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BACKGROUND Epilepsy is a syndrome characterized by transient, rigid, paroxysmal, and repetitive central nervous system dysfunction. Prevention, control, and improvement of cognitive and behavioral dysfunction are of great significance for improving the patients’ intellectual development and quality of life. Electroencephalograms (EEG) can predict an accelerated decline in cognitive function. AIM To determine the clinical and EEG characteristics and treatment results of benign epilepsy in spiking children. METHODS A total of 106 cases of benign epilepsy in children with myocardial spines treated at our hospital from January 2017 to January 2020 were selected. Differences in clinical data and EGG characteristics between treatment-effective/-ineffective patients were analyzed, and children’s intellectual development before and after treatment evaluated using the Gesell Development Diagnostic Scale. RESULTS EEG showed that the discharge proportion in the awake and sleep periods was 66.04%, and the peak/peak discharge was mainly single-sided, accounting for 81.13%, while the discharge generalization accounted for 31.13%. There was no significant difference in any of these variables between sexes and ages (P > 0.05). The proportion of patients with early onset (< 5 years old) and seizure frequency > 3 times/half a year was 40.00% and 60.00%, respectively; the incidence rate and seizure frequency in the younger age group (< 5 years old) were significantly higher than those in the treatment-effective group (P < 0.05), while the discharge index was significantly lower than that in the treatment-effective group (P < 0.05). The discharge index was negatively correlated with fine motor skill and language development (r = -0.274 and -0.247, respectively; P < 0.05), but not with the rest (P > 0.05). Logistic regression analysis showed that low age onset (< 5 years old) and seizure frequency were the factors affecting ineffective-treatment of benign epilepsy in children (odds ratio = 11.304 and 5.784, respectively; P < 0.05). The discharge index of the responsive group after treatment was significantly lower than that of the unresponsive group (P < 0.05). However, there was no significant difference between groups after treatment in gross and fine motor skills, adaptability, language, and personal social development (P > 0.05). CONCLUSION The EEG of children with benign epilepsy due to spinal wave in central time zone has characteristic changes, and the therapeutic effect is influenced by age of onset and attack frequency.

Background Pleural effusion (PE) has been one of the promising sources of liquid biopsy in advanced lung cancer patients. However, its clinical utility is not widely accepted due to the lack of full estimation of its potential versus routine clinical samples. Method A total of 164 advanced lung cancer patients were enrolled with 164 matched tumor tissue and PE-cfDNA, 153 accompanied plasma and 63 1 PE-sDNA. Result PE-cfDNA displayed significantly higher median mutant allele frequency and an overall mutation concordance rate of 65% to tissue, which was higher than PE-sDNA (43%) and plasma-cfDNA (43%). The discrepancies between PE-cfDNA and tumor tissue were high in several genes, including SMARCA4, PIK3CA, ERBB2, KM T2A, ALK and NF1. For clinically actionable mutations, the concordance rate between PE-cfDNA and tumor tissue is 87%. Eleven patients were identified with actionable mutations in PE-cfDNA and four patients benefited from PE-cfDNA-guided targeted. Meanwhile, PE-cfDNA recapitulated mutations of diverse tissue origins and provided more mutational information under the circumstance that tumor tissue or tumor tissue of different origins were unavailable. The combination of tumor tissue and PE-cfDNA profiling increased positive detection rates of patients compared to tumor tissue alone. Our finding highlighted the importance of PE-cfDNA in the optimal selection of patients for targeted therapy. Conclusion The PE-cfDNA-based liquid biopsy displays better performance in the characterization of gene alterations than PE-sDNA and plasma-cfDNA. PE-cfDNA together with tumor tissue profiling optimizes comprehensively genomic profiling of lung cancer patients, which might be important for selecting patients for better treatment management.

In this study, a low complexity WLAN indoor positioning system based on the curve fitting, the interpolation, and an adaptive neural fuzzy system is addressed. The proposed modeling techniques are used to reduce the false distance estimation of a mobile device to the access points induced by the unpredictable interference, reflection and multipath effects. It is clear that the experimental results show that a empirical propagation model can be easily provided by the curve fitting based approach but involves a limited flexibility and adaptation; the interpolation based approach has a simple computation for the distance estimation and a high flexibility due to the possible arragement of the reference nodes but requires horrible trails and errors for optimization. Therefore a hybrid learning algorithm under a combination of gradient descent and the least-squares method based neural fuzzy inference system is proposed to solve the problems mentioned above. Due to its nonlinear ability, fast learning capacity and adaptation capability, the accurate distance estimates of a mobile device to the access points can be obtained. The distances are firstly converted by the addressed ANFIS propagation model during the the online stage in real-time with the currently observed RSSI value from the tested WLAN area. The available distances from the positions localized in the test bed are recursively estimated by an extended Kalman filter while it could blend the information optimally minimizing the variance of the estimation error. Consequently, the presented approach is verified to provide the accurate localization in the real indoor environment of 20.6m×37.4m with 4 access points only. The addressed positioning algorithm is straightforward and can be implemented by simple mobile devices, such as PDAs or mobile phones, to provide the location-aware services without any hardware cost since WLAN signals are popular sensor sources for indoor environments. There are many potential applications for location positioning and network accessing, such as library or museum tour-guide, free mobility and nursing at home, patient transporting in the hospital and easy going capability in the shopping mall.

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The driver's physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human's health status. When detecting the driver's HR, using traditional contact-type devices might bring about the driver's distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver's HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver's HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers' HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver's HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the success-rate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).

Since the permanent magnet synchronous motor system in this research needs about 40 ms to finish a control cycle, such a long delay in time strongly causes the bad performance for the conventional controllers, especially for position control. To well control the speed and position, an adaptive neural predictive control is proposed. A two-layer recursive neural network is employed as a speed predictor, and an extended Kalman filter is utilized to tune the parameters of the predictor adaptively. Chaos optimization algorithm and Newton-Raphson optimization are combined to solve the problem of predictive control. As for the speed control, the proposed method shows better performance. The position control is designed based on the speed control. Due to the physical limitation of the plant, the steady state error is still large. Hence, a fuzzy compensator is applied. From the experiment, the error is reduced obviously.

As computers are widely used in daily life, computer users spend a lot of time staring at screens. long-term sitting postures lead to upper body pain which brings negative physical and mental impacts. Any exaggerated forward curvature in the upper back is called kyphosis. The common unhealthy postures are cervical kyphosis, thoracic kyphosis, and raised-shoulders. PoseX can recognize cervical spine kyphosis, thoracic spine kyphosis, and raised-shoulders from a facial image with only one general webcam. Based on the body key-points provided by OpenPose, PoseX uses the proportional relation between face width, shoulders width, and ears-shoulders height as the features to extract depth information from the RGB image. In our experiment, 15 computer users were recruited. The sitting scenarios include four tasks (normal, cervical kyphosis, thoracic kyphosis, and raised-shoulders) and three distance conditions (60 cm, 85 cm, 110 cm), PoseX records personalized parameters in 3 seconds and performs an accuracy higher or equal to 84 percent in each task. The proposed technique shows a promising low-cost health monitoring system for computer users.

BACKGROUND Sudden sensorineural hearing loss (SSNHL) is currently treated with a combination of drugs, predominantly with glucocorticoids (GCs). However, the mechanisms of action of GCs in SSNHL are unknown. This study aimed to analyze the role of endoplasmic reticulum stress (ERS) in SSNHL pathogenesis and prognosis. MATERIAL AND METHODS In this study, we evaluated the expression and activation status of the protein kinase RNA-like endoplasmic reticulum kinase (PERK)-C/EBP homologous protein (CHOP) pathway in peripheral blood mononuclear cells (PBMCs) from patients with SSNHL and compared them with those in healthy controls. We also compared differences in expression of activating transcription factor 4 (ATF4) and CHOP before and after glucocorticoid treatment in patients with improved and unimproved SSNHL. RESULTS Treatment with GCs significantly improved hearing in 55% of patients with SSNHL. Levels of phosphorylated PERK (p-PERK) and phosphorylated eukaryotic initiation factor 2alpha were increased in PBMCs from patients with SSNHL compared with healthy controls. ATF4 and CHOP expression were also significantly elevated. After treatment, the amount of ATF4 and CHOP proteins in PBMCs in the patients whose SSNHL improved was significantly reduced compared with the levels measured before treatment in all patients with SSNHL. The expression of the ATF4 and CHOP proteins in PBMCs in the unimproved group, however, was not significantly changed relative to pretreatment levels. CONCLUSIONS ERS may play a significant role in the pathogenesis of SSNHL, and the responsiveness of the condition to GC-mediated mitigation of ERS may be one of the key factors that affect patient prognosis.

Atrial fibrillation (AF) has been proven highly correlated to stroke; more than 43 million people suffer from AF worldwide. However, most of these patients are unaware of their disease. There is no convenient tool by which to conduct a comprehensive screening to identify asymptomatic AF patients. Hence, we provide a non-contact AF detection approach based on remote photoplethysmography (rPPG). We address motion disturbance, the most challenging issue in rPPG technology, with the NR-Net, ATT-Net, and SQ-Mask modules. NR-Net is designed to eliminate motion noise with a CNN model, and ATT-Net and SQ-Mask utilize channel-wise and temporal attention to reduce the influence of poor signal segments. Moreover, we present an AF dataset collected from hospital wards which contains 452 subjects (mean age, 69.313.0 years; women, 46%) and 7,306 30-second segments to verify the proposed algorithm. To our best knowledge, this dataset has the most participants and covers the full age range of possible AF patients. The proposed method yields accuracy, sensitivity, and specificity of 95.69%, 96.76%, and 94.33%, respectively, when discriminating AF from normal sinus rhythm. More than previous studies, other arrhythmias are also taken into consideration, leading to a further investigation of AF vs. Non-AF and AF vs. Other scenarios. For the three scenarios, the proposed approach outperforms the benchmark algorithms. Additionally, the accuracy of the slight motion data improves to 95.82%, 92.39%, and 89.18% for the three scenarios, respectively, while that of full motion data increases by over 3%.