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N6-methyladenosine (m6A) RNA modification is a fundamental determinant of mRNA metabolism, but its role in innate immunity-driven non-alcoholic fatty liver disease (NAFLD) and obesity is not known. Here, we show that myeloid lineage-restricted deletion of the m6A "writer" protein Methyltransferase Like 3 (METTL3) prevents age-related and diet-induced development of NAFLD and obesity in mice with improved inflammatory and metabolic phenotypes. Mechanistically, loss of METTL3 results in the differential expression of multiple mRNA transcripts marked with m6A, with a notable increase of DNA Damage Inducible Transcript 4 (DDIT4) mRNA level. In METTL3-deficient macrophages, there is a significant downregulation of mammalian target of rapamycin (mTOR) and nuclear factor κB (NF-κB) pathway activity in response to cellular stress and cytokine stimulation, which can be restored by knockdown of DDIT4. Taken together, our findings identify the contribution of METTL3-mediated m6A modification of Ddit4 mRNA to macrophage metabolic reprogramming in NAFLD and obesity. [Display omitted] • Loss of METTL3 in myeloid cells prevents obesity and NAFLD in mice • METTL3 regulates Ddit4 mRNA stability via m6A RNA modification • METTL3 deficiency leads to reduced mTOR and NF-κB pathway activity upon cellular stress • Induction of DDIT4 modulates metabolic adaptation to macrophage effector function Myeloid deletion of METTL3 prevents age-related and diet-induced obesity and NAFLD in mice. Qin et al. reveal the contribution of METTL3 to maintaining macrophage homeostasis during NAFLD progression. METTL3 modulates metabolic adaptation of macrophage function in obesity and NAFLD via m6A-decorated DDIT4 mRNA degradation. [ABSTRACT FROM AUTHOR]

Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. [ABSTRACT FROM AUTHOR]

Ethnopharmacology relevance Bufei Yishen formula (BYF), a Traditional Chinese Medicine (TCM), has been extensively applied in clinical treatment of chronic obstructive pulmonary disease (COPD) and provides an effective treatment strategy for the syndrome of lung-kidney qi deficiency in COPD patients. Here, we investigated its anti-COPD mechanism in COPD rats in relation to the balance between T helper (Th) 17 cells and regulatory T (Treg) cells. Methods Rat model of cigarette smoke- and bacterial infection-induced COPD was established, and orally treated with BYF for 12 consecutive weeks. Then, the rats were sacrificed, their lung tissues were removed for histological analysis, and spleens and mesenteric lymph nodes (MLNs) were collected to evaluate the Th17 and Treg cells. Results Oral treatment of BYF markedly suppressed the disease progression and alleviated the pathological changes of COPD. It also decreased the bronchoalveolar lavage fluid (BALF) levels of pro-inflammatory cytokines, including IL-1β, IL-6, TNF-α and Th17-related IL-17A, and induced a significant increase in Treg-related IL-10. Furthermore, BYF treatment obviously decreased the proportion of CD4 + RORγt + T (Th17) cell and increased the proportion of CD4 + CD25 + Foxp3 + T (Treg) cell, leading to restore the Th17/Treg balance. BYF treated groups also decreased RORγt and increased Foxp3 expression in the spleens and MLNs. BYF further inhibited the phosphorylation of signal transducer and activator of transcription-3 (STAT3) and boosted the phosphorylation of STAT5, that were critical transcription factors for TH17 and Treg differentiation. Conclusion these results demonstrated that BYF exerted its anti-COPD efficacy by restoring Th17/Treg balance via reciprocally modulating the activities of STAT3 and STAT5 in COPD rats, which may help to elucidate the underlying immunomodulatory mode of BYF on COPD treatment. [ABSTRACT FROM AUTHOR]

For secure and reliable operation of lithium-ion batteries in electric vehicles, diagnosis of the battery degradation is essential. This can be achieved by monitoring the increase of the internal resistance of the battery cells over the whole lifetime of the battery. In this paper, a method to estimate state of health (SoH) is presented through the established linear relationship between ohmic internal resistance and capacity fade. Firstly, the Thevenin model and the recursive least squares (RLS) algorithm are applied to simulate battery dynamic characteristics and identify model parameters, respectively. Secondly, based on the established linear relationship between ohmic internal resistance and capacity fade, both ohmic internal resistances at the start and the end of the battery’s lifetime are estimated by only two random discharge cycles at different aging stages. Finally, an online SoH estimator is formulated and applied to estimate the SoH of a battery’s remaining cycles. In addition, a series of experiments were carried out based on dynamic loading to verify the proposed method. The SoH estimates indicate that the evaluated maximum SoH errors are within ±4%. The proposed SoH estimation method is consistent with the measurement data of the battery and shows good results with very low computational effort. [ABSTRACT FROM AUTHOR]

In this study a grey extended Kalman filter and a novel open-circuit voltage model for the estimation of the state of charge of lithium-ion batteries are presented. To eliminate the influence of truncation error, this study utilizes a grey prediction model to deal with the state prediction problem. In order to further improve the accuracy of state of charge estimation, a novel open-circuit voltage model based on cubic-Hermite interpolation is also proposed to update the state estimate. Moreover, the accuracy of the proposed open-circuit voltage model is verified in terms of the following two aspects: capacity estimation and state of charge estimation. The accuracy and convergence of the grey extended Kalman filter is analyzed for different types of dynamic loading conditions, including the Urban Dynamometer Driving Schedule and the New European Driving Cycle. The experimental results show that the proposed approach offers good accuracy for the estimation of the state of charge. The experimental results show good agreement with the estimation results, and the proposed method can effectively improve the accuracy of extended Kalman filter. [ABSTRACT FROM AUTHOR]