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Abstract Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time‐specific ML models based on 7‐day and 30‐day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non‐negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time.

Abstract Aims/Introduction Serum amyloid A (SAA) is an acute phase reactive protein that plays a vital role in the early diagnosis, risk prediction, efficacy observation and prognosis evaluation of infectious diseases. This study aimed to assess the association between SAA levels and the prognosis of patients with coronavirus disease 2019 (COVID‐19) and diabetes. Materials and Methods We carried out this retrospective cohort study from March 2022 to May 2022. The population was stratified by tertiles of SAA levels: low (36 mg/L). The primary outcome was whether the patient developed severe COVID‐19, and secondary outcomes included the need for invasive mechanical ventilation and length of hospital stay. Logistic regression analyses were carried out to identify risk factors affecting the prognosis of patients with COVID‐19 and diabetes. Results We analyzed 910 diabetes patients with COVID‐19. The median age of the patients was 69 years, and 52.3% were men. As SAA levels increased, the proportion of severe COVID‐19 (6.3% vs 7.3% vs 22.8%, P 36 mg/L further increased the odds ratio to 4.423 (P 36 mg/L remained an independent risk factor for the development of severe COVID‐19 (adjusted odds ratio 3.038, P

Abstract Renal fibrosis is a common characteristic of various chronic kidney diseases (CKDs) driving the loss of renal function. During this pathological process, persistent injury to renal tubular epithelial cells and activation of fibroblasts chiefly determine the extent of renal fibrosis. In this study, the role of tumor protein 53 regulating kinase (TP53RK) in the pathogenesis of renal fibrosis and its underlying mechanisms is investigated. TP53RK is upregulated in fibrotic human and animal kidneys with a positive correlation to kidney dysfunction and fibrotic markers. Interestingly, specific deletion of TP53RK either in renal tubule or in fibroblasts in mice can mitigate renal fibrosis in CKD models. Mechanistic investigations reveal that TP53RK phosphorylates baculoviral IAP repeat containing 5 (Birc5) and facilitates its nuclear translocation; enhanced Birc5 displays a profibrotic effect possibly via activating PI3K/Akt and MAPK pathways. Moreover, pharmacologically inhibiting TP53RK and Birc5 using fusidic acid (an FDA‐approved antibiotic) and YM‐155(currently in clinical phase 2 trials) respectively both ameliorate kidney fibrosis. These findings demonstrate that activated TP53RK/Birc5 signaling in renal tubular cells and fibroblasts alters cellular phenotypes and drives CKD progression. A genetic or pharmacological blockade of this axis serves as a potential strategy for treating CKDs.

Abstract Bile acid metabolites have been increasingly recognized as pleiotropic signaling molecules that regulate cardiovascular functions, but their role in mesenchymal stromal cells (MSC)‐based therapy has never been investigated. It is found that overexpression of farnesoid X receptor (FXR), a main receptor for bile acids, improves the retention and cardioprotection of adipose tissue‐derived MSC (ADSC) administered by intramyocardial injection in mice with myocardial infarction (MI), which shows enhanced antiapoptotic, proangiogenic, and antifibrotic effects. RNA sequencing, LC‐MS/MS, and loss‐of‐function studies reveal that FXR overexpression promotes ADSC paracrine angiogenesis via Angptl4. FXR overexpression improves ADSC survival in vivo but fails in vitro. By performing bile acid‐targeted metabolomics using ischemic heart tissue, 19 bile acids are identified. Among them, cholic acid and deoxycholic acid significantly increase Angptl4 secretion from ADSC overexpressing FXR and further improve their proangiogenic capability. Moreover, ADSC overexpressing FXR shows significantly lower apoptosis by upregulating Nqo‐1 expression only in the presence of FXR ligands. Retinoid X receptor α is identified as a coactivator of FXR. It is first demonstrated that there is a bile acid pool in the myocardial microenvironment. Targeting the bile acid‐FXR axis may be a novel strategy for improving the curative effect of MSC‐based therapy for MI.

Optimizing to increase the utilization ratio of regenerative braking energy reduces energy consumption, and can be done without increasing the deviation of train running time in one circle. The latter entails that the train timetable is upheld, which guarantees that the demand for passenger transport services is met and the quality of services in the urban rail transit system is maintained. This study proposes a multi-objective optimization model for urban railways with timetable optimization to minimize the total energy consumption of trains while maximizing the quality of service. To this end, we apply the principles and ideas of calculus to reduce the power of the velocity in the train energy consumption model. This greatly simplifies the complexity of the optimization model. Then, considering the conflicting requirements of decision-makers, weight factors are added to the objective functions to reflect decision-makers’ preferences for energy-saving and the quality of service. We adopt the nondominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model. A practical case study of the Yizhuang urban railway line in Beijing is conducted to verify the effectiveness of the proposed model and evaluate the advantages of the optimal energy saving timetable (OEST) in comparison to the optimal quality of service timetable (OQOST). The results showed that the OEST reduced total energy consumption by 8.72% but increased the deviation of trains running time in one circle by 728 s. The total energy consumption was reduced by 6.09%, but there was no increase in the deviation of train running time in one circle with the OQOST.

In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.