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Objectives: Liver X receptors (LXRs) are essential for the regulation of intestinal cholesterol absorption. Because two isoforms exist, LXRα and LXRβ, with overlapping but not identical functions, we investigated whether LXRα and LXRβ exert different effects on intestinal cholesterol absorption.Design: Wild-type (WT), LXRα(-/-) and LXRβ(-/-) mice were fed control diet, 0.2% cholesterol-enriched diet or 0.2% cholesterol-enriched diet plus the LXR agonist GW3965.Results: When fed a control diet, all three genotypes showed similar levels of cholesterol absorption. Of interest, a significant increase in cholesterol absorption was found in the LXRα(-/-) mice, but not in the WT or LXRβ(-/-) animals, when fed a diet enriched with 0.2% cholesterol or 0.2% cholesterol + GW3965. Reduced faecal neutral sterol excretion and a hydrophobic bile acid profile were also observed in LXRα(-/-) mice. Greater increases in the apolipoprotein (apo)B-containing lipoproteins in serum were seen in the LXRα(-/-) mice. A 0.2% cholesterol +GW3965 diet suppressed intestinal Npc1l1 protein expression to the same extent for all genotypes, while Abca1 and Abcg5 were elevated to the same degree.Conclusions: In the intestine, LXRα and LXRβ seem to exert similar effects on expression of cholesterol-transporting proteins such as Npc1l1. Selective activation of LXRβ may generate effects such as increased cholesterol absorption and elevated serum levels of apoB-containing lipoproteins, which seem to be counteracted by LXRα. Therefore, an intestinal LXRβ-specific pathway might exist in terms of cholesterol transportation in addition to the main pathway. [ABSTRACT FROM AUTHOR]

The classification of fresh tobacco leaves during the picking process plays an important role in the subsequent roasting. In this paper, a lightweight convolutional neural network is used to detect the maturity of tobacco leaves quickly. Fresh tobacco leaves in the datasets are divided into 3 categories by the picking position, and each category is divided into 4 maturity levels and finally gets 12 types of tobacco leaves with different maturity. To ensure the lightweight of the model, the new network is based on the MobileNetV2 to establish. By utilizing shortcut operation, the shallow network information is preserved, and network degradation is suppressed. In the tobacco leaf datasets we obtained, the improved network has superior performance and compared with other classic networks, the model size and the number of operations have been reduced. [ABSTRACT FROM AUTHOR]