A novel histopathological grading system based on tumour budding and cell nest size has recently been shown to outperform conventional (WHO‐based) grading algorithms in several tumour entities such as lung, oral, and oesophageal squamous cell carcinoma (SCC) in terms of prognostic patient stratification. Here, we tested the prognostic value of this innovative grading approach in two completely independent cohorts of SCC of the uterine cervix. To improve morphology‐based grading, we investigated tumour budding activity and cell nest size as well as several other histomorphological factors (e.g., keratinization, nuclear size, mitotic activity) in a test cohort (n = 125) and an independent validation cohort (n = 122) of cervical SCC. All parameters were correlated with clinicopathological factors and patient outcome. Small cell nest size and high tumour budding activity were strongly associated with a dismal patient prognosis (p < 0.001 for overall survival [OS], disease‐specific survival, and disease‐free survival; test cohort) in both cohorts of cervical SCC. A novel grading algorithm combining these two parameters proved to be a highly effective, stage‐independent prognosticator in both cohorts (OS: p < 0.001, test cohort; p = 0.001, validation cohort). In the test cohort, multivariate statistical analysis of the novel grade revealed that the hazard ratio (HR) for OS was 2.3 for G2 and 5.1 for G3 tumours compared to G1 neoplasms (p = 0.010). In the validation cohort, HR for OS was 3.0 for G2 and 7.2 for G3 tumours (p = 0.012).In conclusion, our novel grading algorithm incorporating cell nest size and tumour budding allows strongly prognostic histopathological grading of cervical SCC superior to WHO‐based grading. Therefore, our data can be regarded as a cross‐organ validation of previous results demonstrated for oesophageal, lung, and oral SCC. We suggest this grading algorithm as an additional morphology‐based parameter for the routine diagnostic assessment of this tumour entity.

Abstract. Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA, during a field campaign in May 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, which is somewhat larger than the enhancements due to urban emissions (<5 ppb downwind of the city). We found diffuse methane emissions of 73(±22) mol s−1, which is about 50 % of the urban total and 68 % higher than estimated from bottom-up methods, although it is somewhat smaller than estimates from studies using tower and aircraft observations. The measurement and model techniques developed here address many of the challenges present when quantifying urban greenhouse gas emissions and will help in the design of future measurement schemes in other cities.

Abstract. Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict, and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA during a field campaign in May of 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, somewhat larger than the enhancements due to urban emissions (

The COVID‐19 pandemic led to widespread reductions in mobility and induced observable changes in atmospheric emissions. Recent work has employed novel mobility data sets as a proxy for trace gas emissions from traffic by scaling CO2 emissions linearly with those near‐real‐time mobility data. Yet, there has been little work evaluating these emission numbers. Here, we systematically compare these mobility data sets to traffic data from local governments in seven diverse urban and national/state regions to characterize the magnitude of errors that result from using the mobility data. We observe differences in excess of 60% between these mobility data sets and local traffic data. We could not find a general functional relationship between the mobility data and traffic flow over all the regions and observe higher deviations from using such general relationships than the original data. Finally, we give an overview of the potential errors that come from estimating CO2 emissions using (mobility or traffic) activity data. Future work should be cautious while using these mobility metrics for emission estimates.

As the global economy is booming, and the industrialization and urbanization are being expedited, particulate matter 2.5 (PM2.5) turns out to be a major air pollutant jeopardizing public health. Numerous researchers are committed to employing various methods to address the problem of the nonlinear correlation between PM2.5 concentration and several factors to achieve more effective forecasting. However, a considerable space remains for the improvement of forecasting accuracy, and the problem of missing air pollution data on certain target areas also needs to be solved. Our research work is divided into two parts. First, this study presents a novel stacked ResNet-LSTM model to enhance prediction accuracy for PM2.5 concentration level forecast. As revealed from the experimental results, the proposed model outperforms other models such as boosting algorithms or general recurrent neural networks, and the advantage of feature extraction through residual network (ResNet) combined with a model stacking strategy is shown. Second, to solve the problem of insufficient air quality and meteorological data on some research areas, this study proposes the use of a correlation alignment (CORAL) method to carry out a prediction on the target area by aligning the second-order statistics between source area and target area. As indicated from the results, this model exhibits a considerable accuracy even in the absence of historical PM2.5 data in the target forecast area.