Rutin and Quercetin Decrease Cholesterol in HepG2 Cells but Not Plasma Cholesterol in Hamsters by Oral Administration

Rutin (R) and quercetin (Q) are two widespread dietary flavonoids. Previous studies regarding the plasma cholesterol-lowering activity of R and Q generated inconsistent results. The present study was therefore carried out to investigate the effects of R and Q on cholesterol metabolism in both HepG2 cells and hypercholesterolemia hamsters. Results from HepG2 cell experiments demonstrate that both R and Q decreased cholesterol at doses of 5 and 10 µM. R and Q up-regulated both the mRNA and protein expression of sterol regulatory element binding protein 2 (SREBP2), low-density lipoprotein receptor (LDLR), and liver X receptor alpha (LXRα). The immunofluorescence study revealed that R and Q increased the LDLR expression, while only Q improved LDL-C uptake in HepG2 cells. Results from hypercholesterolemia hamsters fed diets containing R (5.5 g/kg diet) and Q (2.5 g/kg diet) for 8 weeks demonstrate that both R and Q had no effect on plasma total cholesterol. In the liver, only Q reduced cholesterol significantly. The discrepancy between the in vitro and in vivo studies was probably due to a poor bioavailability of flavonoids in the intestine. It was therefore concluded that R and Q were effective in reducing cholesterol in HepG2 cells in vitro, whereas in vivo, the oral administration of the two flavonoids had little effect on plasma cholesterol in hamsters.     

A New Lactam from Cannabis sativa

Chemistry of Natural Compounds - A new lactam, 5-hydroxy-1-[(4-hydroxyphenyl)methyl]-2-pyrrolidinone (1), was isolated from the ethyl acetate extract of Cannabis sativa L. The structure was...     

Monitoring the kappa number of bleached pulps based on FT-Raman spectroscopy

Cellulose - A new strategy was proposed to determine the Kappa number of bleached Eucalyptus globulus kraft pulps based on FT-Raman spectroscopy. Before modeling, smoothing (Savitzky-Golay),...     

An actuator-disc model for the prediction of abrupt stall in an axial compressor rotor

The representation of loss in a cascade by the appearance of blockage has been extended to deal with blade rows by the use of a source distribution to represent this blockage, and in the case of the actuator disc approximation, the presence of sources is confined to an axi-symmetric dìstribution over the actuator disc. It is found that if a typical dependence of loss (and consequently diffusion ratio) upon incidence for each section of an axial compressor rotor is represented in this manner, the influence of blockage on the axial velocity distribution may be found using the potential equation combined with the usual actuator disc approximation. Study of the behaviour of the controlling ordinary differential equation for the axial velocity ahead of the disc reveals that as the flow is reduced, the equation contains a singularity within the range of radius and a meaningful solution does not exist. This result is interpreted as the limit to continuous operation and reasonable agreement between this predicted limit and the appearance of abrupt stall (experimentally) is found.     

Extraction-condensation method for solving large sets of equations in FE analysis

In static and dynamic analysis of a large complex structure, one may merely be interested in the stresses and displacements at some important locations. A new solution technique for structural analysis with FEM is proposed in this paper by which those important unknowns are separated from the rest. In static analysis the important unknows can be found directly. In dynamic analysis all mass and exciting forces can be condensed onto a small number of predetermined points in order to make the dynamic analysis much simplified. A large scale problem can be solved with a small capacity computer quickly.     

Factors Affecting Polymer Translocation Through a Nanopore in a Membrane

The variation of the translocation time with respect to the solvent quality was also studied. It showed that there is a transition, as the solvent quality improves from \poor to \good: when ABc, τ increases with increasing AB. When the chain length was changed, it was found that when the absorbed energy u0 was greater than uc, τ was proportional to N1:602; for u0相似文献   

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.