Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll.

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Knowledge, attitudes and willingness to organ donation among the general public: a cross-sectional survey in China.

BACKGROUND: The purpose of this study is to assess the level of knowledge, attitudes, and willingness to organ donation among the general public in China. METHODS: The study population consisted of 4274 participants from Eastern, Central and Western China. The participants' knowledge, attitudes and willingness to organ donation were collected by a self-designed questionnaire consisting of 30 items. Knowledge is measured by 10 items and presented as a 10 point score, attitudes is measured by 20 items using a 5-step Likert scale and total score ranged between 0 and 80; while the willingness to donate is assessed as binary variable (0 = No; 1 = Yes). A logistic regression model was used to assess the association of knowledge and attitudes with willingness to organ donation, controlling for demographic and socioeconomic confounders. RESULTS: The questionnaire response rate was 94.98%. The mean score (± SD) of the general public's knowledge to organ donation was 6.84 ± 1.76, and the mean score (± SD) of attitudes to organ donation was 47.01 ± 9.07. The general public's knowledge and attitudes were the highest in Eastern China, followed by West and Central China. The logistic regression model indicated a positive association between knowledge and the willingness to organ donation (OR = 1.12, 95%CI: 1.08, 1.17; P < 0.001); attitudes were also positively potential determinant of more willingness to organ donation (OR = 1.08, 95%CI: 1.07, 1.09; P < 0.001). CONCLUSIONS: Knowledge and attitudes were found to be positively associated with the Chinese general public's willingness to organ donation. Knowledge about the concept of brain death and the transplant procedure may help raise the rate of willingness to organ donation.

Development and validation of nomogram for prediction of low birth weight: a large-scale cross-sectional study in northwest China.

BACKGROUND: Birth weight is closely related to infant survival and future health, growth and development. In developing countries, the incidence of low birth weight is twice as high as in developed countries. Due to the low economic and medical level in northwest China, the problem of low birth weight needs to be solved urgently. METHODS: We developed the predictive model based on data sets from a cross-sectional study conducted in northwest China, and data were collected from August 2013 to November 2013. A total of 27,233 patients were included in the study. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the optimal predictive characteristics among risk factors. The selected characteristics in the LASSO regression were used in multivariate logistic regression to build the prediction model. C-index and calibration plot were used to evaluate the degree of discrimination and calibration of the model. The decision curve is used to evaluate the net benefit rate of the application of the predictive tool. Bootstrapping validation was used for internal validation. RESULTS: Nomogram included gestational age, the sex of the attendance, the mother's education level, antenatal care, the mother's occupation, pregnancy-induced hypertension, family income, exposure to pesticides and nutritional supplements. The C-index of the predicted nomogram was 0.698(95% confidence interval: 0.671-0.725), C-index of internal verification was 0.694, indicating that the model had a good identification ability. Calibration plot showed that the model had good calibration. Decision curve indicated that patients with a threshold probability of low birth weight between 1% and 71% would benefit more from using the prediction tool. CONCLUSION: The use of this predictive model will contribute to clinicians and pregnant women to make personalized predictions easily and quickly so that early lifestyle detection and medical intervention can be undertaken by physicians and patients.

Maternal exposure to life events during pregnancy and congenital heart disease in offspring: a case-control study in a Chinese population.

BACKGROUND: Previous studies have suggested that maternal stress could increase the risk of some adverse pregnancy outcomes, but evidence on congenital heart disease (CHD) is limited. We aimed to explore the association between maternal exposure to life events during pregnancy and CHD in offspring. METHODS: The data was based on an unmatched case-control study about CHD conducted in Shaanxi province of China from 2014 to 2016. We included 2280 subjects, 699 in the case group and 1581 in the control group. The cases were infants or fetuses diagnosed with CHD, and the controls were infants without any birth defects. The life events were assessed by the Life Events Scale for Pregnant Women, and were divided into positive and negative events for synchronous analysis. A directed acyclic graph was drawn to screen the confounders. Logistic regression was employed to estimate the odds ratio and 95% confidence interval for the effects of life events on CHD. RESULTS: After controlling for the potential confounders, the pregnant women experiencing the positive events during pregnancy had lower risk of CHD in offspring than those without positive events (OR = 0.38, 95%CI: 0.30 ~ 0.48). The risk of CHD in offspring could increase by 62% among the pregnant women experiencing the negative events compared to those without (OR = 1.62, 95%CI: 1.29 ~ 2.03). Both effects showed a certain dose-response association. Besides, the positive events could weaken the risk impact of negative events on CHD. CONCLUSION: It may suggest that maternal exposure to negative life events could increase the risk of CHD in offspring, while experiencing positive events could play a potential protective role.

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

Long-term BMI change trajectories in Chinese adults and its association with the hazard of type 2 diabetes: evidence from a 20-year China Health and Nutrition Survey.

INTRODUCTION: To investigate the relationship between long-term change trajectory in body mass index (BMI) and the hazard of type 2 diabetes among Chinese adults. RESEARCH DESIGN AND METHODS: Data were obtained from the China Health and Nutrition Survey (CHNS). Type 2 diabetes was reported by participants themselves in each survey wave. The duration of follow-up was defined as the period from the first visit to the first time self-reported type 2 diabetes, death, or other loss to follow-up from CHNS. The patterns of change trajectories in BMI were derived by latent class trajectory analysis method. The Fine and Gray regression model was used to estimate HRs with corresponding 95% CIs for type 2 diabetes. RESULTS: Four patterns of the trajectories of change in BMI were identified among Chinese adults, 42.7% of participants had stable BMI change, 40.8% for moderate BMI gain, 8.9% for substantial BMI gain and 7.7% for weight loss. During the follow-up with mean 11.2 years (158 637 person-years contributed by 14 185 participants), 498 people with type 2 diabetes (3.7%) occurred. Risk of type 2 diabetes was increased by 47% among people who gained BMI more substantially and rapidly (HR: 1.47, 95% CI 1.08 to 2.02, p=0.016) and increased by 20% among those in people with the moderate BMI gain (HR: 1.20, 95% CI 0.98 to 1.48, p=0.078), compared with those with stable BMI change. CONCLUSIONS: Long-term substantial gain of BMI was significantly associated with an increased risk of type 2 diabetes in the Chinese adults.

Bisphenol A and the Risk of Obesity a Systematic Review With Meta-Analysis of the Epidemiological Evidence.

Bisphenol A (BPA) is suspected to be associated with several chronic metabolic diseases. The aim of the present study was to review previous epidemiological studies that examined the relationship between BPA exposure and the risk of obesity. PubMed, Web of Science, and Embase databases were systematically searched by 2 independent investigators for articles published from the start of database coverage until January 1, 2020. Subsequently, the reference list of each relevant article was scanned for any other potentially eligible publications. We included observational studies published in English that measured urinary BPA. Odds ratios with corresponding 95% confidence intervals for the highest versus lowest level of BPA were calculated. Ten studies with a sample size from 888 to 4793 participants met our inclusion criteria. We found a positive correlation between the level of BPA and obesity risk. A dose-response analysis revealed that 1-ng/mL increase in BPA increased the risk of obesity by 11%. The similar results were for different type of obesity, gender, and age.