Drug-Tolerant Cancer Cells Show Reduced Tumor-Initiating Capacity: Depletion of CD44+ Cells and Evidence for Epigenetic Mechanisms

Cancer stem cells (CSCs) possess high tumor-initiating capacity and have been reported to be resistant to therapeutics. Vice versa, therapy-resistant cancer cells seem to manifest CSC phenotypes and properties. It has been generally assumed that drug-resistant cancer cells may all be CSCs although the generality of this assumption is unknown. Here, we chronically treated Du145 prostate cancer cells with etoposide, paclitaxel and some experimental drugs (i.e., staurosporine and 2 paclitaxel analogs), which led to populations of drug-tolerant cells (DTCs). Surprisingly, these DTCs, when implanted either subcutaneously or orthotopically into NOD/SCID mice, exhibited much reduced tumorigenicity or were even non-tumorigenic. Drug-tolerant DLD1 colon cancer cells selected by a similar chronic selection protocol also displayed reduced tumorigenicity whereas drug-tolerant UC14 bladder cancer cells demonstrated either increased or decreased tumor-regenerating capacity. Drug-tolerant Du145 cells demonstrated low proliferative and clonogenic potential and were virtually devoid of CD44+ cells. Prospective knockdown of CD44 in Du145 cells inhibited cell proliferation and tumor regeneration, whereas restoration of CD44 expression in drug-tolerant Du145 cells increased cell proliferation and partially increased tumorigenicity. Interestingly, drug-tolerant Du145 cells showed both increases and decreases in many “stemness” genes. Finally, evidence was provided that chronic drug exposure generated DTCs via epigenetic mechanisms involving molecules such as CD44 and KDM5A. Our results thus reveal that 1) not all DTCs are necessarily CSCs; 2) conventional chemotherapeutic drugs such as taxol and etoposide may directly target CD44+ tumor-initiating cells; and 3) DTCs generated via chronic drug selection involve epigenetic mechanisms

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

A thermal runaway prognosis scheme for battery systems in electric vehicles is proposed based on the big data platform and entropy method. It realizes the diagnosis and prognosis of thermal runaway simultaneously, which is caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data is derived from the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing. Furthermore, a thermal security management strategy for thermal runaway is presented under the Z-score approach. The abnormity coefficient is introduced to present real-time precautions of temperature abnormity. The results illustrated that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. The presented method is flexible in all disorder systems and possesses widespread application potential in not only electric vehicles, but also other areas with complex abnormal fluctuating environments

Research on the Effects of Different Electrolyte Ratios on Heat Loss Control in Lithium-Ion Batteries

As the demand for high-performance battery technology increases, the new energy vehicle industry has an urgent need for safer and more efficient battery systems. A model combining five side reactions was developed to be applied to the studies related to this paper. In this paper, the thermal runaway triggering process of Li-ion batteries is simulated, and the relationship between the local heating of the cathode collector surface and the change of the high-temperature area distribution of the diaphragm layer is analyzed. The thermal runaway mechanism is further revealed. Based on the simulation results, the following conclusions can be drawn: phosphonitene compounds can delay the decomposition of the solid electrolyte interphase membrane and reduce the energy yield of battery-side reactions. Compared with the phosphonitene compound, the optimized structure of adding phosphonitene has little effect on the thermal stability of the battery

Impact of information intervention on travel mode choice of urban residents with different goal frames: A controlled trial in Xuzhou, China

In order to assess the degree to which specific groups will adapt their travel behaviors after certain intervention, this study utilized a cluster analysis to discuss three segments’ distinct goal frames, social-demographic properties, travel modes, and habitat, and then carried out an information intervention controlled trial to discover three segments’ modal split shifts. The results indicate that the information have consistent and distinct impacts on travel mode choice by clusters. This consistency is embodied in the simultaneous and significant increase in travel times by green modes (walking, non-powered bicycle, or bus) and in the small but non-significant effects on reducing car use in the three clusters. The distinctness of the impacts is that information have a more effective influence on subjects with gain goal frames because their travel times by all three green modes greatly improved. Subjects with the hedonic goal frame are the least sensitive to information, with the only significant increase in travel times being by non-powered bicycle. This research also addressed the “attitude-behavior gap”, weather impacts, and goal-oriented prompts. The findings suggest that policy interventions should be designed to improve public transit features, especially the bicycle system, rather than only to constrain car use, and that tailored policies should be targeted to specific groups with different goal frames

State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks

Multiple faults in new energy vehicle batteries can be diagnosed using voltage. To find voltage fault information in advance and reduce battery safety risk, a state-partitioned voltage fault prognosis method based on the self-attention network is proposed. The voltage data are divided into three parts with typical characteristics according to the charging voltage curve trends under different charge states. Subsequently, a voltage prediction model based on the self-attention network is trained separately with each part of the data. The voltage fault prognosis is realized using the threshold method. The effectiveness of the method is verified using real operating data of electric vehicles (EVs). The effects of different batch sizes and window sizes on model training are analyzed, and the optimized hyperparameters are used to train the voltage prediction model. The average error of predicted voltage is less than 2 mV. Finally, the superiority and robustness of the method are verified

Low Purchase Willingness for Battery Electric Vehicles: Analysis and Simulation Based on the Fault Tree Model

Purchase intention is the key to popularizing battery electric vehicles (BEVs) and to developing the industry. This study combines classical theoretical and qualitative research, and applies fault tree analysis (FTA) methods to study factors that hinder BEV purchase, and identify the logical relationship between top fault events and basic events, by calculating minimal cut sets and minimal path sets. Activity based classification analysis was used to investigate the key basic event and key event combination (i.e., minimal cut sets) that hinders purchase intention, with the effectiveness and feasibility of the proposed method verified by Monte Carlo simulation. The results indicate (1) there were 26 minimal cut sets and 18 minimal path sets in the fault tree model, and the fault tree was defined by four key event combinations and five key basic events; and (2) by reducing key events’ failure probability, the probability of fault tree cumulative occurrence was reduced from 0.86021 to 0.57406 over 100,000 Monte Carlo simulations, i.e., the willingness to purchase BEVs was significantly increased. Thus, the proposed FTA method was feasible and effective for addressing low purchase intentions. Consequently, some policy implications are suggested

State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks

Multiple faults in new energy vehicle batteries can be diagnosed using voltage. To find voltage fault information in advance and reduce battery safety risk, a state-partitioned voltage fault prognosis method based on the self-attention network is proposed. The voltage data are divided into three parts with typical characteristics according to the charging voltage curve trends under different charge states. Subsequently, a voltage prediction model based on the self-attention network is trained separately with each part of the data. The voltage fault prognosis is realized using the threshold method. The effectiveness of the method is verified using real operating data of electric vehicles (EVs). The effects of different batch sizes and window sizes on model training are analyzed, and the optimized hyperparameters are used to train the voltage prediction model. The average error of predicted voltage is less than 2 mV. Finally, the superiority and robustness of the method are verified

Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the absolute error of SOC estimation is within 4% for the small dataset and within 2% for the large dataset. More importantly, the robustness and effectiveness of the proposed method are validated based on operational data from three different real-world electric vehicles, and the mean square error of SOC estimation does not exceed 0.006. This paper aims to provide guidance for the SOC estimation of real-world electric vehicles

Nested Optimization of Oil-Circulating Hydro-Pneumatic Energy Storage System for Hybrid Mining Trucks

In order to recover and utilize the potential energy of mining trucks efficiently, this paper proposes a nested optimization method of a novel energy storage system. By analyzing the multi-objective optimization problem of the oil-circulating hydro-pneumatic energy storage system, a nested optimization method based on the advanced adaptive Metamodel-based global optimization algorithm is carried out. Research shows that this method only requires a short time to solve the complex nonlinear hybrid optimization problem and achieves better results. The optimized energy storage system has higher system efficiency, energy density, and volume utilization rate, thus obtaining a smaller system volume and weight. Verified by the bench experiment of its powertrain, the hydro-pneumatic hybrid mining truck with the optimized energy storage system significantly reduces its fuel consumption and CO2 emission. Thus, it lays the foundation for the practical application of hydro-pneumatic hybrid mining trucks