Dominance of HIV-1 Subtype CRF01_AE in Sexually Acquired Cases Leads to a New Epidemic in Yunnan Province of China

BACKGROUND: Dating back to the first epidemic among injection drug users in 1989, the Yunnan province has had the highest number of human immunodeficiency virus type 1 (HIV-1) infections in China. However, the molecular epidemiology of HIV-1 in Yunnan has not been fully characterized. METHODS AND FINDINGS: Using immunoassays, we identified 103,015 accumulated cases of HIV-1 infections in Yunnan between 1989 and 2004. We studied 321 patients representing Yunnan's 16 prefectures from four risk groups, 11 ethnic populations, and ten occupations. We identified three major circulating subtypes: C/CRF07_BC/CRF08_BC (53%), CRF01_AE (40.5%), and B (6.5%) by analyzing the sequence of p17, which is part of the gag gene. For patients with known risk factors, 90.9% of injection drug users had C/CRF07_BC/CRF08_BC viruses, whereas 85.4% of CRF01_AE infections were acquired through sexual transmission. No distinct segregation of CRF01_AE viruses was found among the Dai ethnic group. Geographically, C/CRF07_BC/CRF08_BC was found throughout the province, while CRF01_AE was largely confined to the prefectures bordering Myanmar. Furthermore, C/CRF07_BC/CRF08_BC viruses were found to consist of a group of viruses, including C, CRF08_BC, CRF07_BC, and new BC recombinants, based on the characterization of their reverse transcriptase genes. CONCLUSIONS: This is the first report of a province-wide HIV-1 molecular epidemiological study in Yunnan. While C/CRF07_BC/CRF08_BC and CRF01_AE are codominant, the discovery of many sexually transmitted CRF01_AE cases is new and suggests that this subtype may lead to a new epidemic in the general Chinese population. We discuss implications of our results for understanding the evolution of the HIV-1 pandemic and for vaccine development

Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal

Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients

Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal

Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients

Research on the Cooperative Passive Location of Moving Targets Based on Improved Particle Swarm Optimization

Aiming at the cooperative passive location of moving targets by UAV swarm, this paper constructs a passive location and tracking algorithm for a moving target based on the A optimization criterion and the improved particle swarm optimization (PSO) algorithm. Firstly, the localization method of cluster cooperative passive localization is selected and the measurement model is constructed. Then, the problem of improving passive location accuracy is transformed into the problem of obtaining more target information. From the perspective of information theory, using the A criterion as the optimization target, the passive localization process for static targets is further deduced. The Recursive Neural Network (RNN) is used to predict the probability distribution of the target’s location in the next moment so as to improve the localization method and make it suitable for the localization of moving targets. The particle swarm algorithm is improved by using grouping and time period strategy, and the algorithm flow of moving target location is constructed. Finally, through the simulation verification and algorithm comparison, the advantages of the algorithm in this paper are presented

Real Time Screening and Trajectory Optimization of UAVs in Cluster Based on Improved Particle Swarm Optimization Algorithm

To solve the problem of selecting drones for passive positioning within unmanned aerial vehicle (UAV) swarm and optimizing corresponding trajectories. This article constructs a method for determining and optimizing the trajectory of UAVs based on an improved particle swarm optimization (PSO) algorithm. Firstly, the time difference of arrival (TDOA) positioning principle was introduced and corresponding algorithm models were organized. Afterwards, the objective function and constraint conditions for selecting drones and optimizing flight paths were designed. The correlation between the optimal solutions of the continuous time optimization problem is used to determine the UAV for positioning. This paper constructs the UAV determination process based on similarity screening. At the same time, combined with the characteristics of the problem to be optimized, the Particle Swarm Optimization (PSO) is improved from three aspects: updating the initial position of particles, improving the iteration formula and setting the adaptive termination condition. This paper further constructs the track optimization process based on improved particle swarm optimization. Through simulation experiments and algorithm comparison, it can be seen that the method constructed in this article can determine the drone used for positioning in real-time and optimize its spatial position. Compared to the selected drones and mainstream passive positioning methods, the method in this article reduces errors by more than 60% and 45%

Analysis of Motion Characteristics and Stability of Mobile Robot Based on a Transformable Wheel Mechanism

In this research, we propose a novel wheel-legged mobile robot to address the problems of insufficient obstacle-crossing performance and poor motion flexibility of mobile robots in non-structural environments. Firstly, we designed the transformable wheel mechanism and tail adaptive mechanism. Secondly, the kinematic model of the robot is established and solved by analyzing the whole motion and wheel-legged switching motion for the operation requirements under different road conditions. By synthesizing the constraint relationships among the modules and analyzing the robot’s obstacle-crossing abilities, we systematically established the mechanical model of the robot when it encounters obstacles. Thirdly, we studied the stability of the robot based on the stable cone method in the case of slope and unilateral transformation wheel deployment and achieved the tipping condition in the critical state. Finally, we used ADAMS software to simulate and analyze the driving process of the robot in various types of terrain and obstacles in order to verify that it has superior performance through obstacles and motion flexibility. The analysis shows that the robot can passively adapt to various complex and variable obstacle-filled terrains with obstacle heights which are much higher than its center of gravity range. The results of the study can provide a reference for the structural optimization and the obstacle-crossing performance improvement of mobile robots

Analysis of Motion Characteristics and Stability of Mobile Robot Based on a Transformable Wheel Mechanism

In this research, we propose a novel wheel-legged mobile robot to address the problems of insufficient obstacle-crossing performance and poor motion flexibility of mobile robots in non-structural environments. Firstly, we designed the transformable wheel mechanism and tail adaptive mechanism. Secondly, the kinematic model of the robot is established and solved by analyzing the whole motion and wheel-legged switching motion for the operation requirements under different road conditions. By synthesizing the constraint relationships among the modules and analyzing the robot’s obstacle-crossing abilities, we systematically established the mechanical model of the robot when it encounters obstacles. Thirdly, we studied the stability of the robot based on the stable cone method in the case of slope and unilateral transformation wheel deployment and achieved the tipping condition in the critical state. Finally, we used ADAMS software to simulate and analyze the driving process of the robot in various types of terrain and obstacles in order to verify that it has superior performance through obstacles and motion flexibility. The analysis shows that the robot can passively adapt to various complex and variable obstacle-filled terrains with obstacle heights which are much higher than its center of gravity range. The results of the study can provide a reference for the structural optimization and the obstacle-crossing performance improvement of mobile robots

Transcriptomic Analysis Identifies Candidate Genes Related to Intramuscular Fat Deposition and Fatty Acid Composition in the Breast Muscle of Squabs (Columba)

Despite the fact that squab is consumed throughout the world because of its high nutritional value and appreciated sensory attributes, aspects related to its characterization, and in particular genetic issues, have rarely been studied. In this study, meat traits in terms of pH, water-holding capacity, intramuscular fat content, and fatty acid profile of the breast muscle of squabs from two meat pigeon breeds were determined. Breed-specific differences were detected in fat-related traits of intramuscular fat content and fatty acid composition. RNA-Sequencing was applied to compare the transcriptomes of muscle and liver tissues between squabs of two breeds to identify candidate genes associated with the differences in the capacity of fat deposition. A total of 27 differentially expressed genes assigned to pathways of lipid metabolism were identified, of which, six genes belonged to the peroxisome proliferator-activated receptor signaling pathway along with four other genes. Our results confirmed in part previous reports in livestock and provided also a number of genes which had not been related to fat deposition so far. These genes can serve as a basis for further investigations to screen markers closely associated with intramuscular fat content and fatty acid composition in squabs. The data from this study were deposited in the National Center for Biotechnology Information (NCBI)’s Sequence Read Archive under the accession numbers SRX1680021 and SRX1680022. This is the first transcriptome analysis of the muscle and liver tissue in Columba using next generation sequencing technology. Data provided here are of potential value to dissect functional genes influencing fat deposition in squabs