Alveolus analysis: a web browser-based tool to analyze lung intravital microscopy.

BACKGROUND: Acute lung injury and the acute respiratory distress syndrome are characterized by pulmonary inflammation, reduced endothelial barrier integrity and filling of the alveolar space with protein rich edema fluid and infiltrating leukocytes. Animal models are critical to uncovering the pathologic mechanisms of this devastating syndrome. Intravital imaging of the intact lung via two-photon intravital microscopy has proven a valuable method to investigate lung injury in small rodent models through characterization of inflammatory cells and vascular changes in real time. However, respiratory motion complicates the analysis of these time series images and requires selective data extraction to stabilize the image. Consequently, analysis of individual alveoli may not provide a complete picture of the integrated mechanical, vascular and inflammatory processes occurring simultaneously in the intact lung. To address these challenges, we developed a web browser-based visualization application named Alveolus Analysis to process, analyze and graphically display intravital lung microscopy data. RESULTS: The designed tool takes raw temporal image data as input, performs image preprocessing and feature extraction offline, and visualizes the extracted information in a web browser-based interface. The interface allows users to explore multiple experiments in three panels corresponding to different levels of detail: summary statistics of alveolar/neutrophil behavior, characterization of alveolar dynamics including lung edema and inflammatory cells at specific time points, and cross-experiment analysis. We performed a case study on the utility of the visualization with two members or our research team and they found the tool useful because of its ability to preprocess data consistently and visualize information in a digestible and informative format. CONCLUSIONS: Application of our software tool, Alveolus Analysis, to intravital lung microscopy data has the potential to enhance the information gained from these experiments and provide new insights into the pathologic mechanisms of inflammatory lung injury.

Assessing Graph-based Deep Learning Models for Predicting Flash Point.

Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message-passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3 % lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.