Highlights d Discovery of prognosis-associated proteins and pathways at early stage of LUAD d Proteomics revealed three subtypes related to clinical and molecular features d Identification of subtype-specific kinases and cancerassociated phosphoproteins d Identification of potential prognostic biomarkers and drug targets in LUAD

The main objective of this study is to investigate the formation and evolution mechanism of the regional haze in megacity Beijing by analyzing the process of a severe haze that occurred 20–27 September 2011. Mass concentration and size distribution of aerosol particles as well as aerosol optical properties were concurrently measured at the Beijing urban atmospheric environment monitoring station. Gaseous pollutants (SO₂, NO-NO₂-NO_x, O₃, CO) and meteorological parameters (wind speed, wind direction, and relative humidity) were simultaneously monitored. Meanwhile, aerosol spatial distribution and the height of planetary boundary layer (PBL) were retrieved from the signal of satellite and LIDAR (light detection and ranging). Concentrations of NO, NO₂, SO₂, O₃, and CO observed during 23–27 September had exceeded the national ambient air quality standards for residents. The mass concentration of PM_2.5 gradually accumulated during the measurement and reached at 220 μg m⁻³ on 26 September, and the corresponding atmospheric visibility was only 1.1 km. The daily averaged AOD in Beijing increased from ~ 0.16 at λ = 500 nm on 22 September and reached ~ 3.5 on 26 September. The key factors that affected the formation and evolution of this haze episode were stable anti-cyclone synoptic conditions at the surface, decreasing of the height of PBL, heavy pollution emissions from urban area, number and size evolution of aerosols, and hygroscopic growth for aerosol scattering. This case study may provide valuable information for the public to recognize the formation mechanism of the regional haze event over the megacity, which is also useful for the government to adopt scientific approach to forecast and eliminate the occurrence of regional haze in China

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BackgroundThe spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key “hallmarks of cancer”. However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.MethodsIn this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/.FindingsThe overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a “spatial map” of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.InterpretationThe analysis pipeline developed in this study could convert the pathology image into a “spatial map” of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.

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