Analysis of miRNAs and their target genes associated with lipid metabolism in duck liver

Citation: He, J. et al. Analysis of miRNAs and their target genes associated with lipid metabolism in duck liver. Sci. Rep. 6, 27418; doi: 10.1038/srep27418 (2016).Fat character is an important index in duck culture that linked to local flavor, feed cost and fat intake for costumers. Since the regulation networks in duck lipid metabolism had not been reported very clearly, we aimed to explore the potential miRNA-mRNA pairs and their regulatory roles in duck lipid metabolism. Here, Cherry-Valley ducks were selected and treated with/without 5% oil added in feed for 2 weeks, and then fat content determination was performed on. The data showed that the fat contents and the fatty acid ratios of C17:1 and C18:2 were up-regulated in livers of oil-added ducks, while the C12:0 ratio was down-regulated. Then 21 differential miRNAs, including 10 novel miRNAs, were obtain from the livers by sequencing, and 73 target genes involved in lipid metabolic processes of these miRNAs were found, which constituted 316 miRNA-mRNA pairs. Two miRNA-mRNA pairs including one novel miRNA and one known miRNA, N-miR-16020-FASN and gga-miR-144-ELOVL6, were selected to validate the miRNA-mRNA negative relation. And the results showed that N-mir-16020 and gga-miR-144 could respectively bind the 3?-UTRs of FASN and ELOVL6 to control their expressions. This study provides new sights and useful information for future research on regulation network in duck lipid metabolism

Construction and validation of a nomogram of risk factors for new-onset atrial fibrillation in advanced lung cancer patients after non-surgical therapy

ObjectiveRisk factors of new-onset atrial fibrillation (NOAF) in advanced lung cancer patients are not well defined. We aim to construct and validate a nomogram model between NOAF and advanced lung cancer.MethodsWe retrospectively enrolled 19484 patients with Stage III-IV lung cancer undergoing first-line antitumor therapy in Shanghai Chest Hospital between January 2016 and December 2020 (15837 in training set, and 3647 in testing set). Patients with pre-existing AF, valvular heart disease, cardiomyopathy were excluded. Logistic regression analysis and propensity score matching (PSM) were performed to identify predictors of NOAF, and nomogram model was constructed and validated.ResultsA total of 1089 patients were included in this study (807 in the training set, and 282 in the testing set). Multivariate logistic regression analysis showed that age, c-reactive protein, centric pulmonary carcinoma, and pericardial effusion were independent risk factors, the last two of which were important independent risk factors as confirmed by PSM analysis. Nomogram included independent risk factors of age, c-reactive protein, centric pulmonary carcinoma, and pericardial effusion. The AUC was 0.716 (95% CI 0.661–0.770) and further evaluation of this model showed that the C-index was 0.716, while the bias-corrected C-index after internal validation was 0.748 in the training set. The calibration curves presented good concordance between the predicted and actual outcomes.ConclusionCentric pulmonary carcinoma and pericardial effusion were important independent risk factors for NOAF besides common ones in advanced lung cancer patients. Furthermore, the new nomogram model contributed to the prediction of NOAF

Metal Object Detection in a Wireless Power Transfer System Based on Double-Layer Symmetric Sensing Coils

The intrusion of a metal object into a wireless charging system could cause safety issues, such as temperature rise and even combustion. This paper proposes a double-layer symmetric sensing coil design for metal object detection (MOD) of wireless electric vehicle charging to prevent the detection blind area. First, the magnetic effect and the eddy current effect of a metal object in high-frequency magnetic fields are analyzed. Second, the principle of the proposed double-layer symmetric sensing coils is detailed. Finally, the finite element simulation is implemented in order to test the performance of the proposed sensing coil design. The simulation results show that the proposed double-layer symmetric sensing coils can effectively detect the presence and position of the metal object simultaneously

A Multi-Particle Physics-Based Model of a Lithium-Ion Battery for Fast-Charging Control Application

The charging safety of electric vehicles is an area of focus in the electric automobile industry. For the purpose of ensuring safety, charging electric vehicles as soon as possible is a goal pursued by the public. In order to ensure the safety of electric vehicles during fast charging and to reduce the cycle life decay of the battery, a simplified multi-particle lithium-ion battery model is proposed, based on the pseudo two-dimensional (P2D) model. The model was developed by considering heterogeneous electrochemical reactions in the negative electrode area. The Butler–Volmer (BV) kinetic equation and the distribution of the pore wall flux in the negative electrode is approximated by the quasi-linear approximation method. Furthermore, this paper also analyzes the conditions of lithium precipitation from the negative electrode of a lithium-ion battery in the case of high charging rates, which has a certain reference significance for fast-charging control applications. The experimental and simulation results show that the model has a high simulation accuracy and can reflect the heterogeneity of electrochemical reactions in the negative electrode of the battery. The model can be adapted to fast-charging control applications

Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks

Accurate capacity estimation of onboard lithium-ion batteries is crucial to the performance and safety of electric vehicles. In recent years, data-driven methods based on partial charging curve have been widely studied due to their low requirement of battery knowledge and easy implementation. However, existing data-driven methods are usually based on a fixed voltage segment or state of charge, which would be failed if the charging process does not cover the predetermined segment due to the user’s free charging behavior. This paper proposes a capacity estimation method using multiple small voltage sections and back propagation neural networks. It is intended to reduce the requirement of the length of voltage segment for estimating the complete battery capacity in an incomplete charging cycle. Firstly, the voltage segment most possibly covered is selected and divided into a number of small sections. Then, sectional capacity and skewness of the voltage curve are extracted from these small voltage sections, and severed as health factors. Secondly, the Box–Cox transformation is adopted to enhance the correlation between health factors and the capacity. Thirdly, multiple back propagation neural networks are constructed to achieve capacity estimation based on each voltage section, and their weighted average is taken as the final result. Finally, two public datasets are employed to verify the accuracy and generalization of the proposed method. Results show that the root mean square error of the fusion estimation is lower than 4.5%

A Multiple Legs Inverter with Real Time–Reflected Load Detection Used in the Dynamic Wireless Charging System of Electric Vehicles

Dynamic wireless power transfer is a potentially effective method to solve issues related to the range anxiety of electric vehicles (EVs) and reduce the cost of on-board batteries. A novel multiple legs inverter topology with a reflected load identification method for dynamic EV charging is proposed in this paper. In the proposed circuit topology, several inductor-capacitor-capacitor (LCC) reactive power compensation resonant networks and primary pads are selectively excited through a sole primary converter. Besides, a high-response and simple method for the reflected load identification is proposed to rapidly and precisely detect the EV’s position, providing accurate power regulation reference to the converter. With the proposed method, the system can realize high-response and closed-loop power control precisely without any additional wireless communication and position detection devices. Simulation and experimental results verified the efficiency of the proposed scheme. Additionally, the cost comparison results reveal that the proposed scheme could reduce costs by nearly 78% in comparison with the conventional scheme

An adaptive-gain complementary filter for real-time human motion tracking with MARG sensors in free-living environments

High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth\u27s magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods. © 2001-2011 IEEE

Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data

In electric vehicle technologies, the state of health prediction and safety assessment of battery packs are key issues to be solved. In this paper, the battery system data collected on the electric vehicle data management platform is used to model the corresponding state of health of the electric vehicle during charging and discharging processes. The increment in capacity in the same voltage range is used as the battery state of health indicator. In order to improve the modeling accuracy, the influence of ambient temperature on the capacity performance of the battery pack is considered. A temperature correction coefficient is added to the battery state of health model. Finally, a double exponential function is used to describe the process of battery health decline. Additionally, for the case where the amount of data is relatively small, model migration is also applied in the method. Particle swarm optimization algorithm is used to calibrate the model parameters. Based on the migration battery pack model and parameter identification method, the proposed method can obtain accurate battery pack SOH prediction result. The method is simple and easy to perform on the electric vehicle data management platform