RESUMEN
State of health is a critical state which evaluates the degradation level of batteries. However, it cannot be measured directly but requires estimation. While accurate state of health estimation has progressed markedly, the time- and resource-consuming degradation experiments to generate target battery labels hinder the development of state of health estimation methods. In this article, we design a deep-learning framework to enable the estimation of battery state of health in the absence of target battery labels. This framework integrates a swarm of deep neural networks equipped with domain adaptation to produce accurate estimation. We employ 65 commercial batteries from 5 different manufacturers to generate 71,588 samples for cross-validation. The validation results indicate that the proposed framework can ensure absolute errors of less than 3% for 89.4% of samples (less than 5% for 98.9% of samples), with a maximum absolute error of less than 8.87% in the absence of target labels. This work emphasizes the power of deep learning in precluding degradation experiments and highlights the promise of rapid development of battery management algorithms for new-generation batteries using only previous experimental data.
RESUMEN
BACKGROUND: Venous thromboembolism (VTE) is a life-threatening cardiovascular syndrome that characterized by the imbalance of hemostasis and thrombosis and the formation of thrombi in the blood vessels. The aim of this study was to elucidate the clinical impact of the PAI-1 4G/5G polymorphism in Chinese patients with VTE. METHODS: A total of 169 subjects (89 VTE, 10 hyperbilirubinemia, 10 hyperlipidemia and 60 healthy controls) were recruited at Peking Union Medical College Hospital. The accuracy of the TaqMan-MGB RT-PCR method for detecting F5 G1691A (FVL) and PAI-1 4G/5G polymorphisms was evaluated by using sequencing method as the gold standard. Besides, the association of the PAI-1 4G/5G polymorphism with susceptibility, treatment efficacy and recurrence status of VTE in Chinese population were explored. Eventually, the plasma PAI-1 antigen levels and PAI-1 4G/5G polymorphisms were determined on additional 64 subjects (32 VTE and 32 healthy controls) simultaneously. RESULTS: The TaqMan-MGB RT-PCR method was proven to be highly accurate in determining the FVL and PAI-1 4G/5G polymorphisms without interference from bilirubin and lipids in the samples. No obvious correlation of the PAI-1 4G/5G polymorphism with VTE was observed in our study by using five genetic models (allele, genotype, dominant, recessive and additive). Additionally, we also observed that individuals with the 4G/5G genotype had lower neutrophil counts and neutrophil-to-lymphocyte ratio (NLR) than the 5G/5G genotype. Furthermore, we found that the patients with the 5G/5G genotype were more likely to achieve complete recanalization compared to the 4G/4G genotype. In addition, individuals carrying the 5G/5G genotype were more likely to develop a recurrence-free status as compared to individuals with the 4G/4G or 4G/5G genotypes. PAI-1 antigen levels in the VTE group were significantly higher than those in the HC group. However, there was no significant difference in the antigen levels of PAI-1 among subjects carrying various genotypes in the VTE group or HC group. CONCLUSION: The PAI-1 4G/5G polymorphism has potential value in assessing the prognosis of Chinese patients with VTE. Our study has laid the foundation for the application of PAI-1 4G/5G polymorphism in the personalized management and monitoring of patients with VTE.
RESUMEN
Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter-spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking.
RESUMEN
Background: TNF-α has been reported to be closely associated with autoimmune inflammatory diseases. This study aims to investigate the role of TNF-α -308(rs1800629) G/A gene polymorphisms as well as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting the efficacy and safety of TNF inhibitors (TNFi) in patients with ankylosing spondylitis (AS), rheumatoid arthritis (RA), and psoriasis arthritis (PsA). Methods: A total of 515 subjects (181 AS, 144 RA, 48 PsA, 10 hyperbilirubinemia, 10 hyperlipidemia and 122 healthy control) were recruited in this study. The accuracy of RT-PCR methods for identifying individual TNF-α -308 genotypes was assessed using sequencing as the gold standard. Baseline NLR and PLR of patients with AS, RA and PsA and healthy controls (HC) were calculated and compared. Meanwhile, differences between responders and non-responders to TNFi treatment as well as between individuals with and without adverse effects (AE) among responders were compared. Results: The RT-PCR method is stable and reliable for TNF-α -308G/A gene polymorphism analysis, independent of sample status. The GG genotype was overwhelmingly represented, with relatively few GA genotype, whilst the AA genotype was not detected in this study. There was no observed association between TNF-α-308G/A polymorphism and susceptibility in AS, RA or PsA patients. Patients with AS, RA, and PsA had a higher NLR, compared to the HC group. Apart from PsA patients, AS and RA patients had a higher PLR, compared to the HC group. NLR was positively correlated with PLR. Furthermore, a lack of response was more frequently observed in AS and RA patients that carrying the GA genotype than the GG genotype. AS and RA patients with AE had higher NLR and PLR, compared with the non-AE group. Conclusion: Our study preliminarily shown that combining TNF-α -308G/A polymorphisms with NLR and PLR can predict the responsiveness and safety of anti-TNF therapy in patients with AS or RA.
RESUMEN
Lithium-ion batteries are commonly used as sources of power for electric vehicles (EVs). Battery safety is a major concern, due to a large number of accidents, for which short circuit has been considered as one of the main causes. Therefore, diagnosing and prognosticating short circuit are of great significance to improve EV safety. This work reviews the current state of the art about the diagnosis and prognosis of short circuit, covering the method and the key indicators. The findings provide important insights regarding how to improve the battery safety.
RESUMEN
This study focused on the potential therapeutic effect of Baicalin on collagen-induced arthritis (CIA) in mice and the underlying mechanisms. Baicalin (200 mg/kg) was administrated after the induction of CIA for 42 days. Therapeutic effects were evaluated by arthritic scores, X-rays and assessment of histopathological joint destruction. The production of TNF-α,IL-6,IL-17,IL-1ß were also measured. The percentage of Th17 cells in splenocytes were determined by flow cytometry analysis. Our results showed that Baicalin treatment attenuated the severity of arthritis of CIA mice and reduced the levels of several cytokines. Further mechanistic investigations revealed that Baicalin suppressed the expression and phosphorylation of JAK2 and STAT3 protein in splenocytes tissue. Moreover, the percentage of Th17 cells in splenocytes also downregulated by Baicalin. Our experiment indicate that CIA mice can be alleviated by Baicalin treatment via inhibition of JAK2-STAT3 signaling and regulation of Th17 cells in mice.
RESUMEN
The nickel-rich LiNi0.7Co0.15Mn0.15O2 material was sintered by Li source with the Ni0.7Co0.15Mn0.15(OH)2 precursor, which was prepared via hydrothermal treatment after coprecipitation. The intensity ratio of I(110)/I(108) obtained from X-ray diffraction patterns and high-resolution transmission electronmicroscopy confirm that the particles have enhanced growth of (110), (100), and (010) surface planes, which supply superior inherent Li(+) deintercalation/intercalation. The electrochemical measurement shows that the LiNi0.7Co0.15Mn0.15O2 material has high cycling stability and rate capability, along with fast charge and discharge ability. Li(+) diffusion coefficient at the oxidation peaks obtained by cyclic voltammogram measurement is as large as 10(-11) (cm(2) s(-1)) orders of magnitude, implying that the nickel-rich material has high Li(+) diffusion capability.
