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Farnesoid X receptor (FXR) has emerged as a promising therapeutic target for nonalcoholic steatohepatitis (NASH) because of its tightly interwoven relationship with bile acid homeostasis, inflammation, fibrosis, and glucose and lipid metabolism. Evidence showed that intestinal FXR antagonism exhibited remarkable metabolic improvements in mice. Herein, we developed a series of betulinic acid derivatives as potent intestinal FXR antagonists, and

The neutral detergent fiber (NDF) content is a key factor in the forage palatability and livestock digestibility of alpine grassland. Traditional methods of measuring NDF are time-consuming and laborious, while multispectral remote sensing often has difficulty capturing subtle changes due to its broad spectral channels. In this study, we evaluate NDF using hyperspectral data and environmental factors (EFs) during 2016-2019. The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) are used to construct models. The results show that: 1) compared with the correlation analysis, the least absolute shrinkage and selection operator (LASSO) regression can more effectively select the important variables for estimating NDF and achieve significant dimension reduction; 2) estimation accuracy of the models (coefficient of determination (R²) between 0.44 and 0.51, relative root mean square error (RMSE) between 4.31% and 4.61%) based on first derivative (FD) spectra is relatively better than the models based on original spectra (OR), log transformation (Log), and continuum removal (CR) spectra (R² between 0.35 and 0.46, RMSE between 4.39% and 4.92%). The RF model based on FD-Log-vegetation indices (VIs)-EFs is the best model (R² = 0.62, RMSE = 3.98%); and 3) compared with the EFs, hyperspectral feature plays a pivotal role in estimating NDF, with a total contribution of 81.66%, and red-edge and shortwave infrared (SWIR) regions are significant for estimating NDF. In general, the combination of hyperspectral data with the EFs significantly improves the accuracy of estimating NDF. This study provides important guidance for future efforts on estimating NDF of natural grassland using remote sensing and machine learning algorithms.

Takeda G protein-coupled receptor 5 (TGR5) is a promising target for treating metabolic syndrome and inflammatory diseases. Herein, we identified a new series of betulinic acid derivatives as potent TGR5 agonists, which show remarkable activity on human (h) and canine (c) TGR5 but exhibit unpromising activity on murine (m) TGR5. Species difference was also observed with many other reported TGR5 agonists. Therefore, we screened 29 amino acids which were conserved in hTGR5 and cTGR5 but different in mTGR5 and found a key amino acid, H88 in mTGR5 (Y89 in hTGR5), which contributed to the species difference. With the CRISPR/Cas9 system, the mTGR5H88Y mutation was introduced into mice, and the optimized compound 11d-Na displayed a significant glucose-lowering effect and stimulated GLP-1 and insulin secretion in TGR5H88Y mice but not in wild-type animals. Taken together, our study provides a useful tool to bridge the gap of species difference and discovers a potent TGR5 agonist for further investigation.

Dyslipidemia is a chronic metabolic disease characterized by elevated levels of lipids in plasma. Recently, various studies demonstrate that the increased activity of adenosine 5′-monophosphate-activated protein kinase (AMPK) causes health benefits in energy regulation. Thus, great efforts have been made to develop AMPK activators as a metabolic syndrome treatment. In the present study, we investigated the effects of the AMPK activator C24 on dyslipidemia and the potential mechanisms. We showed that C24 (5–40 μM) dose-dependently increased the phosphorylation of AMPKα and acetyl-CoA carboxylase (ACC), and inhibited lipogenesis in HepG2 cells. Using compound C, an AMPK inhibitor, or hepatocytes isolated from liver tissue-specific AMPK knockout AMPKα1α2(fl/fl;Alb-cre) mice (AMPK LKO), we demonstrated that the lipogenesis inhibition of C24 was dependent on hepatic AMPK activation. In rabbits with high-fat and high-cholesterol diet-induced dyslipidemia, administration of C24 (20, 40, and 60 mg · kg(−1)· d(−1), ig, for 4 weeks) dose-dependently decreased the content of TG, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) in plasma and played a role in protecting against hepatic dysfunction by decreasing lipid accumulation. A lipid-lowering effect was also observed in high-fat and high-cholesterol diet-fed hamsters. In conclusion, our results demonstrate that the small molecular AMPK activator C24 alleviates hyperlipidemia and represents a promising compound for the development of a lipid-lowering drug.

Grassland height is one of the main factors used to evaluate grassland conditions. However, the retrieval of natural grassland height at the regional scale by remote sensing data and conventional statistical models will result in large errors, especially in the heterogeneous alpine grassland of the Tibetan Plateau (TP). In this article, we aimed to construct a model based on multiple variables (biogeographical, meteorological, and Moderate Resolution Imaging Spectroradiometer (MODIS) product) using a random forest (RF) algorithm to predict the spatial distribution of grassland height in the TP from 2003 to 2017. The results show the following conditions. 1) Seven variables (elevation, slope, aspect, enhanced vegetation index, reflectance in band seven of MODIS (B7), annual accumulated temperature (≥0 °C), and annual precipitation) that were selected by recursive feature elimination from 11 variables have high importance in the RF model. The final model exhibits good performance, with mean R2 and root mean squared error values of 0.51 and 6.15 cm, respectively, which were determined via 10-fold crossvalidation. 2) The mean grassland height (2003-2017) predicted by the RF model ranges from 5 to 10 cm in most areas of the TP, and the mean height is 10 cm. The grassland height in the east and southeast of the TP is significantly higher than that in other areas. 3) This article achieves a relatively accurate estimation of grassland height over a large spatial scale at 500-m spatial resolution, which plays an important role in accurately estimating aboveground biomass and evapotranspiration over grassland.

Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that 1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R2 0.04-0.39, and RMSE 859.68-1075.09 kg/ha, respectively; 2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate nonparameter models; 3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45%-44.27%. Meanwhile, RF models can explain 89.41% variation in AGB during grass growing season. This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy based on MODIS data was greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

Takeda G protein-coupled receptor 5 (TGR5) is a promising target for treating metabolic syndrome and inflammatory diseases. Herein, we identified a new series of betulinic acid derivatives as potent TGR5 agonists, which show remarkable activity on human (h) and canine (c) TGR5 but exhibit unpromising activity on murine (m) TGR5. Species difference was also observed with many other reported TGR5 agonists. Therefore, we screened 29 amino acids which were conserved in hTGR5 and cTGR5 but different in mTGR5 and found a key amino acid, H88 in mTGR5 (Y89 in hTGR5), which contributed to the species difference. With the CRISPR/Cas9 system, the mTGR5

The accurate and effective retrieval of forage phosphorus (P) content can provide significant information for the management of pastoral agriculture and grazing livestock. In this study, we constructed 39 models to estimate the forage P of alpine grassland in the east of Tibetan Plateau based on hyperspectral remote sensing and multiple factors (topography, soil, vegetation and meteorology) using a machine learning algorithm. The results show that (1) first derivative (FD) and continuum removal (CR) spectra can retrieve more feature bands that are mainly located in the near infrared (NIR) and shortwave infrared (SWIR) regions than log transformed (Log (1/R)) and original (OR) spectra for the forage P estimation; (2) in terms of the model precision, the combination of important bands (IBs) and important factors (longitude and monthly mean temperature) increase the accuracy of forage P estimation as compared with the models that used IBs alone; and (3) considering the precision, stability and simplicity of the model comprehensively, the FD-IBs + support vector machine (SVM) model is the optimum forage P inversion model, which presents coefficient of determination (R2) and root mean squared error (RMSE) values of 0.67 and 0.0472%, respectively, and standard deviations (SDs) of 0.2386 and 0.0050%, respectively. This model can account for 88% of the variation of forage P in alpine grassland. This study demonstrates the importance of using a multi-factor modeling approach and spectral transformation techniques for estimating the forage P of grasslands and provides a scientific basis for the reasonable use and management of alpine grassland resources.

The Zoige Plateau is typical of alpine wetland ecosystems worldwide, which play a key role in regulating global climate and ecological balance. Due to the influence of global climate change and intense human activities, the stability and sustainability of the ecosystems associated with the alpine marsh wetlands are facing enormous threats. It is important to establish a precise risk assessment method to evaluate the risks to alpine wetlands ecosystems, and then to understand the influencing factors of ecological risk. However, the multi-index evaluation method of ecological risk in the Zoige region is overly focused on marsh wetlands, and the smallest units of assessment are relatively large. Although recently developed landscape ecological risk assessment (ERA) methods can address the above limitations, the final directionality of the evaluation results is not clear. In this work, we used the landscape ERA method based on land use and land cover changes (LUCC) to evaluate the ecological risks to an alpine wetland ecosystem from a spatial pixel scale (5 km × 5 km). Furthermore, the boosted regression tree (BRT) model was adopted to quantitatively analyze the impact factors of ecological risk. The results show the following: (1) From 1990 to 2016, the land use and land cover (LULC) types in the study area changed markedly. In particular, the deep marshes and aeolian sediments, and whereas construction land areas changed dramatically, the alpine grassland changed relatively slowly. (2) The ecological risk in the study area increased and was dominated by regions with higher and moderate risk levels. Meanwhile, these areas showed notable spatio-temporal changes, significant spatial correlation, and a high degree of spatial aggregation. (3) The topographic distribution, climate changes and human activities influenced the stability of the study area. Elevation (23.4%) was the most important factor for ecological risk, followed by temperature (16.2%). Precipitation and GDP were also seen to be adverse factors affecting ecological risk, at levels of 13.0% and 12.1%, respectively. The aim of this study was to provide more precise and specific support for defining conservation objectives, and ecological management in alpine wetland ecosystems.

Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R2 and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R2 and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend.

Resistance of hepatocellular carcinoma (HCC) to existing chemotherapeutic agents largely contributes to the poor prognosis of patients, and discovery of novel anti-HCC drug is in an urgent need. Herein we report ψ-Bufarenogin, a novel active compound that we isolated from the extract of toad skin, exhibited potent therapeutic effect in xenografted human hepatoma without notable side effects. In vitro, ψ-Bufarenogin suppressed HCC cells proliferation through impeding cell cycle progression, and it facilitated cell apoptosis by downregulating Mcl-1 expression. Moreover, ψ-Bufarenogin decreased the number of hepatoma stem cells through Sox2 depression and exhibited synergistic effect with conventional chemotherapeutics. Mechanistic study revealed that ψ-Bufarenogin impaired the activation of MEK/ERK pathway, which is essential in the proliferation of hepatoma cells. ψ-Bufarenogin notably suppressed PI3-K/Akt cascade, which was required in ψ-Bufarenogin-mediated reduction of Mcl-1 and Sox2. ψ-Bufarenogin inhibited the auto-phosphorylation and activation of epithelial growth factor receptor (EGFR) and hepatocyte growth factor receptor (c-Met), thereafter suppressed their primary downstream cascades Raf/MEK/ERK and PI3-K/Akt signaling. Taken together, ψ-Bufarenogin suppressed HCC growth via inhibiting, at least partially, receptor tyrosine kinases-regulated signaling, suggesting that ψ-Bufarenogin could be a novel lead compound for anti-HCC drug.

The first total synthesis of aphadilactones A-D, diastereomeric natural products recently isolated from the Meliaceae plant Aphanamixis grandifolia by Yue and co-workers, which possess an unprecedented carbon skeleton, has been achieved. The synthesis features a catalytic asymmetric hetero-Diels-Alder reaction to form the dihydropyran ring, concurrent installation of the lactone and furan moieties via a tandem acid-catalyzed acetal cleavage, oxidation, and cyclization process, and an intermolecular Diels-Alder reaction to forge the target products.

The applicability of hyperspectral remote sensing models for forage nitrogen (N) retrieval during different growth periods is limited. This study aims to develop a multivariate model feasible for estimating the forage N for the growth periods (June to November) in an alpine grassland ecosystem. The random forest (RF) algorithm is employed to determine the optimum combinations of 38 spectral variables capable of capturing dynamic variations in forage N. The results show that (1) throughout the growth period, the red-edge first shifts toward longer wavelengths and then shifts toward shorter wavelengths, the amplitude (AMP) and absorption depth (AD) gradually decrease, and the absorption position (AP) changes slightly; (2) the importance of spectral variables for forage N estimation differs during the different growth periods; (3) the multivariate model achieves better results for the first four periods (June to October) than for the last period (when the grass is completely senesced) (V-R2: 0.58−0.68 versus 0.23); and (4) for the whole growth period (June to November), the prediction accuracy of the general N estimation model validated by the unknown growth period is lower than that validated by the unknown location (V-R2 is 0.28 and 0.55 for the validation strategies of Leave-Time-Out and Leave-Location-Out, respectively). This study demonstrates that the changes in the spectral features of the red wavelength (red-edge position, AMP and AD) are well coupled with the forage N content. Moreover, the development of a multivariate RF model for estimating alpine grasslands N content during different growth periods is promising for the improvement of both the stability and accuracy of the model.

Although self quotient image (SQI) has been popularly used for face recognition as a retinex-based based illumination normalization method, it suffers from strong shadows in faces with abrupt intensity change. In this paper, we propose SQI-based illumination normalization for face recognition based on discrete wavelet transform (DWT). To remove shadow edges while preserving features, we combine DWT with SQI into retinex-based illumination normalization. First, we divide a face image into four sub-bands using wavelet analysis to enhance vertical, horizontal, and diagonal edges. Then, we perform histogram truncation to remove very bright and dark regions. Finally, we conduct illumination normalization from SQI and the histogram truncation result based on the retinex theory. Experimental results demonstrate that the proposed method outperforms state-of-the-art retinex-based ones in terms of face recognition accuracy.

Algorithm of wireless network selection is used to ensure that users can access to the most suitable network when the connected network can't satisfy users' requirement. Previous network selection algorithms are based on single attribute of wireless network. In the next generation network, it will make error if the algorithm still adopts single standard because the complexity is increased, so the algorithm should adopt several network performance evaluation, it is also known as multiple attribute decision making (MADM). This paper proposes an algorithms based on MADM, named wireless network selection algorithm based on independence of influence factors of QoS (WNSAIFQ).WNSAIFQ complies with the principle that the lower nodes are the reason of the upper nodes strictly, this removes the interdependence between those network's properties effectively. The experiment results based on a hardware network platform showed that this algorithm is better than others in aspects of network selection and error control.

The first total synthesis of aphadilactones A–D, diastereomeric natural products recently isolated from the Meliaceae plant Aphanamixis grandifolia by Yue and co-workers, which possess an unprecedented carbon skeleton, has been achieved. The synthesis features a catalytic asymmetric hetero-Diels–Alder reaction to form the dihydropyran ring, concurrent installation of the lactone and furan moieties via a tandem acid-catalyzed acetal cleavage, oxidation, and cyclization process, and an intermolecular Diels–Alder reaction to forge the target products.

Animal husbandry is the main agricultural type over the Tibetan Plateau, above ground biomass (AGB) is very important to monitor the productivity for administration of grassland resources and grazing balance. The MODIS vegetation indices have been successfully used in numerous studies on grassland AGB estimation in the Tibetan Plateau area. However, there are considerable differences of AGB estimation models both in the form of the models and the accuracy of estimation. In this study, field measurements of AGB data at Sangke Town, Gansu Province, China in four years (2013-2016) and MODIS indices (NDVI and EVI) are combined to construct AGB estimation models of alpine meadow grassland. The field measured AGB are also used to evaluate feasibility of models developed for large scale in applying to small area. The results show that (1) the differences in biomass were relatively large among the 5 sample areas of alpine meadow grassland in the study area during 2013-2016, with the maximum and minimum biomass values of 3,963 kg DW/ha and 745.5 kg DW/ha, respectively, and mean value of 1,907.7 kg DW/ha; the mean of EVI value range (0.42-0.60) are slightly smaller than the NDVI’s (0.59-0.75); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on MODIS EVI, with root mean square error of 656.6 kg DW/ha and relative estimation errors (REE) of 36.3%; (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, the Gannan Prefecture, and Xiahe County) are higher than those directly constructed based on the small area of this study by 9.5%–31.7%, with the increase of the modeling study area scales, the REE increasing as well. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

A series of structurally related analogues of the natural product paracaseolide A were synthesized and identified as potent PTP1B inhibitors. Among these analogues, compound 10 in particular showed improved PTP1B enzyme inhibitory activity, high selectivity for PTP1B over TC-PTP, and improved cellular effects.

Diacylglycerol acyltransferase (DGAT), the only limited enzyme in the synthesis of triacylglyc- erol (TAG), is regarded as an important therapeutic target for human obesity and other metabolic syndromes. Compounds 5-8 were designed and synthesized, which the lactone group of aphadilactone C was introduced into the PF-04620110 and AZD-7687, which have entered into the clinical research, and to verify whether the lactone in aphadilactone C played the same role as carboxylic group in PF-04620110 and AZD-7687. The final vitro assay showed that compounds 5-8 no longer have the inhibition activity to DGAT1. This might suggest that inhibition mechanism of aphadilactone C was not the same as PF-04620110 and AZD-7687 and need further study. Keywords aphadilactone C,DGAT1,selective inhibitor,PF-04620110,AZD-7687

An approach is developed for the synthesis of bisbenzannelated spiro[5,5]ketals via a catalytic relay reaction cascade involving a new cyclo-etherification, which is prompted by fluoride and catalyzed by the hypoiodite species generated in situ from irradiative aerobic oxidation of an iodide ion formed in the former step of the reaction cascade.