Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions.

This study predicts pyrolytic product yields via machine learning algorithms from biomass physicochemical characteristics and pyrolysis conditions. Random forest (RF), gradient boosting decision tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Adaptive Boost (Adaboost) algorithms are comparatively analyzed. Among these algorithms, the RF algorithm is the best modeling algorithm and performs best in predicting the bio-oil yield and performs well in predicting biochar and pyrolytic gas yields.

Extrarenal phenotypes of the UT-B knockout mouse.

The urea transporter UT-B is expressed in multiple tissues including erythrocytes, kidney, brain, heart, liver, colon, bone marrow, spleen, lung, skeletal muscle, bladder, prostate, and testis in mammals. Phenotype analysis of UT-B-null mice has confirmed that UT-B deletion results in a urea-selective urine-concentrating defect (see Chap. 9 ).

Urea transporter UT-B deletion induces DNA damage and apoptosis in mouse bladder urothelium.

Background: Previous studies found that urea transporter UT-B is abundantly expressed in bladder urothelium. However, the dynamic role of UT-B in bladder urothelial cells remains unclear. The objective of this study is to evaluate the physiological roles of UT-B in bladder urothelium using UT-B knockout mouse model and T24 cell line.

A novel small-molecule thienoquinolin urea transporter inhibitor acts as a potential diuretic.

Urea transporters (UTs) are a family of membrane channel proteins that are specifically permeable to urea and play an important role in intrarenal urea recycling and in urine concentration. Using an erythrocyte osmotic lysis assay, we screened a small-molecule library for inhibitors of UT-facilitated urea transport. A novel class of thienoquinolin UT-B inhibitors were identified, of which PU-14 had potent inhibition activity on human, rabbit, rat, and mouse UT-B.