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Due to the negative autopsy and without cardiac structural abnormalities, unexpected sudden cardiac death （USCD） is always a tough issue for forensic pathological expertise. USCD may be associated with parts of fatal arrhythmic diseases. These arrhythmic diseases may be caused by disorders of cardiac ion channels or channel-related proteins. Caveolin can combine with multiple myocardial ion channel proteins through its scaffolding regions and plays an important role in maintaining the depolarization and repolarization of cardiac action potential. When the structure and function of caveolin are affected by gene mutations or abnormal protein expression, the functions of the regulated ion channels are correspondingly impaired, which leads to the occurrence of multiple channelopathies, arrhythmia or even sudden cardiac death. It is important to study the effects of caveolin on the functions of ion channels for exploring the mechanisms of malignant arrhythmia and sudden cardiac death.

Abstract This study evaluates the relationship between obstructive sleep apnea (OSA) and asthma. Literature search was carried out in several electronic databases and random effects meta-analyses were performed to obtain pooled estimates of the prevalence of OSA, OSA risk and sleep disordered breathing (SDB) in asthma patients and pooled odds ratios of the prevalence between asthma and non-asthma patients. In adult asthma patients, the prevalence [95% confidence interval] of OSA, OSA risk, and SDB was 49.50 [36.39, 62.60] %, 27.50 [19.31, 35.69] %, and 19.65 [14.84, 24.46] % respectively. The odds of having OSA, OS risk and SDB by the asthma patients were 2.64 [1.76, 3.52], 3.73 [2.90, 4.57] and 1.73 [1.11, 2.36] times higher (p

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

Objective: Previous studies have revealed inconsistent results regarding the association between occult hepatitis B virus (HBV) infection and chronic kidney disease (CKD). Therefore, we conducted a prospective cohort study to evaluate the association between occult HBV infection and CKD. Methods: A total of 4329 adults, aged 46.2 ± 13.7 years, without CKD at baseline were enrolled while undergoing physical examinations. Occult HBV infection was defined as seropositivity for antibody to HBV core antigen. CKD was defined as decreased estimated glomerular filtration rate (eGFR  0.05). After adjustment for potential confounders in the multivariate Logistic regression analysis, age, hypertension, diabetes, and the highest quartile of uric acid were associated with CKD, with ORs of 1.04 (95% CI: 1.02â1.05), 2.1 (95% CI: 1.46â3.01), 2.02 (95% CI: 1.36â2.99), and 1.86 (95% CI: 1.17â2.95), respectively. However, occult HBV infection was not associated with CKD, with an OR of 1.12 (95% CI: 0.65â1.95). Conclusions: This study did not find an association between occult HBV infection and CKD. However, high-risk patients infected with HBV should still be targeted for monitoring for the development of CKD. Keywords: Chronic kidney disease, Proteinuria, Estimated glomerular filtration rate, Hepatitis B virus

Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.

In order to improve the performance of cyclostationary spectrum sensing,reduce the influence of deep fading,a new approach of combining the multiple received signals by multiple antennas was proposed,which means that exploited an improved MRC combination of cyclic autocorrelation and cyclic spectrum,and got the test statistic calculated by implementing asymptotic optimal χ2 test.This approach could detect the existence of the transmitting signal without amplitude prior information.BPSK modulation signal was applied as an example to testify the accuracy of the approach.The results indicate that multiple antennas spectrum sensing has more advantage that single antenna,reduces the influence of deep fading.