One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide

Elevation of circulating big endothelin-1: an independent prognostic factor for tumor recurrence and survival in patients with esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Endothelin(ET) axis plays a key role in many tumor progression and metastasis via various mechanisms such as angiogenesis, mediating extracellular matrix degradation and inhibition of apoptosis. However, there is limited information regarding the clinical significance of plasma big ET-1 levels in esophageal cancer patients. Circulating plasma big ET-1 levels were measured in patients with esophageal squamous cell carcinoma(ESCC) to evaluate the value of ET-1 as a biomarker for predicting tumor recurrence and patients survival.</p> <p>Methods</p> <p>Preoperative plasma big ET-1 concentrations were measured by an enzyme linked immunosorbent assay(ELISA) in 108 ESCC patients before surgery, and then again at 1,2,3,10 and 30 days after curative radical resection for ESCC. The association between preoperative plasma big ET-1 levels and clinicopathological features, tumor recurrence and patient survival, and their changes following surgery were evaluated.</p> <p>Results</p> <p>The preoperative plasma big ET-1 levels in ESCC patients were significantly higher than those in controls. And there was a significant association between plasma big ET-1 levels and disease stage, as well as invasion depth of the tumor and lymph node status. Furthermore, plasma big ET-1 levels decreased significantly after radical resection of the primary tumor and patients with postoperative recurrence had significantly higher plasma big ET-1 levels than that of patients without recurrence. Finally, the survival rate of patients with higher plasma big ET-1 concentrations (>4.3 pg/ml) was significantly lower than that of patients with lower level (≤ 4.3 pg/ml). Multivariate regression analysis showed that plasma big ET-1 level is an independent prognostic factor for survival in patients with ESCC.</p> <p>Conclusion</p> <p>Plasma big ET-1 level in ESCC patients may reflect malignancy and predict tumor recurrence and patient survival. Therefore, the preoperative plasma big ET-1 levels may be a clinically useful biomarker for choice of multimodality therapy in ESCC patients.</p

Neural Prescribed Performance Control for Uncertain Marine Surface Vessels without Accurate Initial Errors

This paper deals with the problems concerned with the trajectory tracking control with prescribed performance for marine surface vessels without velocity measurements in uncertain dynamical environments, in the presence of parametric uncertainties, unknown disturbances, and unknown dead-zone. First, only the ship position and heading measurements are available and a high-gain observer is used to estimate the unmeasurable velocities. Second, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed via the preprocessing. At last, based on neural network approximation in combination with backstepping and Lyapunov synthesis, a robust adaptive neural control scheme is developed to handle the uncertainties and input dead-zone characteristics. Under the designed adaptive controller for marine surface vessels, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the prescribed transient and steady tracking control performance is guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed method

ECG-based identity recognition via deterministic learning

In this paper, a novel method based on electrocardiogram (ECG) signals is proposed for identity recognition. A unique feature called dynamics, which is fundamentally different from features used in literature, is extracted from ECG signals and used for identity recognition. Deterministic learning, a recently proposed machine learning approach, is used to model the dynamics of training ECG signals. A set of estimators employing the modelling results of training ECG signals is constructed. Through comparing the test ECG signal (measured from a subject to be recognized) with the estimators, a set of errors can be obtained and used to measure the similarity between the test and the training ECG signals. The test ECG signal is recognized in accordance with the smallest error, and then the subject can be recognized rapidly. Experimental results indicate that the proposed method is reliable and efficient for identity recognition

Energy Shaping Control for Wireless Power Transfer System in Automatic Guided Vehicles

This paper proposes an energy shaping controller of a DC/DC converter for automatic guided vehicles (AGVs) wireless power transfer (WPT). A transformer is inserted after the LCC topology to improve the transfer power, and the DC/DC boost converter is added before this topology to obtain desired systematic power dynamically. The system power transfer model is derived based on the idea of voltage transformation and the desired power can be implemented indirectly through regulating desired output voltage of DC/DC converter. With the proposed controller, this WPT system will have a much better dynamic performance and the effective working time can be increased significantly. Furthermore, this paper proposes dynamical regulation strategy for output power to get real time target power according to the charging curve of the battery. Simulation and experimental results verified the control performance of the proposed control scheme. A WPT prototype with power up to 1.65 kW was built, and 92.12% efficiency from DC power source to battery load is achieved, which is 4% higher than that obtained by the conventional PID method

A Novel Control Strategy for Uninterruptible Power Supply Based on Backstepping and Fuzzy Neural Network

This paper proposes a novel control strategy for controlling the uninterruptible power supply (UPS) inverter, which is based on backstepping control theory combined with a fuzzy neural network (FNN). The advantage of backstepping control is that it can decompose a complex system into multiple subsystems, stabilize the control object according to Lyapunov stability theory, and simplify the controller design. However, it requires prior knowledge of multiple system parameters. FNN can approximate arbitrary nonlinear functions and system errors, which can reduce the parameters required for controller design. Hence, Combining the advantages of both methods, a UPS inverter control method with only a few parameters is designed. Then the sliding mode gain is added to compensate for the fuzzy neural network to reduce the chattering when the system operates and ensure the needed power quality. To verify the effectiveness of the proposed control system, the effectiveness of the proposed method is verified by a simulation experiment platform

Accuracy of Digital Impressions and Fitness of Single Crowns Based on Digital Impressions

In this study, the accuracy (precision and trueness) of digital impressions and the fitness of single crowns manufactured based on digital impressions were evaluated. #14-17 epoxy resin dentitions were made, while full-crown preparations of extracted natural teeth were embedded at #16. (1) To assess precision, deviations among repeated scan models made by intraoral scanner TRIOS and MHT and model scanner D700 and inEos were calculated through best-fit algorithm and three-dimensional (3D) comparison. Root mean square (RMS) and color-coded difference images were offered. (2) To assess trueness, micro computed tomography (micro-CT) was used to get the reference model (REF). Deviations between REF and repeated scan models (from (1)) were calculated. (3) To assess fitness, single crowns were manufactured based on TRIOS, MHT, D700 and inEos scan models. The adhesive gaps were evaluated under stereomicroscope after cross-sectioned. Digital impressions showed lower precision and better trueness. Except for MHT, the means of RMS for precision were lower than 10 μm. Digital impressions showed better internal fitness. Fitness of single crowns based on digital impressions was up to clinical standard. Digital impressions could be an alternative method for single crowns manufacturing