Abstract. Gridded emission inventories are essential inputs for chemical transport models and climate models. Spatial proxies are applied to allocate emissions from regional totals to spatially resolved grids when the exact locations of emissions are absent, with additional uncertainties arising due to the spatial mismatch between the locations of emissions and spatial proxies. In this study, we investigate the impact of spatial proxies on the accuracy of gridded emission inventories at different spatial resolutions by comparing gridded emissions developed from different spatial proxies (proxy-based inventory) with a highly spatially disaggregated bottom-up emission inventory developed from the extensive use of locations of emitting facilities (bottom-up inventory) in Hebei Province, China. We find that proxy-based inventories are generally comparable to bottom-up inventories for grid sizes larger than 0.25° because spatial errors are largely diminished at coarse resolutions. However, for gridded emissions with finer resolutions, large positive biases in urban centers and negative biases in suburban and rural regions are identified in proxy-based inventories and are then propagated into significant biases in urban-scale chemical transport modeling. Compared to bottom-up inventories, the use of proxy-based emissions exhibits similar modeling results, with biases varying from 3 to 13 % when predicting surface concentrations of different pollutants at 36 km resolution and an additional 8–73 % at 4 km resolution. The resolution dependence of uncertainties in proxy-based gridded inventories can be explained by the decoupling of emission facility locations from spatial surrogates, especially because industry facilities tend to be located away from urban centers. This distance results in a divergence between emission distributions and the allocation of proxies on smaller grids. The decoupling effects are weakened when the grid size increases to cover both urban and rural regions. We conclude that proxy-based inventories are of sufficient quality to support regional and global models (larger than 0.25° in this case study); however, to support urban-scale models with accurate emission inputs, bottom-up inventories incorporating the exact locations of emitting facilities should be developed instead of proxy-based inventories.

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Myoblast proliferation following myotrauma is regulated by multiple factors including growth factors, signal pathways, transcription factors, and miRNAs. However, the molecular mechanisms underlying the orchestration of these regulatory factors remain unclear. Here we show that p38 signaling is required for miR-1/133a clusters transcription and both p38 activity and miR-1/133 expression are attenuated during the early stage of muscle regeneration in various animal models. Additionally, we show that both miR-1 and miR-133 reduce Cyclin D1 expression and repress myoblast proliferation by inducing G1 phase arrest. Furthermore, we demonstrate that miR-133 inhibits mitotic progression by targeting Sp1, which mediates Cyclin D1 transcription, while miR-1 suppresses G1/S phase transition by targeting Cyclin D1. Finally, we reveal that proproliferative FGF2, which is elevated during muscle regeneration, attenuates p38 signaling and miR-1/133 expression. Taken together, our results suggest that downregulation of p38-mediated miR-1/133 expression by FGF2 and subsequent upregulation of Sp1/Cyclin D1 contribute to the increased myoblast proliferation during the early stage of muscle regeneration.

Stochastic Maxwell equations with additive noise are a system of stochastic Hamiltonian partial differential equations intrinsically, possessing the stochastic multi-symplectic conservation law. It is shown that the averaged energy increases linearly with respect to the evolution of time and the flow of stochastic Maxwell equations with additive noise preserves the divergence in the sense of expectation. Moreover, we propose three novel stochastic multi-symplectic methods to discretize stochastic Maxwell equations in order to investigate the preservation of these properties numerically. We make theoretical discussions and comparisons on all of the three methods to observe that all of them preserve the corresponding discrete version of the averaged divergence. Meanwhile, we obtain the corresponding dissipative property of the discrete averaged energy satisfied by each method. Especially, the evolution rates of the averaged energies for all of the three methods are derived which are in accordance with the continuous case. Numerical experiments are performed to verify our theoretical results.