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Developing herbicide resistant cultivars is one of the effective methods to solve the safety problem caused by the use of herbicide. In this study, hyperspectral image was used to develop more robust leaf chlorophyll content (LCC) prediction model based on different datasets to finally analyze the response of LCC to glyphosate-stress. Chlorophyll a fluorescence (ChlF) was used to dynamically monitor the photosynthetic physiological response of transgenic glyphosate-resistant and wild glyphosate–sensitive maize seedlings, and applying chemometrics methods to extract time-series features to screen resistant cultivars. Both the proposed two transfer strategies achieved the best prediction of LCC with a coefficient of determination value of 0.84, and relative root mean square error of 4.03 for the prediction set. Based on the predicted LCC and ChlF data, we found the antioxidant system of glyphosate-sensitive plants is too fragile to protect themselves from the damage, while glyphosate-resistant plants could overcome it by activating more powerful antioxidant system. φE0, VJ, ψE0, and M0 could be used to indicate damage caused by glyphosate to differentiate resistant cultivars. This study provided a new methodology to monitor LCC to finally analyze glyphosate tolerance in a time-series manner, and verified the feasibility of ChlF in crop breeding.HighlightThis study proposed a new methodology to monitor leaf chlorophyll content to finally analyze glyphosate tolerance in vivo, and verified the feasibility of chlorophyll a fluorescence in crop breeding.

OBJECTIVE To investigate the associations of sodium excretion with blood pressure, mortality and cardiovascular diseases in Chinese population. METHODS We studied 39 366 individuals aged 35-70 years from 115 urban and rural communities in 12 centers across mainland China. Trained research staff conducted face-to-face interview to record baseline information of all participants based on questionnaires, and collected their morning fasting urine samples to estimate 24-h sodium excretion (24hUNaE). Multivariable frailty Cox regression accounting for clustering by centre was performed to examine the association between estimated 24hUNaE and the primary composite outcome of death and major cardiovascular events in a Chinese population. RESULTS Mean 24hUNaE was 5.68 (SD 1.69) g/day. After a median follow-up of 8.8 years, the composite outcome occurred in 3080 (7.8%) participants, of which 1426 (3.5%) died and 2192 (5.4%) suffered from cardiovascular events. 24hUNaE was positively associated with increased SBP and DBP. Using the 24hUNaE level of 4-4.99 g/day as the reference group, a 24hUNaE of either lower (

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380&ndash, 1030 nm) and near-infrared range (NIR, 874&ndash, 1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results, all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali.

Summary Background & aims Although fruits and vegetable consumption has been shown to be associated with lower risks of mortality, cancers, and cardiovascular disease (CVD), there are limited data from China on the shape of the association. This study aimed to quantify the relationship between levels of fruit, vegetable, and legume consumption with the risk of major CVD, CVD mortality, cancer incidence, cancer mortality, and all-cause mortality. Methods In the baseline survey, participants attended 1 of 115 (45 urban and 70 rural) communities from 12 provinces to complete a standardized questionnaire, and undergo a physical examination between 2005 and 2009, and were followed up till 2017 (for the current analysis). Diet was assessed through in-person interviews by using validated food-frequency questionnaires. The clinical outcomes were adjudicated centrally by trained physicians using standardized definitions. Cox frailty models were used to explore the associations between fruit, vegetable, and legume consumption with the risk of all-cause, CVD, and cancer mortality. Results A total of 41 243 participants were eligible for inclusion in the analyses. The average combined average daily intake of fruit, vegetable, and legume was 2.97 [standard deviation (SD) 1.22] servings per day. During a median follow-up of 8.9 years [interquartile range (IQR) 6.7–9.9 years], we recorded 1893 major CVDs, 794 cancer events, and 1324 deaths, with 411 CVD deaths and 429 cancer deaths. In the models adjusted for age, sex, and center (random effect), a higher total intake of fruit, vegetable, and legume was inversely associated with CVD mortality, cancer incidence, cancer mortality, and all-cause mortality. After adjusting for additional covariates, the associations were evidently attenuated and only the association with all-cause mortality (hazard ratio [HR] trend 0.92, 95% CI 0.86–0.98, p trend = 0.021) remained significant, with a non-significant trend for major CVD (HR trend 1.02, 95% CI 0.97–1.08, p trend = 0.449), CVD mortality (HR trend 0.94, 95% CI 0.84–1.06, p trend = 0.301), cancer incidence (HR trend 0.97, 95% CI 0.89–1.06, p trend = 0.540), or cancer mortality (HR trend 0.92, 95% CI 0.82–1.04, p trend = 0.174). Compared with the reference group, the risk of all-cause mortality was the lowest for four to five servings of total daily intake of fruit, vegetable, and legume (HR 0.73, 95% CI 0.55–0.97), and did not show a further decrease for the higher intake group. Separately, fruit intake was associated with a lower risk of all-cause mortality (HR trend 0.92, 95% CI 0.86–0.99, p trend = 0.020) and legume intake was associated with a lower risk of major CVD (HR trend 0.95, 95% CI 0.90–0.99, p trend = 0.028) and all-cause mortality (HR trend 0.94, 95% CI 0.89–0.99, p trend = 0.020) in the fully adjusted models. Conclusions This prospective study suggests that Chinese people with daily consumption of four to five servings (equivalent to 500–625 g/day) of fruit, vegetable, and legume demonstrated the lowest mortality, which conveys an encouraging message to the public that lifestyle modification to increase fruit, vegetable, and legume intakes may have greater beneficial effects on reducing all-cause mortality.

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky&ndash, Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

Common maize seeds and silage maize seeds are similar in appearance and are difficult to identify with the naked eye. Four varieties of common maize seeds and four varieties of silage maize seeds were identified by near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics. The pixel-wise principal component analysis was used to distinguish the differences among different varieties of maize seeds. The object-wise spectra of each single seed sample were extracted to build classification models. Support vector machine (SVM) and radial basis function neural network (RBFNN) classification models were established using two different classification strategies. First, the maize seeds were directly classified into eight varieties with the prediction accuracy of the SVM model and RBFNN model over 86%. Second, the seeds of silage maize and common maize were firstly classified with the classification accuracy over 88%, then the seeds were classified into four varieties, respectively. The classification accuracy of silage maize seeds was over 98%, and the classification accuracy of common maize seeds was over 97%. The results showed that the varieties of common maize seeds and silage maize seeds could be classified by NIR-HSI combined with chemometrics, which provided an effective means to ensure the purity of maize seeds, especially to isolate common seeds and silage seeds.

We aim to evaluate the association of systolic and diastolic blood pressure (SBP and DBP) with estimated urinary sodium (Na) and potassium(K) excretions, and their gram-to-gram Na/K ratio across various salt-diet regions during 2005–2009 in China. A prospective cohort study was conducted to recruit 46,285 participants in China. A single fasting morning urine specimen was collected to estimate 24-hour urinary Na and K excretion using Kawasaki formula. Means of estimated Na and K were 5.7 ± 1.7 and 2.1 ± 0.5 grams/day, respectively, and mean estimated Na/K ratio was 2.8 ± 0.8. Adjusted analyses showed 1.70 mmHg SBP and 0.49 mmHg DBP increase per 1-g increment of estimated Na, while 1.10 mmHg SBP and 0.91 mmHg DBP decrease for one-gram increase of K. A significant increase in SBP (4.33 mmHg) and DBP (1.54 mmHg) per 1 unit increase in Na/K ratio was observed. More changes of SBP (4.39 mmHg) and DBP (1.67 mmHg) per one-unit increase of Na/K ratio were observed in low-salt regions, though significant changes were also found in moderate- and heavy-salt regions (P for heterogeneity

Background The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases ( CVD s) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity‐related anthropometric indices (body mass index, waist circumference, hip circumference, waist‐to‐hip ratio, waist‐to‐height ratio) in Chinese population. Methods and Results A total of 44 048 participants in the study were derived from the baseline data of the PURE ‐China (Prospective Urban and Rural Epidemiology) study in China. All participants’ anthropometric parameters, CVD s, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity‐related anthropometric indices to the cardiometabolic risk factors and CVD s. A positive association was observed between each anthropometric index and cardiometabolic risk factors and CVD s in all models ( P AUC s) for predicting dyslipidemia ( AUC s: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia ( AUC s: 0.595, sensitivity: 60%, specificity: 45%), and CVD s ( AUC s: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure ( AUC s: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or CVD s than that of any other traditional obesity‐related indices. Conclusions Waist‐to‐height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and CVD s, while waist circumference had a better prediction for abnormal blood pressure.

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874&ndash, 1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874–1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

Introduction Social capital, characterised by trust, reciprocity and cooperation, is positively associated with a number of health outcomes. We test the hypothesis that among hypertensive individuals, those with greater social capital are more likely to have their hypertension detected, treated and controlled.\ud \ud Methods Cross-sectional data from 21 countries in the Prospective Urban and Rural Epidemiology study were collected covering 61 229 hypertensive individuals aged 35–70 years, their households and the 656 communities in which they live. Outcomes include whether hypertensive participants have their condition detected, treated and/or controlled. Multivariate statistical models adjusting for community fixed effects were used to assess the associations of three social capital measures: (1) membership of any social organisation, (2) trust in other people and (3) trust in organisations, stratified into high-income and low-income country samples.\ud \ud Results In low-income countries, membership of any social organisation was associated with a 3% greater likelihood of having one’s hypertension detected and controlled, while greater trust in organisations significantly increased the likelihood of detection by 4%. These associations were not observed among participants in high-income countries.\ud \ud Conclusion Although the observed associations are modest, some aspects of social capital are associated with better management of hypertension in low-income countries where health systems are often weak. Given that hypertension affects millions in these countries, even modest gains at all points along the treatment pathway could improve management for many, and translate into the prevention of thousands of cardiovascular events each year.

Background Most cardiovascular diseases occur in low‐ and middle‐income regions of the world, but the socioeconomic distribution within China remains unclear. Our study aims to investigate whether the prevalence of cardiovascular diseases differs among high‐, middle‐, and low‐income regions of China and to explore the reasons for the disparities. Methods and Results We enrolled 46 285 individuals from 115 urban and rural communities in 12 provinces across China between 2005 and 2009. We recorded their medical histories of cardiovascular diseases and calculated the INTERHEART Risk Score for the assessment of cardiovascular risk‐factor burden, with higher scores indicating greater burden. The mean INTERHEART Risk Score was higher in high‐ and middle‐income regions than in low‐income regions (9.47, 9.48, and 8.58, respectively, P P trend =0.0064). In high‐ and middle‐income regions, urban communities have higher INTERHEART Risk Score and higher prevalent rate than rural communities. In low‐income regions, however, the prevalence of total cardiovascular disease was similar between urban and rural areas despite the significantly higher INTERHEART Risk Score for urban settings. Conclusions We detected an inverse trend between risk‐factor burden and cardiovascular disease prevalence in urban and rural communities in high‐, middle‐, and low‐income regions of China. Such asymmetry may be attributed to the interregional differences in residents’ awareness, quality of healthcare, and availability and affordability of medical services.

Digital, Methods We assessed fruit and vegetable consumption using data from country-specific, validated semi-quantitative food frequency questionnaires in the Prospective Urban Rural Epidemiology (PURE) study, which enrolled participants from communities in 18 countries between Jan 1, 2003, and Dec 31, 2013. We documented household income data from participants in these communities; we also recorded the diversity and non-sale prices of fruits and vegetables from grocery stores and market places between Jan 1, 2009, and Dec 31, 2013. We determined the cost of fruits and vegetables relative to income per household member. Linear random effects models, adjusting for the clustering of households within communities, were used to assess mean fruit and vegetable intake by their relative cost. Findings Of 143 305 participants who reported plausible energy intake in the food frequency questionnaire, mean fruit and vegetable intake was 3·76 servings (95% CI 3·66–3·86) per day. Mean daily consumption was 2·14 servings (1·93–2·36) in low-income countries (LICs), 3·17 servings (2·99–3·35) in lower-middle-income countries (LMICs), 4·31 servings (4·09–4·53) in upper-middle-income countries (UMICs), and 5·42 servings (5·13–5·71) in high-income countries (HICs). In 130 402 participants who had household income data available, the cost of two servings of fruits and three servings of vegetables per day per individual accounted for 51·97% (95% CI 46·06–57·88) of household income in LICs, 18·10% (14·53–21·68) in LMICs, 15·87% (11·51–20·23) in UMICs, and 1·85% (−3·90 to 7·59) in HICs (ptrend=0·0001). In all regions, a higher percentage of income to meet the guidelines was required in rural areas than in urban areas (p, Ciencias Médicas y de la Salud

BACKGROUND: Several international guidelines recommend the consumption of two servings of fruits and three servings of vegetables per day, but their intake is thought to be low worldwide. We aimed to determine the extent to which such low intake is related to availability and affordability. METHODS: We assessed fruit and vegetable consumption using data from country-specific, validated semi-quantitative food frequency questionnaires in the Prospective Urban Rural Epidemiology (PURE) study, which enrolled participants from communities in 18 countries between Jan 1, 2003, and Dec 31, 2013. We documented household income data from participants in these communities; we also recorded the diversity and non-sale prices of fruits and vegetables from grocery stores and market places between Jan 1, 2009, and Dec 31, 2013. We determined the cost of fruits and vegetables relative to income per household member. Linear random effects models, adjusting for the clustering of households within communities, were used to assess mean fruit and vegetable intake by their relative cost. FINDINGS: Of 143 305 participants who reported plausible energy intake in the food frequency questionnaire, mean fruit and vegetable intake was 3·76 servings (95% CI 3·66-3·86) per day. Mean daily consumption was 2·14 servings (1·93-2·36) in low-income countries (LICs), 3·17 servings (2·99-3·35) in lower-middle-income countries (LMICs), 4·31 servings (4·09-4·53) in upper-middle-income countries (UMICs), and 5·42 servings (5·13-5·71) in high-income countries (HICs). In 130 402 participants who had household income data available, the cost of two servings of fruits and three servings of vegetables per day per individual accounted for 51·97% (95% CI 46·06-57·88) of household income in LICs, 18·10% (14·53-21·68) in LMICs, 15·87% (11·51-20·23) in UMICs, and 1·85% (-3·90 to 7·59) in HICs (ptrend=0·0001). In all regions, a higher percentage of income to meet the guidelines was required in rural areas than in urban areas (p