Statistical delay distribution analysis on high-speed railway trains

Published by SpringerOpen, Heidelber

Black holes in Einstein-dilaton-Massive gravity

In this paper, we focus on the Einstein-dilaton-Massive (EdM) gravity including the coupling of dilaton scalar field to massive graviton terms, and then derive static and spherically symmetric solutions of dilatonic black holes in four dimensional spacetime. We discover that the dilatonic black hole could possess two horizons (event and cosmological), extreme (Nariai) and naked singularity black holes for the suitably fixed parameters. Moreover, the dilatonic black hole solutions are neither asymptotic flat nor (A)dS in the appearance of coupling of the dilaton field. In addition, we investigate thermodynamic properties of these dilatonic black holes, and check the corresponding first law of black hole thermodynamics. Extending to the EdM gravity in high dimensions, we further obtain the dilatonic black hole solutions in ($d+1$) dimensional spacetime.Comment: 17 pages, 6 figure

Whole-genome shotgun sequencing unravels the influence of environmental microbial co-infections on the treatment efficacy for severe pediatric infectious diseases

BackgroundThe microbiome plays a pivotal role in mediating immune deviation during the development of early-life viral infections. Recurrent infections in children are considered a risk factor for disease development. This study delves into the metagenomics of the microbiome in children suffering from severe infections, seeking to identify potential sources of these infections.AimsThe aim of this study was to identify the specific microorganisms and factors that significantly influence the treatment duration in patients suffering from severe infections. We sought to understand how these microbial communities and other variables may affect the treatment duration and the use of antibiotics of these patients with severe infections.MethodWhole-genome shotgun sequencing was conducted on samples collected from children aged 0–14 years with severe infections, admitted to the Pediatrics Department of Xiamen First Hospital. The Kraken2 algorithm was used for taxonomic identification from sequence reads, and linear mixed models were employed to identify significant microorganisms influencing treatment duration. Colwellia, Cryptococcus, and Citrobacter were found to significantly correlate with the duration of clinical treatment. Further analysis using propensity score matching (PSM) and rank-sum test identified clinical indicators significantly associated with the presence of these microorganisms.ResultsUsing a linear mixed model after removed the outliers, we identified that the abundance of Colwellia, Cryptococcus, and Citrobacter significantly influences the treatment duration. The presence of these microorganisms is associated with a longer treatment duration for patients. Furthermore, these microorganisms were found to impact various clinical measures. Notably, an increase in hospitalization durations and medication costs was observed in patients with these microorganisms. In patients with Colwellia, Cryptococcus, and Citrobacter, we discover significant differences in platelets levels. We also find that in patients with Cryptococcus, white blood cells, hemoglobin, and neutrophils levels are lower.ConclusionThese findings suggest that Colwellia, Cryptococcus, and Citrobacter, particularly Cryptococcus, could potentially contribute to the severity of infections observed in this cohort, possibly as co-infections. These microorganisms warrant further investigation into their pathogenic roles and mechanisms of action, as their presence in combination with disease-causing organisms may have a synergistic effect on disease severity. Understanding the interplay between these microorganisms and pathogenic agents could provide valuable insights into the complex nature of severe pediatric infections and guide the development of targeted therapeutic strategies

Simulation and economic analysis of an innovative indoor solar cooking system with energy storage

Solar energy technology and energy storage technology are promising to make a contribution to current energy and global climate issue. The energy demand of daily cooking is enormous, and conventional cooking methods use gas or electricity with large carbon emissions. This paper proposes an innovative solar cooking system (SCS) integrated with rock-bed thermocline storage. Thermal oils transfer heat from the collectors to the rocks in the charging process and release heat in cooktop unit for cooking. The energy consumption of a household is first assessed by a reasonable hypothesis. Mathematical models and simulation models are then established to analyze the heat transfer performance of the cooktop unit and the annual running performance of the SCS. The rock-bed thermocline storage, single-tank thermocline storage and two-tank storage are compared. The simulation results indicate that the rock-bed thermocline storage unit employed to SCS will enhance the annual running performance and acquire the minimum initial investment cost. The economic analysis shows that the lowest levelized cost of cooking energy (LCOC) of the SCS is 0.3884 $/kWh, while the corresponding levelized cost of cooking a meal (LCCM) is 0.953$/Meal and the solar fraction (SF) is 71%. Compared to the electrical and natural gas cooker, the SCS saves 1.75 tons and 0.52 tons of carbon emissions annually, respectively

Dietary alpha-ketoglutarate promotes beige adipogenesis and prevents obesity in middle-aged mice

Aging usually involves the progressive development of certain illnesses, including diabetes and obesity. Due to incapacity to form new white adipocytes, adipose expansion in aged mice primarily depends on adipocyte hypertrophy, which induces metabolic dysfunction. On the other hand, brown adipose tissue burns fatty acids, preventing ectopic lipid accumulation and metabolic diseases. However, the capacity of brown/beige adipogenesis declines inevitably during the aging process. Previously, we reported that DNA demethylation in the Prdm16 promoter is required for beige adipogenesis. DNA methylation is mediated by ten-eleven family proteins (TET) using alpha-ketoglutarate (AKG) as a cofactor. Here, we demonstrated that the circulatory AKG concentration was reduced in middle-aged mice (10-month-old) compared with young mice (2-month-old). Through AKG administration replenishing the AKG pool, aged mice were associated with the lower body weight gain and fat mass, and improved glucose tolerance after challenged with high-fat diet (HFD). These metabolic changes are accompanied by increased expression of brown adipose genes and proteins in inguinal adipose tissue. Cold-induced brown/beige adipogenesis was impeded in HFD mice, whereas AKG rescued the impairment of beige adipocyte functionality in middle-aged mice. Besides, AKG administration up-regulated Prdm16 expression, which was correlated with an increase of DNA demethylation in the Prdm16 promoter. In summary, AKG supplementation promotes beige adipogenesis and alleviates HFD-induced obesity in middle-aged mice, which is associated with enhanced DNA demethylation of the Prdm16 gene

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas