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Pulmonary arterial hypertension (PAH) is a malignant cardiovascular disease. Eukaryotic initiation factor 2α (eIF2α) plays an important role in the proliferation of pulmonary artery smooth muscle cells (PASMCs) in hypoxia-induced pulmonary hypertension (HPH) rats. However, the regulatory mechanism of eIF2α remains poorly understood in PAH rats. Here, we discover eIF2α is markedly upregulated in monocrotaline (MCT)-induced PAH rats, eIF2α can be upregulated by mRNA methylation, and upregulated eIF2α can promote PASMC proliferation in MCT-PAH rats. GSK2606414, eIF2α inhibitor, can downregulate the expression of eIF2α and alleviate PASMC proliferation in MCT-PAH rats. And we further discover the mRNA of eIF2α has a common sequence with N 6-methyladenosine (m6A) modification by bioinformatics analysis, and the expression of METTL3, WTAP, and YTHDF1 is upregulated in MCT-PAH rats. These findings suggest a potentially novel mechanism by which eIF2α is upregulated by m6A modification in MCT-PAH rats, which is involved in the pathogenesis of PAH. [ABSTRACT FROM AUTHOR]

Two new anthraquinone glycosides, named 1-methyl-8-hydroxyl-9,10-anthraquinone-3-O-β-d-(6'-O-cinnamoyl)glucopyranoside (1) and rhein-8-O-β-d-[6'-O-(3''-methoxyl malonyl)]glucopyranoside (2), have been isolated from the roots of Rheum palmatum, together with seven known compounds, rhein-8-O-β-d-glucopyranoside (3), physcion-8-O-β-d-glucopyranoside (4), chrysophanol-8-O-β-d-glucopyranoside (5), aleo-emodin-8-O-β-d-glucopyranoside (6), emodin-8-O-β-d-glucopyranoside (7), aleo-emodin-ω-O-β-d-glucopyranoside (8), and emodin-1-O-β-d-glucopyranoside (9). Their structures were elucidated on the basis of chemical and spectral analysis. [ABSTRACT FROM AUTHOR]

As a biocatalyst, enzyme has the advantages of high catalytic efficiency, strong reaction selectivity, specific target products, mild reaction conditions, and environmental friendliness, and serves as an important tool for the synthesis of complex organic molecules. With the continuous development of gene sequencing technology, molecular biology, genetic manipulation, and other technologies, the diversity of enzymes increases steadily and the reactions that can be catalyzed are also gradually diversified. In the process of enzyme-catalyzed synthesis, the majority of common enzymatic reactions can be achieved by single enzyme catalysis, while many complex reactions often require the participation of two or more enzymes. Therefore, the combination of multiple enzymes together to construct the multi-enzyme cascade reactions has become a research hotspot in the field of biochemistry. Nowadays, the biosynthetic pathways of more natural products with complex structures have been clarified, and secondary metabolic enzymes with novel catalytic activities have been identified, discovered, and combined in enzymatic synthesis of natural/unnatural molecules with diverse structures. This study summarized a series of examples of multi-enzyme-catalyzed cascades and highlighted the application of cascade catalysis methods in the synthesis of carbohydrates, nucleosides, flavonoids, terpenes, alkaloids, and chiral molecules. Furthermore, the existing problems and solutions of multi-enzyme-catalyzed cascade method were discussed, and the future development direction was prospected.

Microglia-associated neuroinflammation plays an important role in the pathophysiology of ischemic stroke. Microglial activation and polarization, and the inflammatory response mediated by these cells play important roles in the development, progression and outcome of brain injury after ischemic stroke. Currently, there is no effective strategy for treating ischemic stroke in clinical practice. Therefore, it is clinically important to study the role and regulation of microglia in stroke. In this review, we discuss the involvement of microglia in the neuroinflammatory process in ischemic stroke, with the aim of providing a better understanding of the relationship between ischemic stroke and microglia., Competing Interests: None

To investigate the chemical composition and pollution characteristics of spring fine particles (PM 2.5 ) in Changzhou, a total of 84 PM 2.5 samples were collected from March 1st to May 30th, 2017. We measured and analyzed conventional components, such as water-soluble ions (WSIIs) and carbonaceous components (OC and EC). The water-soluble organic aerosol (WSOA) was also analyzed by an aerodyne soot particle aerosol mass spectrometer (SP-AMS). During the sampling period, the average daily PM 2.5 concentration was 101.97 μg·m -3 , with more than 73.8% sampling days exceeding the Target-2 standard of the national ambient air quality standard of China. The air quality during the sampling period was dominated by light, moderate, and heavy pollution, accounting for 39.3%, 21.4%, and 13.1% of the total days, respectively. The total WSIIs accounted for 39.86% of PM 2.5 mass, of which secondary ions (SO 4 2- , NH 4 + , and NO 3 - ) accounted for 81.85% of the total WSIIs. The slope of the linear fitted line of the anion and cation charge balance (AE/CE) was greater than 1 (1.09), which indicated that PM 2.5 was weakly acidic. The average OC/EC ratio was 2.53, indicating that PM 2.5 was influenced by the secondary conversion. WSOA included C x H y + (32.1%), C x H y O + (30.4%), C x H y O 2 + (25.4%), and H y O + (4.7%) identified by SP-AMS. The average oxygen-to-carbon (O/C), hydrogen-to-carbon (H/C), nitrogen-to-carbon (N/C), and organic matter-to-organic carbon (OM/OC) ratios of the WSOA were 0.72, 1.53, 0.04, and 2.15, respectively. Higher O/C indicated higher contributions from secondary photochemical reaction conversion in spring. Positive matrix factorization (PMF) analysis for AMS mass spectra of WSOA identified three sources, namely hydrocarbon-like (HOA), semi-volatile oxygenated OA (SVOOA)-biomass burning OA (BBOA), and low-volatility oxygenated OA (LVOOA), which on average accounted for 18.4%, 34.1%, and 47.4% of the total WSOA, respectively.

To investigate the characteristics of diurnal variation of humic-like substances (HULIS) in atmospheric aerosols during winter in Changzhou, a total of 64 fine particle (PM 2.5 ) samples were collected from January 1 to February 28, 2017. In this study, the concentration as well as light absorption parameters of humic-like substances of carbon (HULIS-C) were examined. The results showed that the average day PM 2.5 and HULIS-C concentrations were 122.60 μg·m -3 and 4.18 μg·m -3 , respectively, slightly higher than those (111.72 μg·m -3 and 3.74 μg·m -3 ) at night. Via UV-vis analysis, we found that the ratios of absorbance at 250 nm ( A 250 ) of HULIS and WSOA (day:~77%, night:~75%) were significantly higher than the concentration ratios of HULIS-C and WSOC (day:~51%, night:~50%), indicating that more UV-absorbing substances and poly-conjugated aromatic structures exist in HULIS. The daytime E 250 / E 365 and SUVA 280 in HULIS were close to the nighttime ones, indicating that there was no obvious difference between day and night in HULIS with reference to aromaticity and molecular weight. There were no significant differences in MAE 365 and AAE 300-400 of HULIS between day and night. In addition, to obtain the main influencing factors of HULIS in winter in Changzhou, the correlation analysis of HULIS-C and other chemical components were conducted. The results show that biomass burning, fossil fuel combustion, factory emissions, and especially secondary formation, were the main influencing factors. Moreover, daytime HULIS were mainly influenced by secondary reaction of anthropogenic precursor contaminants, while nighttime HULIS were affected not only by secondary formation by but by also primary combustion emissions.

To understand the characteristics and sources of water-soluble organic carbon (WSOC) and organic nitrogen (WSON) in atmospheric aerosols during spring in Changzhou, 84 fine particle (PM 2.5 ) samples were collected from March 1 to May 30, 2017, in Changzhou. The water-soluble components, including water-soluble organic carbon, water-soluble total nitrogen (WSTN), water-soluble ions, and carbonaceous components (OC and EC), were analyzed. The levels of WSOC and WSON and their source characteristics were discussed. The results show that the average concentrations of PM 2.5 , WSOC, and WSON are 101.97, 7.63, and 1.50 μg·m -3 , respectively, during the sampling period. The WSON accounts for 12.9% of the WSTN and the water-soluble inorganic nitrogen mainly exists in two forms, that is, NH 4 + and NO 3 - , accounting for 86.15% of the WSTN. The WSOC is weakly correlated with WSON ( r =0.58), indicating that WSOC and WSON do not have the same sources. The WSOC is related to SOC, K + , and secondary ions (SO 4 2- , NH 4 + , and NO 3 - ), indicating that it is mainly derived from biomass burning and secondary conversion; WSON is strongly correlated to secondary ions, indicating that it is mainly derived from secondary conversion. The wind speed is the main factor affecting the WSOC and WSON concentration levels. Furthermore, the WSON is positively correlated with the air pressure and negatively correlated with the temperature. The results of the principal component analysis show that PM 2.5 mainly originates from four sources:secondary formation, dust, coal combustion, biomass burning, and the ocean. The backward trajectory analysis indicates that the total concentrations of PM 2.5 , WSOC, and WSON in air masses from long-distance transformation are higher than that from short-distance transmission, whereas there is no significant difference in the WSON/WSTN ratio from different transmission paths.

Maize is among the most important economic corps in China while moisture content is a critical parameterin the process of storage and breeding. To measure the moisture content in maize kernel, a near-infrared hyperspectral imaging system has been built to acquire reflectance images from maize kernel samples in the spectral region between 1 000 and 2 500 nm. Near-infrared hyperspectral information of full surface and embryo of maize kernel were firstly extracted based on band ratio coupled with a simple thresholding method and the spectra analysis between moisture content in maize kernel and embryo was performed. The characteristic bands were then selected with the help of Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA) and Successive Projection Algorithm (SPA). Finally, these selected variables were used as the inputs to build Partial Least Square (PLS) models for determining the moisture content of maize kernel. In this study, a significant relation, which the spectral reflectance decreases as moisture content increase, between moisture content and spectral of embryo in maize kernel was observed. For the investigated independent test samples, all the proposed regression models, namely CARS-PLS, GA-PLS and SPA-PLS, achieved a good performance by using the information of embryo region. The correlation coefficient (Rp) and Root Mean Squared Error of Prediction (RMSEP) and number of characteristic wavelength for the prediction set were 0.931 2, 0.315 3, 9 and 0.917 6, 0.336 9, 14 and 0.922 7, 0.336 6, 16 for CARS-PLS, GA-PLS and SPA-PLS models, respectively. And, compared with models obtained by full surface spectral information, less characteristic wavelengths is used for development of CARS-PLS, GA-PLS and SPA-PLS models, while similar results were obtained. Comprehensively analyzing to both model accuracy and model complexity, SPA-PLS model by using embryo region information achieved the best result. Wavelengths at 1 197，1 322 and 1 495 nm were applied to extracted the information of embryo region, and the bands at 1 322, 1 342, 1 367, 1 949, 2 070 and 2 496 nm were used to establish the SPA-PLS model. These results demonstrated that near-infrared hyperspectral information from embryo region is more effective for determination of moisture nondestructive in maize kernel.

Non-destructive detection for soluble solids content (SSC) is important to improve watermelon’s internal quality, which attracts more and more attention from consumers. In order to realize the precise detection for SSC of mini watermelon’s whole surface by using Near-infrared (NIR) spectroscopy and reduce the influence of detective position variability on the accuracy of NIR prediction model for SSC, the diffused transmission spectra and soluble solids content were collected from three different detective positions of ‘jingxiu’ watermelon, including the equator, calyx and stem. The prediction models of single detective position and mixed three detective positions for SSC were established with Partial least square (PLS). Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were adopted to select effective variables of NIR spectroscopy for SSC of watermelon as well. The results showed that the prediction model of mixed three detective positions was better than the model of single detective position. Meanwhile, 42 characteristic variables of NIR spectroscopy selected with CARS were used to establish PLS prediction model for SSC. The prediction model was simplified significantly and the prediction accuracy for SSC was improved greatly. The correlation coefficient of prediction (RP) and root mean square error of prediction (RMSEP) by CARS-PLS were 0.892, 0.684 °Brix for the equator, 0.905, 0.621 °Brix for the calyx, 0.899, 0.721 °Brix for the stem, respectively. However, the prediction result of SPA-PLS established by 19 characteristic wavelength variables of NIR spectroscopy was bad for the equator, calyx and stem detective positions. The correlation coefficient of prediction (RP) is less than 0.752 and root mean square error of prediction (RMSEP) is relatively high. It was proposed that the PLS prediction model established by mixed three different detective positions with effective characteristic wavelength variables selected by CARS can improve the prediction accuracy for SSC. And the CARS-PLS prediction model can achieve fast and precise detection for SSC of mini watermelon’s whole surface. The influence of detective position variability on the accuracy of NIR prediction model could be reduced simultaneously. This paper could provide theoretical basis for calibrating NIR prediction model for SSC of mini watermelon. It also could provide reference for developing the portable and non-destructive detection equipment for soluble solids content of mini watermelon’s whole surface.

In using spectroscopy to quantitatively or qualitatively analyze the quality of fruit, how to obtain a simple and effective correction model is very critical for the application and maintenance of the developed model. Strawberry as the research object, this research mainly focused on selecting the key variables and characteristic samples for quantitatively determining the soluble solids content. Competitive adaptive reweighted sampling (CARS) algorithm was firstly proposed to select the spectra variables. Then, Samples of correction set were selected by successive projections algorithm (SPA), and 98 characteristic samples were obtained. Next, based on the selected variables and characteristic samples, the second variable selection was performed by using SPA method. 25 key variables were obtained. In order to verify the performance of the proposed CARS algorithm, variable selection algorithms including Monte Carlo-uninformative variable elimination (MC-UVE) and SPA were used as the comparison algorithms. Results showed that CARS algorithm could eliminate uninformative variables and remove the collinearity information at the same time. Similarly, in order to assess the performance of the proposed SPA algorithm for selecting the characteristic samples, SPA algorithm was compared with classical Kennard-Stone algorithm Results showed that SPA algorithm could be used for selection of the characteristic samples in the calibration set. Finally, PLS and MLR model for quantitatively predicting the SSC (soluble solids content) in the strawberry were proposed based on the variables/samples subset (25/98), respectively. Results show that models built by using the 0.59% and 65.33% information of original variables and samples could obtain better performance than using the ones obtained by using all information of the original variables and samples. MLR model was the best with R(pre)2 = 0.9097, RMSEP=0.3484 and RPD = 3.3278.

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

The quality and safety of fruits and vegetables are the most concerns of consumers. Chemical analytical methods are traditional inspection methods which are time-consuming and labor intensive destructive inspection techniques. With the rapid development of imaging technique and spectral technique, hyperspectral imaging technique has been widely used in the nondestructive inspection of quality and safety of fruits and vegetables. Hyperspectral imaging integrates the advantages of traditional imaging and spectroscopy. It can obtain both spatial and spectral information of inspected objects. Therefore, it can be used in either external quality inspection as traditional imaging system, or internal quality or safety inspection as spectroscopy. In recent years, many research papers about the nondestructive inspection of quality and safety of fruits and vegetables by using hyperspectral imaging have been published, and in order to introduce the principles of nondestructive inspection and track the latest research development of hyperspectral imaging in the nondestructive inspection of quality and safety of fruits and vegetables, this paper reviews the principles, developments and applications of hyperspectral imaging in the external quality, internal quality and safety inspection of fruits and vegetables. Additionally, the basic components, analytical methods, future trends and challenges are also reported or discussed in this paper.

To improve the precision and robustness of the NIR model of the soluble solid content (SSC) on pear. The total number of 160 pears was for the calibration (n=120) and prediction (n=40). Different spectral pretreatment methods, including standard normal variate (SNV) and multiplicative scatter correction (MSC) were used before further analysis. A combination of genetic algorithm (GA) and successive projections algorithm (SPA) was proposed to select most effective wavelengths after uninformative variable elimination (UVE) from original spectra, SNV pretreated spectra and MSC pretreated spectra respectively. The selected variables were used as the inputs of least squares-support vector machine (LS-SVM) model to build models for de- termining the SSC of pear. The results indicated that LS-SVM model built using SNVE-UVE-GA-SPA on 30 characteristic wavelengths selected from full-spectrum which had 3112 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.956, 0.271 for SSC. The model is reliable and the predicted result is effective. The method can meet the requirement of quick measuring SSC of pear and might be important for the development of portable instruments and online monitoring.

The present study proposed competitive adaptive reweighted sampling (CARS) algorithm to be used to select the key variables from near-infrared hyperspectral imaging data of "Ya" pear. The performance of the developed model was evaluated in terms of the coefficient of determination(r2), and the root mean square error of prediction (RMSEP) and the ratio (RPD) of standard deviation of the validation set to standard error of prediction were used to evaluate the performance of proposed model in the prediction process. The selected key variables were used to build the PLS model, called CARS-PLS model. Comparing results obtained from CARS-PLS model and results obtained from full spectra PLS, it was found that the better results (r(2)pre = 0. 908 2, RMSEP=0. 312 0 and RPD=3. 300 5) were obtained by CARS-PLS model based on only 15. 6% information of full spectra. Moreover, performance of CARS-PLS model was also compared with PLS models built by using variables got by Monte Carlo-uninformative variable elimination (MC-UVE) and genetic algorithms (GA) method. The result found that CARS variable selection algorithm not only can remove the uninformative variables in spectra, but also can reduce the collinear variables from informative variables. Therefore, this method can be used to select the key variables of near-infrared hyperspectral imaging data. This study showed that near-infrared hyperspectral imaging technology combined with CARS-PLS model can quantitatively predict the soluble solids content (SSC) in "Ya" pear. The results presented from this study can provide a reference for predicting other fruits quality by using the near-infrared hyperspectral imaging.

Bruising is one of the major defects occurring on apple surface inevitably during postharvest handling and processing stage. To detect slight bruises on apples fast and efficiently, a novel bruises detection algorithm based on hyperspectral imaging and minimum noise fraction transform is proposed. First, the hyperspectral images in the visible and near-infrared (400 approximately 1 000 nm) ranges are acquired, and MNF transform based on full ranges could obtain better detection performance compared to PCA transform; Second, five wavebands (560, 660, 720, 820 and 960 nm) are selected as the effective wavebands based on the coefficient curve of I-RELIEF method conducted on spectra extracted from intact and bruise surface; Third, the bruises detection algorithm is developed based on the effective wavebands and MNF transform method. For the investigated 40 sound samples and 40 different time stage bruise samples, the results with a 97. 1% overall detection rate are got. The recognition results indicate that the proposed methods and the effective wavelengths selected in this paper are feasible and efficient. This research lays a foundation for the development of multispectral imaging system based on MNF transform for slight bruises detection on apples.

It is very important to extract effective wavelengths for quantitative analysis of fruit internal quality based on hyperspectral imaging. In the present study, genetic algorithm (GA), successive projections algorithm (SPA) and GA-SPA combining algorithm were used for extracting effective wavelengths from 400-1 000 nm hyperspectral images of Yantai "Fuji" apples, respectively. Based on the effective wavelengths selected by GA, SPA and GA-SPA, different models were built and compared for predicting soluble solids content (SSC) of apple using partial least squares (PLS), least squared support vector machine (LS-SVM) and multiple linear regression (MLR), respectively. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Among all the models, the SPA-MLR achieved the best results, where Rp(2), RMSEP and RPD were 0.950 1, 0.308 7 and 4.476 6 respectively. Results showed that SPA can be effectively used for selecting the effective wavelengths from hyperspectral data. And, SPA-MLR is an optimal modeling method for prediction of apple SSC. Furthermore, less effective wavelengths and simple and easily-interpreted MLR model show that the SPA-MLR model has a great potential for online detection of apple SSC and development of a portable instrument.