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BACKGROUND: A recent study suggests that systemic hypoxemia in adult male mice can induce cardiac myocytes to proliferate. The goal of the present experiments was to confirm these results, provide new insights on the mechanisms that induce adult cardiomyocyte cell cycle reentry, and to determine if hypoxemia also induces cardiomyocyte proliferation in female mice. METHODS: EdU-containing mini pumps were implanted in 3-month-old, male and female C57BL/6 mice. Mice were placed in a hypoxia chamber, and the oxygen was lowered by 1% every day for 14 days to reach 7% oxygen. The animals remained in 7% oxygen for 2 weeks before terminal studies. Myocyte proliferation was also studied with a mosaic analysis with double markers mouse model. RESULTS: Hypoxia induced cardiac hypertrophy in both left ventricular (LV) and right ventricular (RV) myocytes, with LV myocytes lengthening and RV myocytes widening and lengthening. Hypoxia induced an increase (0.01±0.01% in normoxia to 0.11±0.09% in hypoxia) in the number of EdU+ RV cardiomyocytes, with no effect on LV myocytes in male C57BL/6 mice. Similar results were observed in female mice. Furthermore, in mosaic analysis with double markers mice, hypoxia induced a significant increase in RV myocyte proliferation (0.03±0.03% in normoxia to 0.32±0.15% in hypoxia of RFP+ myocytes), with no significant change in LV myocyte proliferation. RNA sequencing showed upregulation of mitotic cell cycle genes and a downregulation of Cullin genes, which promote the G1 to S phase transition in hypoxic mice. There was significant proliferation of nonmyocytes and mild cardiac fibrosis in hypoxic mice that did not disrupt cardiac function. Male and female mice exhibited similar gene expression following hypoxia. CONCLUSIONS: Systemic hypoxia induces a global hypertrophic stress response that was associated with increased RV proliferation, and while LV myocytes did not show increased proliferation, our results minimally confirm previous reports that hypoxia can induce cardiomyocyte cell cycle activity in vivo.

This study aims to develop a neural network model to predict work zone capacity including various uncertainties stemming from traffic and operational conditions. The neural network model is formulated in terms of the number of total lanes, number of open lanes, heavy vehicle percentage, work intensity, and work duration. The data used in this paper are obtained from previous studies published in open literature. To capture the uncertainty of work zone capacity, this paper provides two recent methods that enable neural network models to generate prediction intervals which are determined by mean work zone capacity and prediction standard error. The research first builds a Bayesian neural network model with the application of black-box variational inference (BBVI) technique. The second model is based on a regular artificial neural network with an application of the recently proposed Monte-Carlo dropout technique. Both of the neural network models construct prediction intervals under various confidence levels and provide the coverage rates of the actual work zone capacities. The statistical accuracy (MAPE, MAE, MSE, and RMSE) of the models is then compared with traditional estimation methods in predicted mean work zone capacity. BBVI produces better statistical results than the other three models. Both of the models provide predicted work zone capacity distribution and prediction intervals, whereas traditional models only provide a single estimate.

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In many large-scale evacuations, public agencies often have limited resources to evacuate all citizens, especially vulnerable populations such as the elderly and disabled people, and the demand for additional transportation means for evacuation can be high. The recent development of ride-sourcing companies can be leveraged in evacuations as an additional and important resource in future evacuation planning. In contrast to public transit, the availability of ride-sourcing drivers is highly dependent on the price, since surge pricing will occur when the demand is high and the supply is low. The key challenge is thus to find the balance between evacuation demand and driver supply. Based on the two-sided market theory, we propose mathematical modeling and analysis strategies that can help balance demand and supply through a pricing mechanism designed for ride-sourcing services in evacuation. A subsidy is considered in the model such that lower-income and vulnerable individuals could benefit from ride-sourcing services. A hypothetical hurricane evacuation scenario in New York City in the case study showed the feasibility of the proposed method and the applicability of subsidies for ride-sourcing services in evacuation. The methodology and results given in this research can provide useful insights for modeling on-demand ride-sourcing for future evacuation planning.