Synthesis and Biological Evaluation of Novel Chlorogenic Acid-Apigenin Conjugates as Anti-acute Gout Agents.

Gout is the second largest metabolic disease worldwide after diabetes, with acute gouty arthritis as most common symptom. Xanthine oxidase (XOD) and the NOD like receptor-3 (NLRP3) inflammasome are the key targets for acute gout treatment. Chlorogenic acid has been reported with a good anti-inflammatory activity, and Apigenin showed an excellent potential in XOD inhibition. Therefore, a series of chlorogenic acid-apigenin (CA) conjugates with varying linkers were designed and synthesized as dual XOD/NLRP3 inhibitors, and their activities both in XOD and NLRP3 inhibition were evaluated. An in vitro study of XOD inhibitory activity revealed that the majority of CA conjugates exhibited favorable XOD inhibitory activity. Particularly, the effects of compounds 10c and 10d, with an alkyl linker on the apigenin moiety, were stronger than that of allopurinol. The selected CA conjugates also demonstrated a favorable anti-inflammatory activity in RAW264.7 cells. Furthermore, compound 10d, which showed the optimal activity both in XOD inhibition and anti-inflammatory, was chosen and its inhibitory ability on NLRP3 and related proinflammatory cytokines was further tested. Compound 10d effectively reduced NLRP3 expression and the secretion of interluekin-1ß (IL-1ß) and tumor necrosis factor-α (TNF-α) with an activity stronger than the positive control isoliquiritigenin (ISL). Based on these findings, compound 10d exhibits dual XOD/NLRP3 inhibitory activity and, therefore, the therapeutic effects on acute gout is worthy of further study.

The Mosquito Larvicidal Activity of Lignans from Branches of Cinnamomum camphora chvar. Borneol.

The chemical investigation of branches of Cinnamomum camphora chvar. Borneol guided by mosquito larvicidal activity led to the isolation of fourteen known lignans (1-14). Their structures were elucidated unambiguously based on comprehensive spectroscopic analysis and comparison with the literature data. This is the first report of these compounds being isolated from branches of Cinnamomum camphora chvar. Borneol. Compounds 3-5 and 8-14 were isolated from this plant for the first time. All compounds isolated were subjected to anti-inflammatory, mosquito larvicidal activity and cytotoxic activity evaluation. Compounds (1-14) showed significant mosquito larvicidal activity against Culex pipiens quinquefasciatus with lethal mortality in 50% (LC50), with values ranging from 0.009 to 0.24 µg/mL. Among them, furofuran lignans(1-8) exhibited potent mosquito larvicidal activity against Cx. p. quinquefasciatus, with LC50 values of 0.009-0.021 µg/mL. From the perspective of a structure-activity relationship, compounds with a dioxolane group showed high mosquito larvicidal activity and have potential to be developed into a mosquitocide.

Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves.

This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.

Fruit quality evaluation using spectroscopy technology: a review.

An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.

[Study on the Color Determination of Tomato Leaves Stressed by the High Temperature Based on Hyperspectral Imaging].

Determination of color values on tomato leaves stressed by the high temperature using hyperspectral imaging technique was studied in this paper. Hyperspectral images of sixty healthy and sixty unhealthy tomato leaves in the wavelengths of 380-1023 nm were acquired by the hyperspectral imaging system. Simultaneously, three color parameters (L*, a* and b*) were measured by a colorimeter. Reflectance of all pixels in the region of interest (ROI) was extracted from the corrected hyperspectral image. Partial Least Squares (PLS) models were established based on different preprocessing methods. Successive Projections Algorithm (SPA) was identified to select effective wavelengths. Finally, Partial Least Squares-Discriminant Analysis (PLS-DA) models were built to classify different types of samples. The results showed that the determination coefficient (R²) were 0. 818, 0. 109 and 0. 896 in the prediction sets of PLS modes; 0.591, 0.244 and 0.673 in the prediction sets of SPA-PLS models. The overall classification accuracy in the prediction sets of PLS-DA models were over 77.50%. It demonstrated that it is feasible to measure color values on tomato leaves and identify different types of samples using hyperspectral imaging technique.

[Prediction the Soluble Solid Content in Sugarcanes by Using Near Infrared Hyperspectral Imaging System].

In order to explore the feasibility of prediction soluble solid contents (SSC) in sugarcane stalks by using near infrared hyperspectral imaging techniques, two hundred and forty sugarcane stalks which come from three different varieties were studied. After obtaining the raw hyperspectral images of sugarcane stalks, the spectral information and textural features were discussed respectively. The prediction models were established by using partial least squares regression (PLSR), principal components regression (PCR) and least squares support vector machines (LS-SVM) algorithms. Besides, three different selected wavelengths algorithms such as successive projection (SPA) algorithms, intervals partial least squares (iPLS) algorithms and uninformation variables elimination (UVE) algorithm were analyzed after building partial least squares regression model. The results indicate that partial least squares regression model based on spectral features can be an steady model to predict SSC and the correlation coefficient (R2) of calibration sets and prediction sets are 0.879, 0.843. The root mean square errors of calibration sets and prediction sets are 0.644, 0.742 respectively. The obtained 105 wavelengths which were selected by UVE algorithm are effective spectral features. The R2 results of calibration sets and prediction sets of its PLSR model are 0.860, 0.813. The root mean square errors of calibration sets and prediction sets are 0.693, 0.810 respectively

[Review of Crop Canopy Spectral Information Detection Technology and Methods].

Compared with the traditional chemical methods and the subjective visual ways for measuring plant physiology information indicators, the assessments of crop canopy information through spectral radiometer are more simple, rapid and accurate. The applications of different types of spectral radiometer, especially for international general used Cropscan multispectral radiometer, for predicting crop canopy leaf area index under different growth stage, biomass, nitrogen, chlorophyll and yield, and monitoring plant diseases and insect pests were summarized based on crop group information acquisition methods in recent years. The varity of vegetation indices (VIs) were concluded after comparing regression coefficients of related models among different crops. In general, the correlation coefficients of mathematical models were high and it can realize the crop detection of various kinds of physiological information. Besides, the combination of multispectral radiometer and other sensors can provide useful information to evaluate the status of crops growth, which is very important in practice.

[Different wavelengths selection methods for identification of early blight on tomato leaves by using hyperspectral imaging technique].

Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonaliza-tion, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380-1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4. 7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97. 83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques.

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

A review of the recent advances for the in ovo sexing of chicken embryos using optical sensing techniques.

The culling of day-old male chicks has caused ethical and economic concerns. Traditional approaches for detecting the in ovo sex of chicken embryos involve opening the eggshell and inner membrane, which are destructive, time-consuming, and inefficient. Therefore, noncontact optical sensing techniques have been examined for the in ovo sexing of chicken embryos. Compared with traditional methods, optical sensing can increase determination throughput and frequency for the rapid sexing of chicken embryos. This paper presented a comprehensive review of the different optical sensing techniques used for the in ovo sexing of chicken embryos, including visible and near-infrared (Vis-NIR) spectroscopy, hyperspectral imaging, Raman spectroscopy, fluorescence spectroscopy, and machine vision, discussing their advantages and disadvantages. In addition, the latest research regarding different detection algorithms and models for the in ovo sexing of chicken embryos was summarized. Therefore, this paper provides updated information regarding the optical sensing techniques that can be used in the poultry industry and related research.

Prediction of acrylamide content in potato chips using near-infrared spectroscopy.

Acrylamide (ACR), a neurotoxin with carcinogenic properties that can affect fertility, is commonly found in fried and baked foods such as potato chips. This study was carried out to predict the ACR content in fried and baked potato chips using near-infrared (NIR) spectroscopy. Effective wavenumbers were identified using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA). Six wavenumbers (12799 cm-1, 12007 cm-1, 10944 cm-1, 10943 cm-1, 5801 cm-1, and 4332 cm-1) were selected using the ratio (λi/λj) and difference (λi-λj) of any two wavenumbers from the CARS and SPA results. First, partial least squares (PLS) models were established based on full spectral wavebands (12799-4000 cm-1), and the prediction models were subsequently redeveloped based on effective wavenumbers to predict ACR content. Results showed that the full and selected wavenumbers-based PLS models obtained the coefficient of determination (R2) of 0.7707 and 0.6670, respectively, and the root mean square errors of prediction (RMSEP) of 53.0442 µg/kg and 64.3810 µg/kg, respectively, in the prediction sets. The results of this work demonstrate the suitability of NIR spectroscopy as a non-destructive method for predicting ACR content in potato chips.

Detection of the 5-hydroxymethylfurfural content in roasted coffee using machine learning based on near-infrared spectroscopy.

Since 5-hydroxymethylfurfural (5-HMF) is carcinogenic to humans, its detection in foods is essential. This study performed near-infrared (NIR) spectroscopy (11998-4000 cm-1) to determine the 5-HMF content in roasted coffee. The random forest (RF) was used to extract important wavenumbers, after which three machine learning models (ordinary least square (OLS), support vector machine (SVM), and RF) were established for the prediction. RF obtained the best prediction results (Rc2 = 0.98 and Rp2 = 0.92) compared with OLS and SVM and effectively extracted the important wavenumbers (11667 cm-1, 11666 cm-1, 10905 cm-1, 7096 cm-1, 7095 cm-1, 7094 cm-1, 7093 cm-1, 7092 cm-1, 5054 cm-1, 5026 cm-1, 5025 cm-1, and 5024 cm-1). The results demonstrated that machine learning models based on NIR spectroscopy could provide a non-destructive approach for determining 5-HMF content in roasted coffee.

Carvacrol protects mice against LPS-induced sepsis and attenuates inflammatory response in macrophages by modulating the ERK1/2 pathway.

Macrophages play an important role in the development of life-threatening sepsis, which is characterized by multiorgan dysfunction, through their ability to produce inflammatory cytokines. Carvacrol is a phenolic compound that has been confirmed to possess strong anti­inflammatory activity. In this study, we mainly investigated the effect of carvacrol on lipopolysaccharide (LPS)-induced macrophage proinflammatory responses and endotoxic shock. The results showed that carvacrol significantly reduced mouse body weight loss and ameliorated pathological damage to the liver, lung, and heart under LPS-induced sepsis. Carvacrol attenuated inflammatory responses by inhibiting the LPS-induced production of inflammatory cytokine interleukin-6 (IL-6) in vivo and in vitro. Mechanistically, carvacrol inhibited IL-6 production mainly through the ERK1/2 signalling pathway in macrophages. Furthermore, carvacrol improved the survival of septic mice. This study sheds light on the role of carvacrol in the pathogenesis of LPS-induced sepsis, and thus, its potential in treating sepsis patients may be considered.

Characterizing the moisture content of tea with diffuse reflectance spectroscopy using wavelet transform and multivariate analysis.

Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325-1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888-1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

[Study of detection of SPAD value in tomato leaves stressed by grey mold based on hyperspectral technique].

Hyperspectral imaging feature of chlorophyll content (SPAD) in tomato leaves stressed by grey mold was studied in the present paper. Hyperspectral imagings of healthy and infected tomato leaves were obtained by hyperspectral imaging system from 380 to 1 030 nm and diffuse spectral response of region of interest (ROI) from hyperspectral imaging was extracted by EN-VI software, then different preprocessing methods were used including smoothing and normalization etc. The partial least squares regress (PLSR) and principal component regress (PCR) models were developed for the prediction of SPAD value in tomato leaves based on normalization preprocessing method, then the back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models were built based on the four variables suggested by PLSR model. Among the four models, LS-SVM model was the best to predict SPAD value and the coefficient of determination (R2) was 0.901 8 with the root mean square error of prediction (RMSEP) of 2.599 2. It was demonstrated that chlorophyll content (SPAD) in healthy and infected tomato leaves can be effectively detected by the hyperspectral imaging technique.

Copper-promoted trifluoromethylation of primary and secondary alkylboronic acids.

New couple: The Cu-promoted trifluoromethylation of primary and secondary alkylboronic acids with TMSCF(3) extends the scope of transition-metal-catalyzed trifluoromethylation reactions to sp(3)-hybridized carbon centers. It also represents one of the first examples for Cu-catalyzed C-C cross-coupling reactions of alkylboronic acid derivatives.

(E)-9-Octadecenoic Acid Ethyl Ester Derived from Lotus Seedpod Ameliorates Inflammatory Responses by Regulating MAPKs and NF-κB Signalling Pathways in LPS-Induced RAW264.7 Macrophages.

Inflammation is generally considered a key risk factor in the progress of several chronic diseases, such as arthritis, gastritis, and hepatitis. Natural products with anti-inflammatory ability have played a great role in the process of overcoming these inflammatory diseases. In this study, we evaluated the anti-inflammatory activities of ten natural compounds derived from lotus seedpod and discovered (E)-9-octadecenoic acid ethyl ester (E9OAEE) inhibited the production of nitric oxide (NO) optimally in lipopolysaccharide (LPS)-induced RAW264.7 macrophages. Furthermore, we explored the effects of E9OAEE on inflammatory responses and the underlying mechanisms in LPS-induced RAW264.7 macrophages. The results indicated that E9OAEE significantly suppressed the production of NO, prostaglandin E2 (PGE2), and tumour necrosis factor-α (TNFα) in a dose-dependent manner. The protein expression and mRNA levels of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX2) were inhibited by pretreatment of E9OAEE. Furthermore, E9OAEE restrained the phosphorylation of mitogen-activated protein kinase (MAPKs) family members, ERK, P38, and JNK stimulated by LPS-treated for 30 min and prevented the nuclear translocation of nuclear factor-kappa B (NF-κB) prompted by LPS-treated for 6 h in RAW264.7 macrophages. Taken together, we discovered an anti-inflammatory component from lotus seedpod and identified E9OAEE attenuated the inflammatory response in LPS-induced RAW264.7 macrophages probably by regulating the activation of MAPKs and NF-κB signalling pathways, which would provide some base for the development of new anti-inflammatory drugs.

Airborne bacterial communities in the poultry farm and their relevance with environmental factors and antibiotic resistance genes.

The accelerating occurrence and environmental dissemination of bacteria, gas pollutants and antibiotic resistance genes (ARGs) in aerosols of poultry farms have become emerging environmental issues due to their potential threat to animals, workers, and the communities located near such farms. Here, aerosol samples were gathered from inside and outside of the chicken house in winter with a transportable high-flow bioaerosol sampler. Then, 16S rRNA gene amplicon sequencing was used to categorize the bacteria in air samples, and the abundance of 12 ARG subtypes was researched via the real-time quantitative polymerase chain reaction (qPCR). Results indicated that the bacterial richness and diversity and total absolute abundance of ARGs were similar in the bioaerosols from indoor and downwind site of the poultry farm. The zoonotic pathogens, Staphylococcus and Corynebacterium, were detected both inside and outside of the chicken house, and the four most abundant target genes were blaTEM, tetQ, ermB and sul1 in aerosols. Moreover, the correlation between the bacterial communities and environmental factors, such as NH3 and H2S concentrations, wind speed, temperature and relative humidity, was analyzed. The result revealed that the indoor bacteria community was positively associated with temperature and concentrations of air pollutants (NH3 and H2S), and could spread from confinement buildings to the ambient atmosphere through wind. In addition, the network analysis result showed that the airborne bacteria might significantly contribute in shaping the ARGs' profiles in bioaerosol from inside and outside of the poultry house. Overall, our results revealed the airborne bacterial communities and their associated influencing factors in the micro-environment (inside of the chicken house and nearby the boundary of the farm), and brought a new perspective for studying the gas pollutants and bioaerosol from poultry farms in winter.

The polysaccharide from Camellia oleifera fruit shell enhances immune responses via activating MAPKs and NF-κB signaling pathways in RAW264.7 macrophages.

Background: Macrophage plays an important role in innate immune responses by secreting immune molecules and phagocytosis. Camellia oleifera fruit shell, accounting for approximately 60% weight of the single C. oleifera fruit, is rich in polysaccharides and has several biological activities such as anti-oxidation, lipid regulation and anticancer. However, the immunomodulatory activity of the polysaccharide from C. oleifera fruit shells (CPS) has not been reported. Objective: This study aimed to investigate the immunomodulatory activities and mechanisms of CPS in RAW264.7 macrophages. Methods: The Methyl Thiazolyl Tetrazolium (MTT) method was used to evaluate the effect of CPS on the cell viability of RAW264.7 macrophages, and cell morphology was pictured using microscope. The production of immune-related molecules, including nitric oxide (NO), prostaglandin E2 (PGE2), tumor necrosis factor α (TNFα), interleukin (IL)-1ß and IL-6, was detected by Griess assay and enzyme-linked immunosorbent assay (ELISA). The protein expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase 2 (COX2) and the phosphorylation level of mitogen-activated protein kinases (MAPKs) were analyzed through western blotting. The mRNA levels of related genes were tested using reverse transcription-polymerase chain reaction (RT-PCR). The nuclear translocation of nuclear factor-kappa B (NF-κB) was detected using immunofluorescence technology. Results: The results indicated that CPS treatment stimulated the production of NO and PGE2 and significantly enhanced the protein expression of iNOS and COX2 with little effect on the cell morphology and viability. Also, the secretion and mRNA levels of TNFα were increased by the treatment of CPS. In addition, CPS treatment markedly upregulated the phosphorylation level of MAPKs including Extracellular Signal Regulated Kinase (ERK), P38, and c-Jun N-terminal Kinase (JNK) at different time points and caused the activation and nuclear translocation of NF-κB. Conclusion: Our data implied that CPS exerts immunomodulatory activities by activating MAPKs and NF-κB signaling pathways in RAW264.7 macrophages.

Anti-Inflammatory Effects of Gynura procumbens on RAW264.7 Cells via Regulation of the PI3K/Akt and MAPK Signaling Pathways.

Gynura procumbens is a traditional herb and food extensively cultivated in China and Southeast Asian countries. In this work, the crude extract (CE) of G. procumbens was purified with macroporous resin to obtain the refined fraction, and its anti-inflammatory activity was compared with that of CE. Moreover, the detailed mechanisms of anti-inflammatory activity were also investigated for the first time. The results indicated that CE was more effective in anti-inflammatory activity and it could reduce the secretion of NO, TNF-α, and PGE2 via decreasing the iNOS, TNF-α, and COX-2 genes transcription and related proteins translation, which were associated with the inhibition of AP-1 and NF-κB nuclear translocation and downregulation of PI3K/Akt and MAPK signaling pathways. In conclusion, the extract of G. procumbens has a promising potential in inflammation-related disorders alleviation, and these findings could provide the basis for the comprehensive utilization of G. procumbens and the new functional food development.