[Adsorption Characteristics of Tetracycline by CuFeO-modified Biochar].

CuFeO-modified biochars were prepared through co-precipitation and hydrothermal methods, and the composites had high efficiency removal for tetracycline (TC) from water. The CuFeO-modified biochar with a 2:1 mass ratio of CuFeO to BC450 (CuFeO/BC450=2:1) demonstrated the best adsorption performance. The kinetic process of TC adsorption by CuFeO/BC450=2:1 was well fitted with the intraparticle diffusion model, suggesting that the adsorption process was controlled by film and pore diffusion.

Serum ferritin is correlated with non-alcoholic fatty liver disease in middle-aged and older patients with type 2 diabetes.

Objective: Previous studies have shown the correlations between serum ferritin and non-alcoholic fatty liver disease (NAFLD) or diabetes. However, this relationship remains unclear in patients with type 2 diabetes (T2DM) with NAFLD. Therefore, this study aimed to elaborate the relationship between serum ferritin levels and NAFLD in middle-aged and older patients with T2DM and further explored the biomarkers for NAFLD in T2DM.

Formation of iodinated aromatic DBPs at different molar ratios of chlorine and nitrogen in iodide-containing water.

Variations in iodinated aromatic disinfection byproducts (DBPs) in the presence of I and organic compounds as a function of reaction time in different molar ratios (MRs) of HOCl:NH-N were investigated. Up to 17 kinds of iodinated aromatic DBPs were identified in the breakpoint chlorination of iodide (I)/organic (phenol, bisphenol S (BPS) and p-nitrophenol (p-NP)) systems, and the possible pathways for the formation of iodinated aromatic DBPs were proposed. The reaction pathways include HOCl/HOI electrophilic substitution and oxidation, while the dominant iodinated DBPs were quantified.

Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO.

Variable selection is of crucial significance in QSAR modeling since it increases the model predictive ability and reduces noise. The selection of the right variables is far more complicated than the development of predictive models. In this study, eight continuous and categorical data sets were employed to explore the applicability of two distinct variable selection methods random forests (RF) and least absolute shrinkage and selection operator (LASSO).