8 times faster than that of the level set, the SVM and Hu’s method, respectively. The standard deviation of our proposed method is the lowest among four methods. The results validate that our proposed method is efficient and satisfactory for the LV segmentation. [ABSTRACT FROM AUTHOR]