A Causal Intervention Scheme for Semantic Segmentation of Quasi-Periodic Cardiovascular Signals.

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology ( Am) and rhythm ( Ar). Our key insight is to suppress the over-dependence on Am or Ar while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively. In this article, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.

lncRNA DANCR promotes the migration an invasion and of trophoblast cells through microRNA-214-5p in preeclampsia.

Studies have shown that lncRNA DANCR is down-regulated in placental tissues of patients with preeclampsia (PE). The aim of this study was to explore the effect of lncRNA DANCR on trophoblast cells as well as its acting mechanism. We disrupted or overexpressed lncRNA DANCR in trophoblast cells HTR-8/SVneo and JEG-3 and detected the associated cellular functional changes by MTT, flow cytometry, Transwell experiment, and scratch experiment. The results showed that overexpression of lncRNA DANCR significantly increased the proliferation, invasion, migration, and EMT process of trophoblast cells. Interfering with lncRNA DANCR showed the opposite result. Further, the targeted interaction between lncRNA DANCR and miR-214-5p was confirmed by the dual-luciferase reporter gene assay. In addition, the expression of PI3K/AKT signaling pathway-related proteins was analyzed by Western blot. Overexpression of lncRNA DANCR can increase the phosphorylation of PI3K/AKT protein and activate this signaling pathway. In conclusion, the enforcing of lncRNA DANCR activates the activation of the PI3K/AKT pathway by down-regulating miR-214-5p, and promotes the migration and invasion of chorionic trophoblast cells. This provides a potential new target for PE therapy.

Temporal-Framing Adaptive Network for Heart Sound Segmentation Without Prior Knowledge of State Duration.

OBJECTIVE: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference. METHODS: In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III). RESULTS: TFAN achieved a superior F1 score for all 12 databases except for 'Test-B,' with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall F1 score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN. CONCLUSION: TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN. SIGNIFICANCE: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

Adaptive Federated IMM Filter for AUV Integrated Navigation Systems.

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

Novel greener approached synthesis of polyacrylic nanoparticles for therapy and care of gestational diabetes.

In the present medical diagnostic method for the therapeutic of gestational diabetes mellitus (GDM), it is problematic and difficult to release successful and secure release of drugs to the exact site. Hence, many researchers have been carried out to bring antidiabetic using modern method to release of drugs for their production. This research work focusses on to provide an assemblage to the recent growth in the field of Ramulus mori extract (RME) loaded on polyacrylic gold nanoparticle for antidiabetics with special highlighting on nursing of GDM. Keynote of gold nanoparticle: diabetes mellitus, nursing, insulin, antidiabetic, drugs, and new system for drug delivery. Rat is used to test the drug delivery system. In vivo examination was not prepared seldom including in this research paper. This research investigation could be a new avenue for the development of drug delivery system of GDM.

Association of hemorrhage at termination in caesarean scar pregnancy using the crossover sign of ultrasound image.

PURPOSE: To analyze whether crossover sign (COS) can help predict the risk of bleeding during surgical evacuation in patients with caesarean scar pregnancy (CSP). METHODS: This study retrospectively analyzed the clinical presentations, ultrasound images and treatment outcomes of patients with CSP. The relationship among the gestational sac, caesarean scar and the anterior uterine wall, defined as the COS, was analyzed to predict the risk of severe bleeding during surgical evacuation in these patients. All patients were categorized according to the relationship between the endometrial line and the superior-inferior diameter of the gestational sac into crossover sign-1 and crossover sign-2 groups. The Mann-Whitney U test was used to compare the data with non-normal distribution, and logistic regression analysis was performed to identify the correlates of severe bleeding. RESULTS: A total of 74 patients were included. In COS-1 group (n = 21), 16 (76.19%) patients suffered heavy bleeding(≥200 mL) during surgical evacuation, while COS-2 group (n = 53) had only 1(11.89%) patient complaint of heavy bleeding (≥200 mL) (P < 0.01). Adverse surgical outcomes were more common in women with COS-1. Logistic regression analysis showed that COS-1 (OR, 7.93; 95% CI, 1.35-46.67) was independently associated with severe bleeding. CONCLUSION: COS can help predict who has a higher risk of severe hemorrhage in patients with CSP and guide the clinical treatment selection for optimal management of this condition.

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System.

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

Optimized Multi-Position Calibration Method with Nonlinear Scale Factor for Inertial Measurement Units.

Navigation grade inertial measurement units (IMUs) should be calibrated after Inertial Navigation Systems (INSs) are assembled and be re-calibrated after certain periods of time. The multi-position calibration methods with advantage of not requiring high-precision equipment are widely discussed. However, the existing multi-position calibration methods for IMU are based on the model of linear scale factors. To improve the precision of INS, the nonlinear scale factors should be calibrated accurately. This paper proposes an optimized multi-position calibration method with nonlinear scale factor for IMU, and the optimal calibration motion of IMU has been designed based on the analysis of sensitivity of the cost function to the calibration parameters. Besides, in order to improve the accuracy and robustness of the optimization, an estimation method on initial values is presented to solve the problem of setting initial values for iterative methods. Simulations and experiments show that the proposed method outperforms the calibration method without nonlinear scale factors. The navigation accuracy of INS can be improved by up to 17% in lab conditions and 12% in the moving vehicle experiment, respectively.

An Improved Adaptive Compensation H∞ Filtering Method for the SINS' Transfer Alignment Under a Complex Dynamic Environment.

Transfer alignment on a moving base under a complex dynamic environment is one of the toughest challenges in a strapdown inertial navigation system (SINS). With the aim of improving rapidity and accuracy, velocity plus attitude matching is applied in the transfer alignment model. Meanwhile, the error compensation model is established to calibrate and compensate the errors of inertial sensors online. To suppress the filtering divergence during the process of transfer alignment, this paper proposes an improved adaptive compensation H∞ filtering method. The cause of filtering divergence has been analyzed carefully and the corresponding adjustment and optimization have been made in the proposed adaptive compensation H∞ filter. In order to balance accuracy and robustness of the transfer alignment system, the robustness factor of the adaptive compensation H∞ filter can be dynamically adjusted according to the complex external environment. The aerial transfer alignment experiments illustrate that the adaptive compensation H∞ filter can effectively improve the transfer alignment accuracy and the pure inertial navigation accuracy under a complex dynamic environment, which verifies the advantage of the proposed method.

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems.

Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles.

Terrain-aided navigation is a potentially powerful solution for obtaining submerged position fixes for autonomous underwater vehicles. The application of terrain-aided navigation with high-accuracy inertial navigation systems has demonstrated meter-level navigation accuracy in sea trials. However, available sensors may be limited depending on the type of the mission. Such limitations, especially for low-grade navigation sensors, not only degrade the accuracy of traditional navigation systems, but further impact the ability to successfully employ terrain-aided navigation. To address this problem, a tightly-coupled navigation is presented to successfully estimate the critical sensor errors by incorporating raw sensor data directly into an augmented navigation system. Furthermore, three-dimensional distance errors are calculated, providing measurement updates through the particle filter for absolute and bounded position error. The development of the terrain aided navigation system is elaborated for a vehicle equipped with a non-inertial-grade strapdown inertial navigation system, a 4-beam Doppler Velocity Log range sensor and a sonar altimeter. Using experimental data for navigation performance evaluation in areas with different terrain characteristics, the experiment results further show that the proposed method can be successfully applied to the low-cost AUVs and significantly improves navigation performance.

A Direct and Non-Singular UKF Approach Using Euler Angle Kinematics for Integrated Navigation Systems.

This paper presents a direct and non-singular approach based on an unscented Kalman filter (UKF) for the integration of strapdown inertial navigation systems (SINSs) with the aid of velocity. The state vector includes velocity and Euler angles, and the system model contains Euler angle kinematics equations. The measured velocity in the body frame is used as the filter measurement. The quaternion nonlinear equality constraint is eliminated, and the cross-noise problem is overcome. The filter model is simple and easy to apply without linearization. Data fusion is performed by an UKF, which directly estimates and outputs the navigation information. There is no need to process navigation computation and error correction separately because the navigation computation is completed synchronously during the filter time updating. In addition, the singularities are avoided with the help of the dual-Euler method. The performance of the proposed approach is verified by road test data from a land vehicle equipped with an odometer aided SINS, and a singularity turntable test is conducted using three-axis turntable test data. The results show that the proposed approach can achieve higher navigation accuracy than the commonly-used indirect approach, and the singularities can be efficiently removed as the result of dual-Euler method.