Pathogenesis of Tobacco-Associated Lung Adenocarcinoma Is Closely Coupled with Changes in the Gut and Lung Microbiomes

Microbial dysbiosis has emerged as a modulator of oncogenesis and response to therapy, particularly in lung cancer. Here, we investigate the evolution of the gut and lung microbiomes following exposure to a tobacco carcinogen. We performed 16S rRNA-Seq of fecal and lung samples collected prior to and at several timepoints following (nicotine-specific nitrosamine ketone/NNK) exposure in Gprc5a^−/− mice that were previously shown to exhibit accelerated lung adenocarcinoma (LUAD) development following NNK exposure. We found significant progressive changes in human-relevant gut and lung microbiome members (e.g., Odoribacter, Alistipes, Akkermansia, and Ruminococus) that are closely associated with the phenotypic development of LUAD and immunotherapeutic response in human lung cancer patients. These changes were associated with decreased short-chain fatty acids (propionic acid and butyric acid) following exposure to NNK. We next sought to study the impact of Lcn2 expression, a bacterial growth inhibitor, given our previous findings on its protective role in LUAD development. Indeed, we found that the loss of Lcn2 was associated with widespread gut and lung microbiome changes at all timepoints, distinct from those observed in our Gprc5a^−/− mouse model, including a decrease in abundance and diversity. Our overall findings apprise novel cues implicating microbial phenotypes in the development of tobacco-associated LUAD.     

Simulation and Optimization of Back Surface Field for Efficient HIT Solar Cells

Silicon - We investigate an appropriate material for the optimisation of Back Surface Field (BSF) layer in Heterojunction with Intrinsic thin layer (HIT) solar cells:...     

Historical document image analysis using controlled data for pre-training

Using neural networks for semantic labeling has become a dominant technique for layout analysis of historical document images. However, to train or fine-tune appropriate models, large labeled datasets are needed. This paper addresses the case when only limited labeled data are available and promotes a novel approach using so-called controlled data to pre-train the networks. Two different strategies are proposed: The first addresses the real labeling task by using artificial data; the second uses real data to pre-train the networks with a pretext task. To assess these strategies, a large set of experiments has been carried out on a text line detection and classification task using different variants of U-Net. The observations, obtained from two different datasets, show that globally the approach reduces the training time while offering similar or better performance. Furthermore, the effect is bigger on lightweight network architectures.