Exploration on the occurrence state of fluorine in cement hydration products mixed with high fluorine alkali free liquid accelerator.

If there was abundant fluorine in shotcrete, it might leach out and pollute the soil or migrate to corrode the reinforcement.Therefore, this research mainly investigated the basic properties of high-fluorine alkali free liquid accelerator (HF-AFA) and its occurrence forms in cement hydration products.The macro-test results showed that with the increase of HF-AFA dosage, it appeared excellent coagulation promoting property.

APP/PS1 Gene-Environmental Cadmium Interaction Aggravates the Progression of Alzheimer's Disease in Mice via the Blood-Brain Barrier, Amyloid-β, and Inflammation.

Background: There is limited information about gene-environment interaction on the occurrence and the progression of Alzheimer's disease.

Objective: To explore the effect of environmental low-dose cadmium (Cd) exposure on the progress of Alzheimer's disease and the underlining mechanism.

Methods: We administered 1 mg/L, 10 mg/L cadmium chloride (treated groups), and water (control group) to C57BL/6J and APP/PS1 mice through drinking water, from one week before mating, until the offspring were sacrificed at 6 months of age.

Optimizing the models for rapid determination of chlorogenic acid, scopoletin and rutin in plant samples by near-infrared diffuse reflectance spectroscopy.

Polyphenols in plant samples have been extensively studied because phenolic compounds are ubiquitous in plants and can be used as antioxidants in promoting human health. A method for rapid determination of three phenolic compounds (chlorogenic acid, scopoletin and rutin) in plant samples using near-infrared diffuse reflectance spectroscopy (NIRDRS) is studied in this work. Partial least squares (PLS) regression was used for building the calibration models, and the effects of spectral preprocessing and variable selection on the models are investigated for optimization of the models.

Selecting significant genes by randomization test for cancer classification using gene expression data.

Gene selection is an important task in bioinformatics studies, because the accuracy of cancer classification generally depends upon the genes that have biological relevance to the classifying problems. In this work, randomization test (RT) is used as a gene selection method for dealing with gene expression data. In the method, a statistic derived from the statistics of the regression coefficients in a series of partial least squares discriminant analysis (PLSDA) models is used to evaluate the significance of the genes.