[Research on fast detecting tomato seedlings nitrogen content based on NIR characteristic spectrum selection].

In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.

[Study on germination rate of zoysia (Zoysia japonica Steud.) seeds using near infrared reflectance spectroscopy].

With 37 zoysia seed samples with different germination rates ranging from 58.5% to 92%, harvested in different years from 2009 to 2011 and from different locations of China, a model for determining germination rate of zoysia seeds was tried to be built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). All the seeds samples were divided into two groups: calibration set (including 28 samples) and validation set (including 9 samples). The results showed that with the spectral range from 6 000 to 7 000 cm(-1) and 6 main components, there was a better fitting between the predictive value and true value. Determination coefficients (R2) of calibration and validation sets are 90.73% and 91.80%, the coefficients of correlation are 0.986 6 and 0.987 2, the standard errors are 9.80 and 9.47, and the average absolute errors are 7.64% and 6.98% respectively. Even with different calibration samples, the models have a high determination coefficient (R2 over building of NIR model for determining 90%), low standard errors (about 10.00) and low absolute errors (about 8.00%). The building of NIR model for determining germination rate of zoysia seeds could promote the application of high quality seeds in production.

[Study on varieties identification of Kentucky bluegrass using hyperspectral imaging and discriminant analysis].

Hyperspectral images of six varieties of Kentucky bluegrass were acquired using hyperspectral imager (550-1 000 nm) and the leaf spectral properties were extracted. Wilks' lambda stepwise method was used and 9 optimal wavelengths were selected from the original 94 wavelengths and the discriminant models for varieties identification of Kentucky bluegrass were built based on Fisher' s linear discriminant function. The results showed that the Fisher' s linear discriminant model with 9 wavelengths achieved classification accuracies of 100% for both training and testing samples. While for the models with three wavelengths and six wavelengths, classification accuracies reached 83.3% and 96.7% for the testing samples, respectively. It indicates that hyperspectral images combined with discriminant analysis might be a good method to identify the varieties of Kentucky bluegrass.

County-based demographics, patterns of treatment seeking, and disease burden for malignant tumor patients in China.

BACKGROUND: The mortality of malignant tumors in rural areas of China in 2015 was significantly higher than that in city areas (213.6 per 100,000 vs. 191.5 per 100,000), bringing huge economic pressure to individuals and the local community. The comprehensive reform of county-level public hospitals in Yun County has helped patients with critical disease receive appropriate treatment. Therefore, an analysis focused on the epidemiology and disease burden of malignant tumors in Yun County could provide guidance for administrators and health practitioners in other counties. METHODS: This retrospective database study extracted data from the Yun County medical community (including two higher level hospitals: Yun County People's Hospital and Yun County Chinese Medicine Hospital, and 13 township central hospitals) from 1st Jul 2017 to 30th Aug 2020. Patients diagnosed as having a malignant tumor were enrolled and those with abnormal key baseline information were excluded. The epidemiology and disease burden for malignant tumor patients were assessed. RESULTS: A total of 3,792 patients were enrolled, and the most prevalent cancer in 2018 was thyroid (35.4 patients/100,000) and in 2019 this was lung cancer (30.6 patients/100,000). The mean outpatient visits per person for all-cause and tumor-specific visits were 9.99 and 3.94 visits across 2018 and 2019, respectively, and the mean inpatient visits per person in both years were the same at 2.56 visits. Total costs were 14.471 and 20.29 million in 2018 and 2019, respectively, and 71.7% and 73.0% of the total cost were covered by medical insurance over the 2 years. CONCLUSIONS: The medical environment has improved since medical system reform commenced in 2019 in Yun County, and medical insurance has decreased the disease burden for patients and their families significantly.

China county-based demographics, clinical characteristics, treatment patterns, and health resource consumption analysis for diabetes.

BACKGROUND: The diagnosis and treatment of diabetes depends on reasonable chronic disease management. Compared with urban areas, county areas are high-risk areas for chronic disease patients, due to the low awareness of chronic disease, poor treatment compliance with chronic disease, low drug persistence, and low cure rate. Therefore, more attention should be paid to chronic disease management in county areas. METHODS: This retrospective, observational study was conducted at the Yun county medical community, Yun county, Yunan province. Data were collected from the medical records of diabetic patients from July 2017 to Aug 2020. The primary outcome variable was the proportion of patients with diabetic complications in county areas. The secondary outcome variables were demographics and clinical characteristics of diabetic patients in county areas, achievement of the HbA1c target, and clinical inertia of diabetic patients in county areas. Comparisons of the simple diabetes group and the diabetic kidney disease (DKD) group in terms of demographics, clinical characteristics, treatment patterns, and health resource consumption were also conducted. A series of appropriate statistical tests were applied to the study population to examine the various outcomes. RESULTS: A total of 9,721 type 2 diabetic patients were included for the study analysis. Diabetic retinopathy (11.83%), cerebrovascular disease (10.31%), and DKD (9.29%) were the 3 most common complications in overall admissions. Among the 1,347 patients with HbA1c test results, 536 (39.8%) patients achieved the HbA1c target, while 566 (87.62%) of the 661 patients who did not achieve the HbA1c target had clinical inertia during the next 6 months. Compared with simple diabetes patients, patients with DKD had a higher age, wider coverage of medical insurance, and longer duration of diabetes, and were more likely to be complicated with hyperuricemia, dyslipidemia, and hypertension. Regular insulin, metformin, alpha-glucosidase inhibitor, and sulfonylurea were the most widely used antidiabetic drugs in patients with DKD. The health resources consumption also significantly increased. CONCLUSIONS: The proportion of complications in diabetic patients is high in county areas, and blood glucose control is still insufficient. Chronic complications are the key reasons for the decrease in quality of life and high medical costs.

Five 11α, 12α-epoxy pentacyclic triterpenoid saponins with antithrombus activities from Glechoma longituba.

The present study reports the phytochemical investigation of n-butanol-soluble extracts of Glechoma longituba. Five new oleanane-type triterpenoid saponins with an 11α, 12α-epoxy unit, named glechomanosides A - E, were isolated from the n-butanol soluble fraction of G. longituba. Their chemical structures were established using HRESIMS, IR, 1D NMR, and 2D NMR techniques. The compounds were all evaluated for their antithrombus activities by monitoring thrombin time (TT), prothrombin time (PT), activated partial thromboplastin time (APTT), and antiplatelet aggregation assays. These results suggest that G. longituba might be a candidate plant source of an interesting antithrombotic activity.

[Spectrum analysis of crop and weeds at seedling].

The infestation information on field weeds is the basis of variable spraying herbicides. It was found that the method using the spectral characteristics of plant is superior in real-time respect. The Fourier transform infrared spectrum technique was applied to measure the reflectance of wheat and weeds in the range from 700 to 1100 nm. The discrimination analysis was done using the SPSS software. Firstly, the source spectrum data were compressed and normalized. Secondly, the characteristic wavelengths were selected by using stepwise method. Thirdly, the discrimination model was set up to use the selected wavelengths as the variables for detecting wheat and weeds. It was shown by the result of discrimination analysis that the correct classification rate of wheat and weeds detection with the selected wavelength points achieved 97%. In addition, the selected wavelength points were marked in the "red edge" of reflectance within some range, and the rate of correct classification increased with the increase in the numbers of the selected wavelength points. According to the selected wavelength points, the proper filters were chosen to perform the multi-spectral images captured and processed with the machine vision system.