Spindle component 25 predicts the prognosis and the immunotherapy response of cancers: a pan-cancer analysis.

Spindle component 25 (SPC25) is one of the four proteins that make up the nuclear division cycle 80 (NDC80) complex, the other three components being Ndc80p, Nuf2p, and spindle component 24. Deregulation of the components of this complex can lead to uncontrolled proliferation and reduced apoptosis. However, the prognostic and immunotherapeutic value of SPC25 in pan-cancer remains unclear. Data from the UCSC Xena, TIMER2.0, and TCGA were analyzed to investigate the overall differential expression of SPC25 across multiple cancer types. The survival prognosis, clinical features, and genetic changes of SPC25 were also evaluated. Finally, the relationship between SPC25 and immunotherapy response was further explored through Gene Set Enrichment Analysis, tumor microenvironment, and immune cell infiltration. The transcription and protein expression of SPC25 were significantly increased in most cancer types and had prognostic value for the survival of certain cancer patients such as ACC, CESC, KIRC, KIRP, LIHC, LUAD, MESO, STAD, THYM, and UCEC. In some cancer types, SPC25 expression was also markedly correlated with the TMB, MSI, and clinical characteristics. Gene Set Enrichment Analysis showed that SPC25 was significantly associated with immune-related pathways. In addition, it was also confirmed that the expression level of SPC25 was strongly correlated with immune cell infiltration, immune checkpoint genes, immune regulatory genes, Ferroptosis-related genes, Cuproptosis-related genes, and lactate metabolism-related genes. This study comprehensively explored the potential value of SPC25 as a prognostic and immunotherapeutic marker for pan-cancer, providing new direction and evidence for cancer therapy.

The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18.

China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013-18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM2.5 concentration slightly lower by 0.6-2.2 µg m-3 and 1.4-6.0 µg m-3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014-15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China.

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.

Nonlinear response of SIA to emission changes and chemical processes over eastern and central China during a heavy haze month.

This study used a chemical transport model to investigate the response of secondary inorganic aerosols (SIA) to chemical processes and its precursor emissions over northern and southern city-clusters of China in January 2014. Unexpectedly, SIA concentrations with low levels of precursor emissions were much higher over the southern regions than those over the northern region with high levels of precursor emissions, based on ground observations and high-precision simulations. The sensitivity analysis of chemical processes suggests that the gas-phase chemistry was a critical factor determining the SIA pattern, especially the higher efficiency of nitrogen conversion to nitrate in southern cities controlled by favorable meteorological elements than that in northern city. However, the heterogeneous process led to the decrease of SIA in southern regions by 3% to 36% and the increasing of SIA in NCP by 26.9%, mainly attributing to the impact on nitrate. The reason was that sulfate enhancement by the heterogeneous reactions can compete ammonia (NH3) and the excessive nitric acid converted into nitrogen oxide (NOx), leading to nitrate decrease in southern regions under NH3-deficient regimes. Moreover, through sensitivity experiments of precursor emission reduction by 20%, NH3 control was found to be the most effective for reducing SIA concentrations comparing to sulfur dioxide (SO2) and NOx reduction and a more remarkable decrease of SIA was in southern regions by 10% to 15% than that in northern region by 6.7%. The effect of the synergy control of precursors emission varied in different city-clusters, inferring that the control strategy aimed at improving air quality should be implemented based on specific characteristics of precursors emission in different regions of China.

Improving PM2.5 Forecasts in China Using an Initial Error Transport Model.

Efforts of using data assimilation to improve PM2.5 forecasts have been hindered by the limited number of species and incomplete vertical coverage in the observations. The common practice of initializing a chemical transport model (CTM) with assimilated initial conditions (ICs) may lead to model imbalances, which could confine the impacts of assimilated ICs within a day. To address this challenge, we introduce an initial error transport model (IETM) approach to improving PM2.5 forecasts. The model describes the transport of initial errors by advection, diffusion, and decay processes and calculates the impacts of assimilated ICs separately from the CTM. The CTM forecasts with unassimilated ICs are then corrected by the IETM output. We implement our method to improve PM2.5 forecasts over central and eastern China. The reduced root-mean-square errors for 1-, 2-, 3-, and 4-day forecasts during January 2018 were 51.2, 27.0, 16.4, and 9.4 µg m-3, respectively, which are 3.2, 6.9, 8.6, and 10.4 times those by the CTM forecasts with assimilated ICs. More pronounced improvements are found for highly reactive PM2.5 components. These and similar results for July 2017 suggest that our method can enhance and extend the impacts of the assimilated data without being affected by the imbalance issue.

Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter.

Ammonia (NH3) emission inventories are an essential input in chemical transport models and are helpful for policy-makers to refine mitigation strategies. However, current estimates of Chinese NH3 emissions still have large uncertainties. In this study, an improved inversion estimation of NH3 emissions in China has been made using an ensemble Kalman filter and the Nested Air Quality Prediction Modeling System. By first assimilating the surface NH3 observations from the Ammonia Monitoring Network in China at a high resolution of 15 km, our inversion results have provided new insights into the spatial and temporal patterns of Chinese NH3 emissions. More enhanced NH3 emission hotspots, likely associated with industrial or agricultural sources, were captured in northwest China, where the a posteriori NH3 emissions were more than twice the a priori emissions. Monthly variations of NH3 emissions were optimized in different regions of China and exhibited a more distinct seasonality, with the emissions in summer being twice those in winter. The inversion results were well-validated by several independent datasets that traced gaseous NH3 and related atmospheric processes. These findings highlighted that the improved inversion estimation can be used to advance our understanding of NH3 emissions in China and their environmental impacts.

Source tagging modeling study of heavy haze episodes under complex regional transport processes over Wuhan megacity, Central China.

Wuhan as a megacity of Central China was suffering from severe particulate matter pollution according to previous observation studies, however, the mechanism behind the pollution formation especially the impact of regional chemical transport is still unclear. This study, carried out on the Nested Air Quality Prediction Modeling System (NAQPMS) coupled with an on-line source-tagging module, explores different roles regional transport had in two strong haze episodes over Wuhan in October 2014 and quantitatively assesses the contributions from local and regional sources to PM2.5 concentration. Validation of predictions based on observations shows modeling system good skills in reproducing key meteorological and chemical features. The first short-time haze episode occurred on 12 October under strong northerly winds, with a hourly PM2.5 peak of 180 µg m-3, and was found to be caused primarily by the long-range transport from the northern regions, which contributed 60.6% of the episode's PM2.5 concentration (versus a total of 32.7% from sources in and near Wuhan). The second episode lasted from the 15-20 October under stable regional large-scale synoptic conditions and weak winds, and had an hourly PM2.5 peak of 231.0 µg m-3. In this episode, both the long-distance transport from far regions and short-range transport from the Wuhan-cluster were the primary causes of the haze episode and account for 24.8% and 29.2% of the PM2.5 concentration respectively. Therefore, regional transport acts as a crucial driver of haze pollution over Wuhan through not only long-range transfer of pollutants, but also short-range aerosol movement under specific meteorological conditions. The present findings highlight the important role of regional transport in urban haze formation and indicate that the joint control of multi city-clusters are needed to reduce the particulate pollution level in Wuhan.