Electrophysiological characterization of drug response in hSC-derived cardiomyocytes using voltage-sensitive optical platforms

Introduction: Voltage-sensitive optical (VSO) sensors offer a minimally invasive method to study the time course of repolarization of the cardiac action potential (AP). This Comprehensive in vitro Proarrhythmia Assay (CiPA) cross-platform study investigates protocol design and measurement variability of VSO sensors for preclinical cardiac electrophysiology assays. Methods: Three commercial and one academic laboratory completed a limited study of the effects of 8 blinded compounds on the electrophysiology of 2 commercial lines of human induced pluripotent stem-cell derived cardiomyocytes (hSC-CMs). Acquisition technologies included CMOS camera and photometry; fluorescent voltage sensors included di-4-ANEPPS, FluoVolt and genetically encoded QuasAr2. The experimental protocol was standardized with respect to cell lines, plating and maintenance media, blinded compounds, and action potential parameters measured. Serum-free media was used to study the action of drugs, but the exact composition and the protocols for cell preparation and drug additions varied among sites. Results: Baseline AP waveforms differed across platforms and between cell types. Despite these differences, the relative responses to four selective ion channel blockers (E-4031, nifedipine, mexiletine, and JNJ 303 blocking IKr, ICaL, INa, and IKs, respectively) were similar across all platforms and cell lines although the absolute changes differed. Similarly, four mixed ion channel blockers (flecainide, moxifloxacin, quinidine, and ranolazine) had comparable effects in all platforms. Differences in repolarisation time course and response to drugs could be attributed to cell type and experimental method differences such as composition of the assay media, stimulated versus spontaneous activity, and single versus cumulative compound addition. Discussion: In conclusion, VSOs represent a powerful and appropriate method to assess the electrophysiological effects of drugs on iPSC-CMs for the evaluation of proarrhythmic risk. Protocol considerations and recommendations are provided toward standardizing conditions to reduce variability of baseline AP waveform characteristics and drug responses

Comparative study on permeability enhancement effect of separate-layer fracturing and multi-layers comprehensive fracturing in coal seam group

In this paper, a gas roadway with multiple coal seams in southwest China is taken as the test site, and the separate-layer hydraulic fracturing and multilayers comprehensive hydraulic fracturing are carried out. This paper analyzes the whole process of coal seam fracturing development, discusses the influence of water injection volume in coal seam on subsequent drilling construction, and obtains the distribution law of gas content and water content of coal seam after fracturing, and the gas drainage effect. And the fracturing influence ranges of the two fracturing methods are obtained. The results show that the hydraulic fracturing in coal seam composes of five stages: stress accumulation, fracture initiation, fracture propagation, multiple fracture initiation and propagation, and fracture propagation completion. Compared with the multilayers comprehensive hydraulic fracturing, the average pressure of separate-layer hydraulic fracturing is slightly higher, but the total water injection volume is significantly less. The gas content and water content of coal seam after separate-layer hydraulic fracturing are lower and higher than those of comprehensive fracturing technology, respectively; and the initial gas drainage concentration of coal seam is also higher. In addition, the average gas drainage concentration of single hole and drilling field are 54.1% and 43%, increased by 14.9% and 9% compared with comprehensive fracturing technology. And the effective fracturing influence radius of separate-layer fracturing and multilayers comprehensive fracturing reaches about 50 m and 40 m, respectively. This indicates that the separate-layer hydraulic fracturing technology can significantly improve the effective influence range and effect of seam permeability improvement in coal seam group. Moreover, the separate-layer hydraulic fracturing technology can reduce the water injection volume in coal seam, decreasing the bad phenomena in the subsequent drilling construction of gas drainage borehole, such as sticking drilling

Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model.

The remaining useful life (RUL) of lithium-ion batteries is a decisive factor in the stability of electric vehicle systems. Aiming at the problem of limited robustness of Deep Extreme Learning Machine (DELM) in lithium-ion battery RUL prediction, an improved whale optimization algorithm (IWOA) is proposed to improve the prediction ability of DELM. Four health features are extracted from the battery aging data, the outliers in the feature data are detected and removed using Hampel filtering, and the health features are dimensionality reduced using principal component analysis to avoid data overfitting. Then, chaotic tent mapping, positive cosine algorithm, and chaotic adaptive inertia weights are used to improve the whale optimization algorithm and increase the search diversity. The introduction of IWOA to optimize the parameter selection of the DELM model effectively solves the problems of low efficiency and poor stability of parameter selection. The method was fully validated using the cycle battery dataset and the prediction results were compared with the conventional method. The results show that the IWOA-DELM method has small prediction errors, strong state tracking fitting ability, good generalization ability, and robustness

High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework.

In response to the current issue of low accuracy and robustness in the remaining useful life (RUL) model of lithium-ion batteries. In the framework of AdaBoost, a lithium-ion battery life prediction model based on an improved whale optimization algorithm to optimize the Kernel Extreme Learning Machine (IWOA-KELM) is proposed. The IWOA-KELM model is used as a weak predictor. A weighted voting mechanism is used to set a weight coefficient for each weak predictor and then combine the strong predictor of battery RUL. Constant current charge time, constant voltage charge time, internal resistance, and incremental capacity curves peak were extracted from the Cycle data set as health features to accurately describe battery degradation. Pearson correlation coefficient and Savitzky-Golay filter preprocessed health features. Tent chaotic mapping is used to initialize whale populations and maintain their diversity. The iterative updating strategy of the hunting speed control factor is introduced to reduce the probability of the local optimal case of the whale optimization algorithm. The kernel function parameters and regularization parameters of KELM are optimized by IWOA to improve the model prediction ability. After verification, the RUL error of the method proposed in this article can be as accurate as 4 cycles

High-precision state of charge estimation of urban-road-condition lithium-ion batteries based on optimized high-order term compensation-adaptive extended Kalman filtering.

It is significant to improve the accuracy of estimating the state of charge (SOC) of lithium-ion batteries under different working conditions on urban roads. In this study, an improved second-order polarized equivalent circuit (SO-PEC) modeling method is proposed. Accuracy test using segmented parallel exponential fitting parameter identification method. Online parameter identification using recursive least squares with variable forgetting factors(VFFRLS). An optimized higher-order term compensation-adaptive extended Kalman filter (HTC-AEKF) is proposed in the process of estimating SOC. The algorithm incorporates a noise-adaptive algorithm that introduces noise covariance into the recursive process in real-time to reduce the impact of process noise and observation noise on the accuracy of SOC estimation. Multiple iterations are performed for some of the processes in the extended Kalman filter(EKF) to compensate for the accuracy impact of missing higher-order terms in the linearization process. Model validation results show over 98% accuracy. The results after comparing with the EKF algorithm show a 4.1% improvement in SOC estimation accuracy under Hybrid Pulse Power Characterization(HPPC) working conditions. 2.7% improvement in accuracy in Dynamic Stress Test(DST) working conditions. 2.1% improvement in Beijing Bus Dynamic Stress Test(BBDST) working conditions. The superiority of the algorithm is demonstrated

High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model.

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algorithm for optimizing the hybrid kernel least squares support vector regression machine used in lithium-ion battery SOH prediction. This research extracted four key health features from the raw data of each battery in the Cycle dataset, which is publicly accessible. Data preprocessing of health features involved Pearson correlation analysis and Hampel filtering techniques. The framework of least squares support vector regression constructs a hybrid kernel function of polynomial kernel function and radial basis function. The integration of differential evolution and the law of survival of the fittest into the grey wolf algorithm enhances its optimization ability. The improved grey wolf algorithm optimizes the parameters of the hybrid kernel least squares support vector regression machine, improving the accuracy and robustness of the model. After data validation, it is known that the optimal average absolute error value predicted by the model can reach 0.32%. This indicates that the proposed method is effective and feasible

Toxic effects and mechanism of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) on Lemna minor

To investigate the toxic effect and mechanism of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) in aquatic plants, in vivo and in vitro exposure to BDE-47 were conducted. After 14-d exposure to 5-20 mu g/L BDE-47, the growth of Lemna minor plants was significantly suppressed, and the chlorophyll and soluble protein contents in fronds markedly decreased. Accordingly, the photosynthetic efficiency (Fv/Fm, PI) decreased. When the thylakoid membranes isolated from healthy fronds was exposed to 5-20 mg/L BDE-47 directly in vitro for 1 h, the photosynthetic efficiency also decreased significantly. In both the in vitro (5-20 mu g/L) and in vivo (5-20 mg/L) experiments, BDE-47 led to an increased plasma membrane permeability. Hence, we concluded that BDE-47 had a direct toxicity to photosynthetic membranes and plasma membranes. However, direct effects on the activities of peroxidase (POD), malate dehydrogenase (MDH) and nitroreductase (NR) were not observed by adding 5-20 mg/L BDE-47 into crude enzyme extracts. The malondialdehyde (MDA) and superoxide anion radical (O-2(-)) contents in the BDE-47 treated fronds were higher than those in the control fronds, suggesting that L. minor can not effectively relieve reactive oxygen species (ROS). The data above indicates that BDE-47 is toxic to L. minor through acting directly on biomembranes, which induces the production of ROS and thus causes remarkable oxidative damage to cells. (C) 2017 Elsevier Ltd. All rights reserved