Lung adenocarcinoma promotion by air pollutants

A complete understanding of how exposure to environmental substances promotes cancer formation is lacking. More than 70 years ago, tumorigenesis was proposed to occur in a two-step process: an initiating step that induces mutations in healthy cells, followed by a promoter step that triggers cancer development1. Here we propose that environmental particulate matter measuring ≤2.5 μm (PM2.5), known to be associated with lung cancer risk, promotes lung cancer by acting on cells that harbour pre-existing oncogenic mutations in healthy lung tissue. Focusing on EGFR-driven lung cancer, which is more common in never-smokers or light smokers, we found a significant association between PM2.5 levels and the incidence of lung cancer for 32,957 EGFR-driven lung cancer cases in four within-country cohorts. Functional mouse models revealed that air pollutants cause an influx of macrophages into the lung and release of interleukin-1β. This process results in a progenitor-like cell state within EGFR mutant lung alveolar type II epithelial cells that fuels tumorigenesis. Ultradeep mutational profiling of histologically normal lung tissue from 295 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 53% of healthy tissue samples, respectively. These findings collectively support a tumour-promoting role for PM2.5 air pollutants and provide impetus for public health policy initiatives to address air pollution to reduce disease burden

Residual Images Remove Illumination Artifacts!

Abstract. Real-world image sequences (e.g., recorded for vision-based driver assistance) are typically degraded by various types of noise, changes in lighting, out-of-focus lenses, differing exposures, and so forth. In past studies, illumination effects have been proven to cause the most common problems in correspondence algorithms. We address this problem using the concept of residuals, which is the difference between an image and a smoothed version of itself. In this paper, we conduct a study identifying that the residual images contain the important information in an image. We go on to show that they remove illumination artifacts using a mixture of synthetic and real-life images. This effect is highlighted more drastically when the illumination and exposure of the corresponding images is not the same.

A methodology for evaluating illumination artifact removal for corresponding images

Abstract. Robust stereo and optical flow disparity matching is essential for computer vision applications with varying illumination conditions. Most robust disparity matching algorithms rely on computationally expensive normalized variants of the brightness constancy assumption to compute the matching criterion. In this paper, we reinvestigate the removal of global and large area illumination artifacts, such as vignetting, camera gain, and shading reflections, by directly modifying the input images. We show that this significantly reduces violations of the brightness constancy assumption, while maintaining the information content in the images. In particular, we define metrics and perform a methodical evaluation to firstly identify the loss of information in the images, and secondly determine the reduction of brightness constancy violations. Thirdly, we experimentally validate that modifying the input images yields robustness against illumination artifacts for optical flow disparity matching.

Soft-switching adaptive technique of impulsive noise removal in color images

Abstract. In this paper a novel class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter family is based on the kernel function which regulates the noise suppression properties of the proposed filtering scheme. The comparison of the new filtering method with standard techniques used for impulsive noise removal indicates superior noise removal capabilities and excellent structure preserving properties.