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Nationwide longitudinal evidence linking cognitive decline with exposure to fine particulate matter (PM2.5) constituents remains scarce in China. By constructing a dynamic cohort based on the China Health and Retirement Longitudinal Study, we aimed to assess individual and joint associations of PM2.5 constituents with cognitive function among middle-aged and older adults in China. Linear mixed-effects models incorporated with quantile-based g-computation were applied to investigate individual and joint associations of long-term exposures to PM2.5 constituents with cognitive function. Among 13,507 respondents, we evaluated 38,950 follow-up records of cognitive function tests. Declines in global cognitive score associated with an interquartile range (IQR) increase in exposure were −1.477 (95% CI: −1.722, −1.232) for nitrate, followed by −1.331 (−1.529, −1.133) for ammonium, −1.033 (−1.184, −0.883) for sulfate, −0.988 (−1.144, −0.832) for organic matter and −0.822 (−0.946, −0.699) for black carbon. An IQR-equivalent increase in joint exposure to these PM2.5 constituents was associated with a decline of −1.353 (−1.659, −1.048) in global cognitive score. Female, younger, and well-educated individuals were at greater vulnerability to cognitive impairment related to individual and joint exposure to PM2.5 constituents. This study suggested that later-life exposures to PM2.5 constituents were associated with cognitive decline in middle-aged and older adults in China. [ABSTRACT FROM AUTHOR]

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions. [ABSTRACT FROM AUTHOR]

The microstructure of rapidly quenched A1-Mn alloys were studied by TEM and SEM. The icosahedral phase in AI-Mn alloys was observed to show various types of fractal morphologies, which may be classified into four kinds: 1) dendritic shape, 2) flower-like shape, 3) granular shape and 4) grain-oscillation shape. After being digitized by a computer, the fractal dimensions (D) of these morphologies were calculated. Based on the traditional diffusion limited aggregation (DLA) model computer simulations were made with two-seeded and many-seeded clusters, which reflect the growing mechanism of some fractal structures in A1-Mn alloys. It is suggested that these fractal structures are formed by many icosahedral particles about 20 nm in size aggregating during the rapid quenching process. [ABSTRACT FROM AUTHOR]