Activation of AMP-Activated Protein Kinase Is Required for Berberine-Induced Reduction of Atherosclerosis in Mice: The Role of Uncoupling Protein 2

Berberine, a botanical alkaloid purified from Coptidis rhizoma, is reported to activate the AMP-activated protein kinase (AMPK). Whether AMPK is required for the protective effects of berberine in cardiovascular diseases remains unknown. This study was designed to determine whether AMPK is required for berberine-induced reduction of oxidative stress and atherosclerosis in vivo.ApoE (ApoE⁻/⁻) mice and ApoE⁻/⁻/AMPK alpha 2⁻/⁻ mice that were fed Western diets were treated with berberine for 8 weeks. Atherosclerotic aortic lesions, expression of uncoupling protein 2 (UCP2), and markers of oxidative stress were evaluated in isolated aortas.In ApoE⁻/⁻ mice, chronic administration of berberine significantly reduced aortic lesions, markedly reduced oxidative stress and expression of adhesion molecules in aorta, and significantly increased UCP2 levels. In contrast, in ApoE⁻/⁻/AMPK alpha 2⁻/⁻ mice, berberine had little effect on those endpoints. In cultured human umbilical vein endothelial cells (HUVECs), berberine significantly increased UCP2 mRNA and protein expression in an AMPK-dependent manner. Transfection of HUVECs with nuclear respiratory factor 1 (NRF1)-specific siRNA attenuated berberine-induced expression of UCP2, whereas transfection with control siRNA did not. Finally, berberine promoted mitochondrial biogenesis that contributed to up-regulation of UCP2 expression.We conclude that berberine reduces oxidative stress and vascular inflammation, and suppresses atherogenesis via a mechanism that includes stimulation of AMPK-dependent UCP2 expression

Thermodynamic Simulation of the RDX-Aluminum Interface Using ReaxFF Molecular Dynamics

We use reactive molecular dynamics (RMD) simulations to study the interface between cyclotrimethylene trinitramine (RDX) and aluminum (Al) with different oxide layers to elucidate the effect of nanosized Al on thermal decomposition of RDX. A published ReaxFF force field for C/H/N/O elements was retrained to incorporate Al interactions and then used in RMD simulations to characterize compound energetic materials. We find that the predicted adsorption energies for RDX on the Al(111) surface and the apparent activation energies of RDX and RDX/Al are in agreement with ab initio calculations. The Al(111) surface-assisted decomposition of RDX occurs spontaneously without potential barriers, but the decomposition rate becomes slow when compared with that for RDX powder. We also find that the Al(111) surface with an oxide layer (Al oxide) slightly increases the potential barriers for decomposition of RDX molecules, while α-Al_2O_3(0001) retards thermal decomposition of RDX, due to the changes in thermal decomposition kinetics. The most likely mechanism for the thermal decomposition of RDX powder is described by the Avrami–Erofeev equation, with n = 3/4, as random nucleation and subsequent growth model. Although the decomposition mechanism of RDX molecules in the RDX/Al matrix complies with three-dimensional diffusion, Jander’s equation for RDX(210)/Al oxide and the Zhuralev–Lesokin–Tempelman (Z-L-T) equation for RDX(210)/Al_2O_3(0001) provide a more accurate description. We conclude that the origin of these differences in dynamic behavior is due to the variations in the oxide layer morphologies

Tyrosine Nitration of PA700 Links Proteasome Activation to Endothelial Dysfunction in Mouse Models with Cardiovascular Risk Factors

Oxidative stress is believed to cause endothelial dysfunction, an early event and a hallmark in cardiovascular diseases (CVD) including hypertension, diabetes, and dyslipidemia. However, the targets for oxidative stress-mediated endothelial dysfunction in CVD have not been completely elucidated. Here we report that 26S proteasome activation by peroxynitrite (ONOO−) is a common pathway for endothelial dysfunction in mouse models of diabetes, hypertension, and dyslipidemia. Endothelial function, assayed by acetylcholine-induced vasorelaxation, was impaired in parallel with significantly increased 26S proteasome activity in aortic homogenates from streptozotocin (STZ)-induced type I diabetic mice, angiotensin-infused hypertensive mice, and high fat-diets -fed LDL receptor knockout (LDLr−/−) mice. The elevated 26S proteasome activities were accompanied by ONOO−-mediated PA700/S10B nitration and increased 26S proteasome assembly and caused accelerated degradation of molecules (such as GTPCH I and thioredoxin) essential to endothelial homeostasis. Pharmacological (administration of MG132) or genetic inhibition (siRNA knockdown of PA700/S10B) of the 26S proteasome blocked the degradation of the vascular protective molecules and ablated endothelial dysfunction induced by diabetes, hypertension, and western diet feeding. Taken together, these results suggest that 26S proteasome activation by ONOO−-induced PA700/S10B tyrosine nitration is a common route for endothelial dysfunction seen in mouse models of hypertension, diabetes, and dyslipidemia

The Overseeing Mother: Revisiting the Frontal-Pose Lady in the Wu Family Shrines in Second Century China

Located in present-day Jiaxiang in Shandong province, the Wu family shrines built during the second century in the Eastern Han dynasty (25–220) were among the best-known works in Chinese art history. Although for centuries scholars have exhaustively studied the pictorial programs, the frontal-pose female image situated on the second floor of the central pavilion carved at the rear wall of the shrines has remained a question. Beginning with the woman’s eyes, this article demonstrates that the image is more than a generic portrait (“hard motif ”), but rather represents “feminine overseeing from above” (“soft motif ”). This synthetic motif combines three different earlier motifs – the frontal-pose hostess enjoying entertainment, the elevated spectator, and the Queen Mother of the West. By creatively fusing the three motifs into one unity, the Jiaxiang artists lent to the frontal-pose lady a unique power: she not only dominated the center of the composition, but also, like a divine being, commanded a unified view of the surroundings on the lofty building, hence echoing the political reality of the empress mother’s “overseeing the court” in the second century during Eastern Han dynasty

Low-Dose Cd Induces Hepatic Gene Hypermethylation, along with the Persistent Reduction of Cell Death and Increase of Cell Proliferation in Rats and Mice

Cadmium (Cd) is classified as a human carcinogen probably associated with epigenetic changes. DNA methylation is one of epigenetic mechanisms by which cells control gene expression. Therefore, the present study genome-widely screened the methylation-altered genes in the liver of rats previously exposed to low-dose Cd.Rats were exposed to Cd at 20 nmol/kg every other day for 4 weeks and gene methylation was analyzed at the 48(th) week with methylated DNA immunoprecipitation-CpG island microarray. Among the 1629 altered genes, there were 675 genes whose promoter CpG islands (CGIs) were hypermethylated, 899 genes whose promoter CGIs were hypomethylated, and 55 genes whose promoter CGIs were mixed with hyper- and hypo-methylation. Caspase-8 gene promoter CGIs and TNF gene promoter CGIs were hypermethylated and hypomethylated, respectively, along with a low apoptosis rate in Cd-treated rat livers. To link the aberrant methylation of caspase-8 and TNF genes to the low apoptosis induced by low-dose Cd, mice were given chronic exposure to low-dose Cd with and without methylation inhibitor (5-aza-2'-deoxyctidene, 5-aza). At the 48(th) week after Cd exposure, livers from Cd-treated mice displayed the increased caspase-8 CGI methylation and decreased caspase-8 protein expression, along with significant increases in cell proliferation and overexpression of TGF-β1 and cytokeratin 8/18 (the latter is a new marker of mouse liver preneoplastic lesions), all which were prevented by 5-aza treatment.These results suggest that Cd-induced global gene hypermethylation, most likely caspase-8 gene promoter hypermethylation that down-regulated its expression, leading to the decreased hepatic apoptosis and increased preneoplastic lesions

Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data

The identification of seed vigor is of great significance to improve the seed germination rate, increase crop yield, and ensure product quality. In this study, based on a hyperspectral data acquisition system and an improved feature extraction algorithm, an identification model of the germination characteristics for corn seeds was constructed. In this research, hyperspectral data acquisition and the standard corn seed germination test for Zhengdan 958 were carried out. By integrating the hyperspectral data in the spectral range of 386.7–1016.7 nm and the first derivative information of the spectral data, the root length prediction for corn seeds was successfully completed. The data regression model and prediction relationship between the spectral characteristics and seedling root length were established by principal component regression, partial least squares, and support vector regression. The first derivative information of the hyperspectral data was obtained by comparing the prediction model results with the original spectral data, which was preprocessed by Savitzky–Golay smoothing, multiplicative scatter correction, standard normal variate, and curve fitting. The results showed that the prediction model based on the first-order differential spectral data showed better performance than the one based on the spectral data obtained by other processing algorithms. By comparing the prediction results using different data characteristics and regression models, it was found that the hyperspectral method can effectively predict the root length of the seed, with the coefficient of determination reaching 0.8319

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

Abstract Background Ultrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details. Methods To overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels. Results The results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metrics, such as peak signal to noise ratio, SNR and root mean squared error. Conclusions The experimental results show that the proposed method is effective for removing the speckle and the background noise in ultrasound images. The main reason is that it performs a “detect and replace” two-step mechanism. The advantages of the proposed RBSF lie in two aspects. Firstly, each central pixel is classified as noise, speckle or noise-free texture according to the absolute difference between the target pixel and the reference median. Subsequently, the Rayleigh-maximum-likelihood filter and the bilateral filter are switched to eliminate speckle and noise, respectively, while the noise-free pixels are unaltered. Therefore, it is implemented with better accuracy and robustness than the traditional methods. Generally, these traits declare that the proposed RSBF would have significant clinical application

Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data

The identification of seed vigor is of great significance to improve the seed germination rate, increase crop yield, and ensure product quality. In this study, based on a hyperspectral data acquisition system and an improved feature extraction algorithm, an identification model of the germination characteristics for corn seeds was constructed. In this research, hyperspectral data acquisition and the standard corn seed germination test for Zhengdan 958 were carried out. By integrating the hyperspectral data in the spectral range of 386.7–1016.7 nm and the first derivative information of the spectral data, the root length prediction for corn seeds was successfully completed. The data regression model and prediction relationship between the spectral characteristics and seedling root length were established by principal component regression, partial least squares, and support vector regression. The first derivative information of the hyperspectral data was obtained by comparing the prediction model results with the original spectral data, which was preprocessed by Savitzky–Golay smoothing, multiplicative scatter correction, standard normal variate, and curve fitting. The results showed that the prediction model based on the first-order differential spectral data showed better performance than the one based on the spectral data obtained by other processing algorithms. By comparing the prediction results using different data characteristics and regression models, it was found that the hyperspectral method can effectively predict the root length of the seed, with the coefficient of determination reaching 0.8319

Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7⁻1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis