CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes

CpG islands (CGIs) are CpG-rich regions compared to CpG-depleted bulk DNA of mammalian genomes and are generally regarded as the epigenetic regulatory regions in association with unmethylation, promoter activity and histone modifications. Accurate identification of CpG islands with epigenetic regulatory function in bulk genomes is of wide interest. Here, the common features of functional CGIs are identified using an average mutual information method to differentiate functional CGIs from the remaining CGIs. A new approach (CpG mutual information, CpG_MI) was further explored to identify functional CGIs based on the cumulative mutual information of physical distances between two neighboring CpGs. Compared to current approaches, CpG_MI achieved the highest prediction accuracy. This approach also identified new functional CGIs overlapping with gene promoter regions which were missed by other algorithms. Nearly all CGIs identified by CpG_MI overlapped with histone modification marks. CpG_MI could also be used to identify potential functional CGIs in other mammalian genomes, as the CpG dinucleotide contents and cumulative mutual information distributions are almost the same among six mammalian genomes in our analysis. It is a reliable quantitative tool for the identification of functional CGIs from bulk genomes and helps in understanding the relationships between genomic functional elements and epigenomic modifications

Pre-anesthetic use of butorphanol for the prevention of emergence agitation in thoracic surgery: A multicenter, randomized controlled trial

BackgroundEmergence agitation (EA) is common in patients after general anesthesia (GA) and is associated with poor outcomes. Patients with thoracic surgery have a higher incidence of EA compared with other surgery. This study aimed to investigate the impact of pre-anesthetic butorphanol infusion on the incidence of EA in patients undergoing thoracic surgery with GA.Materials and methodsThis prospective randomized controlled trial (RCT) was conducted in 20 tertiary hospitals in China. A total of 668 patients undergoing elective video-assisted thoracoscopic lobectomy/segmentectomy for lung cancer were assessed for eligibility, and 620 patients were enrolled. In total, 296 patients who received butorphanol and 306 control patients were included in the intention-to-treat analysis. Patients in the intervention group received butorphanol 0.02 mg/kg 15 min before induction of anesthesia. Patients in the control group received volume-matched normal saline in the same schedule. The primary outcome was the incidence of EA after 5 min of extubation, and EA was evaluated using the Riker Sedation-Agitation Scale (RSAS). The incidence of EA was determined by the chi-square test, with a significance of P < 0.05.ResultsIn total, 296 patients who received butorphanol and 306 control patients were included in the intention-to-treat analysis. The incidence of EA 5 min after extubation was lower with butorphanol treatment: 9.8% (29 of 296) vs. 24.5% (75 of 306) in the control group (P = 0.0001). Patients who received butorphanol had a lower incidence of drug-related complications (including injecting propofol pain and coughing with sufentanil): 112 of 296 vs. 199 of 306 in the control group (P = 0.001) and 3 of 296 vs. 35 of 306 in the control group (P = 0.0001).ConclusionThe pre-anesthetic administration of butorphanol reduced the incidence of EA after thoracic surgery under GA.Clinical trial registration[http://www.chictr.org.cn/showproj.aspx?proj=42684], identifier [ChiCTR1900025705]

Prediction-Based Region Tracking Control Scheme for Autonomous Underwater Vehicle

This paper addresses the region tracking control problem for an autonomous underwater vehicle (AUV) and proposes a prediction-based region tracking control (PRTC) scheme for AUV. In the PRTC scheme, the idea of prediction is adopted to solve the problems of overshoot and high energy consumption due to the lack of consideration of the large inertia of the AUV in the traditional scheme. The PRTC scheme predicts the future position of AUV through the past time-series position of AUV and the outer boundary of the desired region, and then designs the controller depending on the predicted results. Furthermore, the relationship between the desired region and the control output of the proposed PRTC scheme is studied. It is found that its control output amplitude is susceptible to the desired region range, resulting in output saturation. Therefore, this paper proposes a control law optimization scheme considering the desired region. This optimization scheme modifies the error signal in the control law of the PRTC scheme so that it is only related to the relative position of the desired region where the AUV is located. Finally, the proposed schemes are applied on the ODIN AUV, and the simulation results verify the feasibility of the proposed schemes

A Real-Time Detection Method for Concrete Surface Cracks Based on Improved YOLOv4

Many structures in civil engineering are symmetrical. Crack detection is a critical task in the monitoring and inspection of civil engineering structures. This study implements a lightweight neural network based on the YOLOv4 algorithm to detect concrete surface cracks. In the extraction of backbone and the design of neck and head, the symmetry concept is adopted. The model modules are improved to reduce the depth and complexity of the overall network structure. Meanwhile, the separable convolution is used to realize spatial convolution, and the SPP and PANet modules are improved to reduce the model parameters. The convolutional layer and batch normalization layer are merged to improve the model inference speed. In addition, using the focal loss function for reference, the loss function of object detection network is improved to balance the proportion of the cracks and the background samples. To comprehensively evaluate the performance of the improved method, 10,000 images (256 × 256 pixels in size) of cracks on concrete surfaces are collected to build the database. The improved YOLOv4 model achieves an mAP of 94.09% with 8.04 M and 0.64 GMacs. The results show that the improved model is satisfactory in mAP, and the model size and calculation amount are greatly reduced. This performs better in terms of real-time detection on concrete surface cracks

Command-Filter-Based Region-Tracking Control for Autonomous Underwater Vehicles with Measurement Noise

This paper investigates the AUV region-tracking control problem with measurement noise and transient and steady-state constraints. To achieve the fluctuation of AUV tracking error within an expected region while satisfying the transient and steady-state performance constraints, this paper proposes an improved nonlinear tracking error transformation method. This method converts the tracking error into a new virtual error variable through nonlinear conversion, thus transforming the above performance requirements for the tracking error into boundedness requirements for the new virtual error variable. In addition, aiming at the problem of measurement noise causing strong fluctuation of the control signal, this paper proposes a finite-time AUV control method based on a two-stage command filter. This method utilizes a finite-time sliding mode differentiator to filter the virtual control signal during the derivation of the control law using the backstepping technique. In light of the signal loss incurred by two-stage filtering and its potential impact on system stability, a finite-time compensator is designed to compensate the signal loss and achieve finite-time stability of the closed-loop system. Finally, simulations conducted using ODIN AUV demonstrate that the proposed method exhibits smooth control signal and low energy consumption characteristics. Furthermore, the tracking error meets the requirements for both transient and steady-state performance, as well as regional tracking

A DNN-Based OFDM Channel Estimation Algorithm Without Training Overheads

In this paper, we propose a channel estimation algorithm for OFDM systems based on a deep neural network to reduce overheads in model training. In this method, the channel estimation problem is formulated as an image repair problem, where a channel matrix containing pilot values is regarded as an incomplete picture, and then a specially designed deep neural network based on the deep image prior (DIP) is exploited to reconstruct complete and noise-removed channel images from the incomplete picture. While reducing complexity and training overheads, the method also ensures estimation accuracy. Simulation results show the superior performance and effectiveness of the proposed channel estimation algorithm

Application of Piezoelectric Material and Devices in Bone Regeneration

Bone injuries are common in clinical practice. Given the clear disadvantages of autologous bone grafting, more efficient and safer bone grafts need to be developed. Bone is a multidirectional and anisotropic piezoelectric material that exhibits an electrical microenvironment; therefore, electrical signals play a very important role in the process of bone repair, which can effectively promote osteoblast differentiation, migration, and bone regeneration. Piezoelectric materials can generate electricity under mechanical stress without requiring an external power supply; therefore, using it as a bone implant capable of harnessing the body’s kinetic energy to generate the electrical signals needed for bone growth is very promising for bone regeneration. At the same time, devices composed of piezoelectric material using electromechanical conversion technology can effectively monitor the structural health of bone, which facilitates the adjustment of the treatment plan at any time. In this paper, the mechanism and classification of piezoelectric materials and their applications in the cell, tissue, sensing, and repair indicator monitoring aspects in the process of bone regeneration are systematically reviewed

Effectiveness of blended learning on improving medical student’s learning initiative and performance in the physiology study

AbstractIn recent years, online learning has been widely used than before. However, it depends on student’s learning initiative and lacks of teacher-student interaction, which cannot bring desired learning performance. In our physiology teaching practice to “5 + 3” integration medical student, we tried to combine online and classroom learning to form the blended learning. This study assessed the effectiveness of the blended learning on students’ learning initiative and performance in the physiology study. It included 180 full-time students from clinical medical specialty across two academic years. These students were divided into the experimental classes receiving blended learning and the control classes receiving traditional learning. We carried out three classroom tests and one questionnaire survey. It found that the students of blended learning who mastered most of the content were as twice as the students of traditional learning. The average accuracy of classroom tests was above 90% from the students of the blended learning, which was higher than the approximate rate of 80% of from the students of the traditional learning. Both the times of preparing lessons and answering questions in class increased in the blended learning practice. In addition, students of blended learning were relatively unaffected by playing with the mobile phones in the class. It found that students acquired more knowledge, performed better in the classroom tests and their learning initiative was excited in the blended learning.This study provides the effect assessment and contributes to improve the teaching effect of the blended learning

Micro Non-Uniform Linear Array (MNULA) for Ultrasound Plane Wave Imaging

Ultrasound plane wave imaging technology has been applied to more clinical situations than ever before because of its rapid imaging speed and stable imaging quality. Most transducers used in plane wave imaging are linear arrays, but their structures limit the application of plane wave imaging technology in some special clinical situations, especially in the endoscopic environment. In the endoscopic environment, the size of the linear array transducer is strictly miniaturized, and the imaging range is also limited to the near field. Meanwhile, the near field of a micro linear array has serious mutual interferences between elements, which is against the imaging quality of near field. Therefore, we propose a new structure of a micro ultrasound linear array for plane wave imaging. In this paper, a theoretical comparison is given through sound field and imaging simulations. On the basis of primary work and laboratory technology, micro uniform and non-uniform linear arrays were made and experimented with the phantom setting. We selected appropriate evaluation parameters to verify the imaging results. Finally, we concluded that the micro non-uniform linear array eliminated the artifacts better than the micro uniform linear array without the additional use of signal processing methods, especially for target points in the near-field. We believe this study provides a possible solution for plane wave imaging in cramped environments like endoscopy