Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung.

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects.

Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning.

Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%.

Mechanistic insights into ball milling enhanced montmorillonite modification with tetramethylammonium for adsorption of gaseous toluene.

Montmorillonite is widely used for pollutants adsorption due to its porous structure and low price. However, the low specific surface area and small porosity limit its application in gas adsorption field. In this study, montmorillonite was organically modified using a facile dry ball milling method by tetramethylammonium bromide.

Spatiotemporal variations of dissolved organic matter in a typical multi-source watershed in northern China: a fluorescent evidence.

The amount of dissolved organic matter (DOM) in a multi-source watershed is important for complete management and assessing the river basin's long-term safety. Based on this, we study the composition, spatiotemporal changes, and primary sources of DOM using the excitation-emission matrix (EEM) and parallel factor analysis (PARAFAC). The relationship between DOM composition and water quality was also discussed.

A review on occurrence and risk of polycyclic aromatic hydrocarbons (PAHs) in lakes of China.

Polycyclic aromatic hydrocarbons (PAHs) residues have attracted attention worldwide. This study summarizes the current levels of PAH exposure in the water environments of lakes. In addition, the risk levels from individual PAHs and ΣPAHs in the water environments of lakes in China were evaluated by incremental lifetime cancer risk (ILCR) assessment, the toxic equivalent concentration (TEQ), the risk quotient (RQ), the effects range-low (ERL) and the effects range-median (ERM).