HBV and HCV Co-Infection in Chinese Newly Diagnosed HIV+ Subjects in 2015 and 2023: A Cross-Sectional Study.

With shared routes of transmission, HBV and HCV co-infection are estimated to occur more in subjects with HIV. This study aimed to characterize and describe the prevalence of HBV and HCV co-infections in a cohort of newly diagnosed HIV+ subjects living in China. We conducted a cross-sectional study among newly diagnosed HIV+ subjects aged 18-100 who participated in surveys on the national HIV molecular epidemiology in 2015 and 2023. (The epidemiological table survey is located in the national database alongside serologic testing). The chi-square test was used to identify changes in infections between the studying populations in 2015 and 2023, and conditional logistic regression models were fit to identify risk factors for each co-infection. Among the 11,024 newly diagnosed HIV+ subjects who were surveyed (n = 4501 in 2015; n = 6523 in 2023), the prevalence of HBV, HCV, and HBV/HCV in 2023 was lower than that in 2015, respectively. No decrease was observed in HCV co-infection in men who had sex with men (MSM) in North China, Northeast China, and East China. Increasing recognition among those at high risk of heterosexual transmission and those with low educational backgrounds is paramount to the prevention and control of HIV/HBV/HCV infections.

Research on Stacked Piezoelectric Cymbal Vibrator.

As demand for haptic feedback increases, piezoelectric materials have become one of the best candidate materials due to their small size, high electromechanical coupling coefficient, and fast response. A stacked piezoelectric cymbal vibrator is proposed based on the common cymbal-type transducer, which is composed of a piezoelectric stack to drive and a cymbal disk to amplify displacement. A coupling theoretical model between the piezoelectric stack and the cymbal-type structure is established. The longitudinal and radial displacements of the stacked piezoelectric cymbal vibrator are calculated in the low frequency range (<1000 Hz) by the theoretical model and the finite element method. The theoretical and numerical results are in good agreement. The results show that the radial displacement can be converted into longitudinal displacement and then effectively amplified by the cymbal disk with an amplification ratio of 30. The feature is conducive to its widespread application in the field of consumer electronics.

Improved sparse domain super-resolution reconstruction algorithm based on CMUT.

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

Design and Experimental Study of a Stepping Piezoelectric Actuator with Large Stroke and High Speed.

A stepping piezoelectric actuator is proposed with large stroke and high speed. The piezoelectric actuator consists of two symmetrical stators and a mover. The actuator can operate with a "double-drive, four-clamp" mode. The proposed actuator solves the problems of short stroke, low speed, and small load inherent in the currently published stepping piezoelectric actuators. By combining Workbench software with APDL language, finite element simulation and statics and dynamics analysis are carried out to guide the design of the actuator. The new piezoelectric simulation method can solve the difficulties regarding parameter setting and loading voltage on multiple interfaces for a complex piezoelectric model. Therefore, the novel method is helpful to develop the simulation of multilayer thin piezoelectric devices. The prototype of the actuator is developed and tested. Experimental results show that the actuator can run stably in the range of 0 to 600 Hz. The driving stroke is greater than 85 mm, the resolution can reach 535 nm, the maximum driving speed is 6.11 mm/s, and the maximum load is 49 N.

Bifurcation analysis of a sheep brucellosis model with testing and saturated culling rate.

Testing-culling is a very effective measure for the prevention and control of animal diseases. In this paper, based on sheep brucellosis control policies and animal testing characteristics and considering the limitation of culling resources, a dynamic model is established to study the impact of testing-culling measure. Theoretical analysis reveals that the model may have one or three positive equilibria. The equilibrium in the middle is always unstable, and the model shows saddle-node bifurcation, generalized Hopf bifurcation and Bogdanov-Takens bifurcation. Moreover, the theoretical results are verified via numerical analysis. These results reveal that testing and culling strategies can induce complex transmission dynamics that can help us develop appropriate prevention and control measures for animal brucellosis.

Design of GHz Mechanical Nanoresonator with High Q-Factor Based on Optomechanical System.

Micro-electromechanical systems (MEMS) have dominated the interests of the industry due to its microminiaturization and high frequency for the past few decades. With the rapid development of various radio frequency (RF) systems, such as 5G mobile telecommunications, satellite, and other wireless communication, this research has focused on a high frequency resonator with high quality. However, the resonator based on an inverse piezoelectric effect has met with a bottleneck in high frequency because of the low quality factor. Here, we propose a resonator based on optomechanical interaction (i.e., acoustic-optic coupling). A picosecond laser can excite resonance by radiation pressure. The design idea and the optimization of the resonator are given. Finally, with comprehensive consideration of mechanical losses at room temperature, the resonator can reach a high Q-factor of 1.17 × 104 when operating at 5.69 GHz. This work provides a new concept in the design of NEMS mechanical resonators with a large frequency and high Q-factor.

Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy.

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.

The improved grasshopper optimization algorithm and its applications.

Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

Effect of Ultrasound-Guided Quadratus Lumborum Block Preemptive Analgesia on Postoperative Recovery of Patients with Open Radical Colon Cancer Surgery: A Retrospective Study.

PURPOSE: To evaluate the effect of ultrasound-guided quadratus lumborum block (QLB) preemptive analgesia on recovery in colon cancer patients undergoing open radical surgery and provide reference for its clinical application. METHODS: From July 2019 to June 2020, according to the anesthesia method, 56 patients who received open radical colon surgery were divided into two groups: Group Q (n=27), which received QLB combined general anesthesia, and Group C (n=29), which received general anesthesia only. Both groups were given self-controlled intravenous analgesia pump after surgery. The primary outcome is a series of parameters representing postoperative recovery. The secondary outcome was VAS scores and opioid consumption. RESULTS: The first time of getting up, flatus, taking semi-liquid diet and the postoperative hospital stay in Group Q were significantly reduced (P<0.01). The rest and active VAS scores were significantly lower in Group Q (P<0.01). The opioids consumption was significantly decreased in Group Q (P<0.05). CONCLUSION: The application of ultrasound-guided QLB preemptive analgesia in open radical colon cancer surgery can significantly enhance the postoperative analgesia effect, reduce opioid consumption, and accelerate the postoperative recovery of the patients. CLINICAL TRIAL REGISTRATION NUMBER: The Chinese Clinical Trial Registry (ChiCTR-2000034824).

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features.

Breast cancer (BC) is the primary threat to women's health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients.

BACKGROUND: COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. METHODS: COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ2-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. RESULTS: A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). CONCLUSIONS: A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

Prediction and analysis of Corona Virus Disease 2019.

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.

MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient.

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

The safety and efficacy of dexmedetomidine versus propofol for patients undergoing endovascular therapy for acute stroke: A prospective randomized control trial.

BACKGROUND: It is uncertain if dexmedetomidine has more favorable pharmacokinetic profile than the traditional sedative drug propofol in patients who undergo endovascular therapy for acute stroke. We conducted a prospective randomized control trial to compare the safety and efficacy of dexmedetomidine with propofol for patients undergoing endovascular therapy for acute stroke. METHODS: A total of 80 patients who met study inclusion criteria were received either propofol (n = 45) or dexmedetomidine (n = 35) between January 2016 and August 2018. We recorded the favorable neurologic outcome (modified Rankin score <3) both at discharge and 3 months after stroke, National Institute of Health Stroke scale (NIHSS) at 48 hours post intervention, modified thrombolysis in myocardial infarction score on digital subtraction angiography, intraprocedural hemodynamics, recovery time, relevant time intervals, satisfaction score of the surgeon, mortality, and complications. RESULTS: There were no significant differences between the 2 groups (P > .05) with respect to heart rate, respiratory rate, and SPO2 during the procedure. The mean arterial pressure (MAP) was significantly low in the propofol group until 15 minutes after anesthesia was induced. No difference was recorded between the groups at the incidence of fall in MAP >20%, MAP >40% and time spent with MAP fall >20% from baseline MAP. In the propofol group, the time spent with MAP fall >40% from baseline MAP was significantly long (P < .05). Midazolam and fentanyl were similar between the 2 groups (P > .05) that used vasoactive drugs. The time interval from stroke onset to CT room, from stroke onset to groin puncture, and from stroke onset to recanalization/end of the procedure, was not significantly different between the 2 groups (P > .05). The recovery time was longer in the dexmedetomidine group (P < .05). There was no difference between the groups with respect to complications, favorable neurological outcome, and mortality both at hospital discharge and 3 months later, successful recanalization and NIHSS score after 48 hours (P > .05). However, the satisfaction score of the surgeon was higher in the dexmedetomidine group (P < .05). CONCLUSIONS: Dexmedetomidine was undesirable than propofol as a sedative agent during endovascular therapy in patients with acute stroke for a long-term functional outcome, though the satisfaction score of the surgeon was higher in the dexmedetomidine group.

Regional level influenza study based on Twitter and machine learning method.

The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.

Distributed Brillouin optical fiber temperature and strain sensing at a high temperature up to 1000 °C by using an annealed gold-coated fiber.

In this study, the distributed temperature and strain sensing with an annealed single mode gold-coated optical fiber over a wide temperature range up to 1000 °C is demonstrated by using the differential pulse pair (DPP) Brillouin optical time domain analysis (BOTDA). Owing to the protection provided by the gold coating, the fiber can withstand high temperature environments and maintain a high strength, which enables the gold-coated fiber acting as a repeatable high-temperature sensor. After annealing twice to remove the internal stress, the temperature coefficient of the gold-coated fiber is stable and consistent with a nonlinear function. Owing to the residual stress accumulated during the cooling process of coating and the low yield strength of gold, a pre-pulling test is essential to measure the strain of a gold-coated fiber. An equal axial force model is used to recalculate the strain distribution induced by the large temperature difference within the furnace. The high-temperature strain coefficient of an annealed gold-coated fiber decreases with temperature, i.e. from ~0.046 MHz/µÎµ at 100 °C to ~0.022 MHz/µÎµ at 1000 °C, mainly due to the increase in Young's modulus of silica with temperature. To the best of our knowledge, this is the first time that an annealed gold-coated fiber has been applied for distributed high-temperature strain sensing, which demonstrates the potential applications for strain monitoring in complex, high-temperature devices such as jet engines or turbines.

Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network.

Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may help save lives. In this paper, we use the Twitter data set and the United States Centers for Disease Control's influenza-like illness (ILI) data set to predict a nearly real-time regional unweighted percentage ILI in the United States by use of an artificial neural network optimized by the improved artificial tree algorithm. The results show that the proposed method is an efficient approach to real-time prediction.

Initialization by a novel clustering for wavelet neural network as time series predictor.

The architecture and parameter initialization of wavelet neural network are discussed and a novel initialization method is proposed. The new approach can be regarded as a dynamic clustering procedure which will derive the neuron number as well as the initial value of translation and dilation parameters according to the input patterns and the activating wavelets functions. Three simulation examples are given to examine the performance of our method as well as Zhang's heuristic initialization approach. The results show that the new approach not only can decide the WNN structure automatically, but also provides superior initial parameter values that make the optimization process more stable and quickly.

Analysis of a piezoelectric power harvester with adjustable frequency by precise electric field method.

A power harvester with adjustable frequency, which consists of a hinged-hinged piezoelectric bimorph and a concentrated mass, is studied by the precise electric field method (PEFM), taking into account a distribution of the electric field over the thickness. Usually, using the equivalent electric field method (EEFM), the electric field is approximated as a constant value in the piezoelectric layer. Charge on the upper electrode (UEC) of the bimorph is often assumed as output charge. However, different output charge can be obtained by integrating on electric displacement over the electrode with different thickness coordinates. Therefore, an average charge (AC) on thickness is often assumed as the output value. This method is denoted EEFM AC. The flexural vibration of the bimorph is calculated by the three methods and their results are compared. Numerical results illustrate that EEFM UEC overestimates resonant frequency, output power, and efficiency. EEFM AC can accurately calculate the output power and efficiency, but underestimates resonant frequency. The performance of the harvester, which depends on concentrated mass weight, position, and circuit load, is analyzed using PEFM. The resonant frequency can be modulated 924 Hz by moving the concentrated mass along the bimorph. This feature suggests that the natural frequency of the harvester can be adjusted conveniently to adapt to frequency fluctuation of the ambient vibration.

A piezoelectric spring-mass system as a low-frequency energy harvester.

We propose a new structure consisting of a piezoelectric spring-mass system as a low-frequency piezoelectric energy harvester. A theoretical model is developed for the system from the theory of piezoelectricity. An analysis is performed to demonstrate the low-frequency nature of the system. Other basic characteristics of the energy harvester, including the output power, voltage, and efficiency, are also calculated and examined.