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Many researchers use life cycle assessment methodology to investigate the energy and environmental impacts of energy-saving and new energy vehicles. However, in the context of China, the life cycle energy-saving and emission-reduction effects of extended-range electric vehicles (EREVs), and the optimal applicable vehicle size and driving conditions for EREVs have been rarely studied. In this study, based on the life cycle assessment theory, the resource consumption, energy exhaustion, and environmental impact of EREVs were comprehensively analyzed. In addition, a differential evaluation model of ecological benefits was established for comparing EREVs with other vehicles with different power sources. Finally, scenario analysis was performed in terms of different vehicle sizes and driving conditions. The results have shown that EREV has great advantages in reducing mineral resource consumption and fossil energy consumption. The consumption of mineral resources of EREV is 14.68% lower than that of HEV, and the consumption of fossil energy is 34.72% lower than that of ICEV. In terms of environmental impact, EREV lies in the middle position. The scenario analysis has revealed that, for EREV in China, the optimal vehicle size is the passenger car and the optimal driving condition is the suburban condition. This work helps to understand the environmental performance of EREVs in China and may provide a decision-making reference for the government.Many studies have demonstrated the clinical utility and importance of epilepsy gene panel testing to confirm the specific aetiology of disease, enable appropriate therapeutic interventions, and inform accurate family counselling. Previously, SCN9A gene variants, in particular a c.1921A&gt;T p.(Asn641Tyr) substitution, have been identified as a likely autosomal dominant cause of febrile seizures/febrile seizures plus and other monogenic seizure phenotypes indistinguishable from those associated with SCN1A, leading to inclusion of SCN9A on epilepsy gene testing panels. Here we present serendipitous findings of genetic studies that identify the SCN9A c.1921A&gt;T p.(Asn641Tyr) variant at high frequency in the Amish community in the absence of such seizure phenotypes. Together with findings in UK Biobank these data refute an association of SCN9A with epilepsy, which has important clinical diagnostic implications.Lifeact is a short actin-binding peptide that is used to visualize filamentous actin (F-actin) structures in live eukaryotic cells using fluorescence microscopy. However, this popular probe has been shown to alter cellular morphology by affecting the structure of the cytoskeleton. The molecular basis for such artefacts is poorly understood. Here, we determined the high-resolution structure of the Lifeact-F-actin complex using electron cryo-microscopy (cryo-EM). [https://www.selleckchem.com/products/ono-7300243.html ONO-7300243] The structure reveals that Lifeact interacts with a hydrophobic binding pocket on F-actin and stretches over 2 adjacent actin subunits, stabilizing the DNase I-binding loop (D-loop) of actin in the closed conformation. Interestingly, the hydrophobic binding site is also used by actin-binding proteins, such as cofilin and myosin and actin-binding toxins, such as the hypervariable region of TccC3 (TccC3HVR) from Photorhabdus luminescens and ExoY from Pseudomonas aeruginosa. In vitro binding assays and activity measurements demonstrate that Lifeact indeed competes with these proteins, providing an explanation for the altering effects of Lifeact on cell morphology in vivo. Finally, we demonstrate that the affinity of Lifeact to F-actin can be increased by introducing mutations into the peptide, laying the foundation for designing improved actin probes for live cell imaging.Identification of bacterial virulence factors is critical for understanding disease pathogenesis, drug discovery and vaccine development. In this study we used two approaches to predict virulence factors of Burkholderia pseudomallei, the Gram-negative bacterium that causes melioidosis. B. pseudomallei is naturally antibiotic resistant and there are no clinically available melioidosis vaccines. To identify B. pseudomallei protein targets for drug discovery and vaccine development, we chose to search for substrates of the B. pseudomallei periplasmic disulfide bond forming protein A (DsbA). DsbA introduces disulfide bonds into extra-cytoplasmic proteins and is essential for virulence in many Gram-negative organism, including B. pseudomallei. The first approach to identify B. pseudomallei DsbA virulence factor substrates was a large-scale genomic analysis of 511 unique B. pseudomallei disease-associated strains. This yielded 4,496 core gene products, of which we hypothesise 263 are DsbA substrates. Manual curation and database screening of the 263 mature proteins yielded 81 associated with disease pathogenesis or virulence. These were screened for structural homologues to predict potential B-cell epitopes. In the second approach, we searched the B. pseudomallei genome for homologues of the more than 90 known DsbA substrates in other bacteria. Using this approach, we identified 15 putative B. pseudomallei DsbA virulence factor substrates, with two of these previously identified in the genomic approach, bringing the total number of putative DsbA virulence factor substrates to 94. The two putative B. pseudomallei virulence factors identified by both methods are homologues of PenI family β-lactamase and a molecular chaperone. These two proteins could serve as high priority targets for future B. pseudomallei virulence factor characterization.The evaluation of fat-free mass (FFM) in patients with Duchenne muscular dystrophy (DMD) is useful to investigate disease progression and therapeutic efficacy. This study aimed to validate the Bioelectrical impedance (BIA) method compared with the dual-energy X-ray absorptiometry (DXA) for estimating the %FFM in boys with DMD. This is a cross-sectional study performed with children and adolescents diagnosed with DMD. Resistance and reactance were measured with a BIA analyzer, from which eight predictive equations estimated the %FFM. The %FFM was also determined by DXA and its used as a reference method. Pearson correlation test, coefficient of determination, the root-mean-square error, the interclass correlation coefficient, and linear regression analysis were performed between %FFM values obtained by BIA and DXA. The agreement between these values was verified with the Bland-Altman plot analysis. Forty-six boys aged from 5 to 20 years were enrolled in the study. All the equations showed a correlation between the %FFM estimated by BIA and determined by DXA (p 0.