Gongju(Chrysanthemum morifolium) is one of the five major medicinal Chrysanthemum varieties included in the Chinese Pharmacopoeia. In recent years, its cultivation areas have changed significantly, resulting in mixed quality of the medicinal herbs. In this study, Gongju cultivated in Anhui, Yunnan, Chongqing, and other places were selected as research objects. Hyperspectral data were collected in the visible-near-infrared(VNIR) and short-wave infrared(SWIR) bands using different modes, such as corolla facing up(A) and flower base facing up(B). After pre-processing the hyperspectral data using five methods, including multiplicative scatter correction(MSC), Savitzky-Golay smoothing(SG), first derivative(D1), second derivative(D2), and standard normal variate(SNV), partial least squares discriminant analysis(PLSDA), random forest(RF), and support vector machine(SVM) were used to establish origin identification models of Gongju at the two geographical scales of the province and the city-county in Anhui province. The accuracy of the prediction results was used as an evaluation index to select the optimal models, and the classification performance of the models was evaluated by confusion matrix. The results showed that the flower base facing up(B) collection model combined with second derivative pretreatment and RF method was the best model for both geographical scale identification models. The modeling effect of the full-band(VNIR + SWIR) was slightly better than that of the single band, with the accuracy of the prediction set in the province and city-county regions reaching 99.69% and 99.40%, respectively. The competitive adaptive reweighted sampling algorithm(CARS), successive projections algorithm(SPA), and variable iterative space shrinkage approach(VISSA) were further used to screen the feature wavelength modeling. The number of feature wavelengths screened by CARS was fewer, and the prediction set accuracy of the two geographical scales models after optimization could reach 99.56% and 98.65%, which was basically comparable to the full-band model. However, the removal of redundant variables could greatly reduce the complexity of the model. The hyperspectral technology combined with the chemometrics model established in this study can achieve the origin identification of Gongju at different geographical scales, providing a theoretical basis and technical reference for the construction of a rapid detection system for Gongju origin and the development of exclusive miniaturized instrumentation and equipment systems.
Chrysanthemum/growth & development* ; China ; Support Vector Machine ; Geography ; Discriminant Analysis ; Spectroscopy, Near-Infrared/methods* ; Spectrum Analysis/methods* ; Drugs, Chinese Herbal/analysis* ; Least-Squares Analysis

In recent years, the research focus on determinants of chronic non-communicable diseases has shifted from non-spatial factors (e.g., lifestyle behaviors) to spatial factors (e.g., natural and built environments). As the intersection of lifecourse epidemiology and spatial epidemiology, spatial lifecourse epidemiology is a research area in the field of health geography. It combines advanced spatial technologies, including geographic information systems, surveying, remote sensing, location-based services and artificial intelligence, to accurately retrace, measure, and simulate individuals' exposures across the life course (i.e., exposome); and adopts lifecourse models, including the accumulation of risk model and critical/sensitive period models, to investigate the impact of individuals' exposures in the past on their health status at different stages of life. This paper introduces the theories, main analysis approaches and focus of spatial lifecourse epidemiology in the research of chronic non-communicable diseases for the purpose of better understanding and applications of spatial lifecourse epidemiology in the research of determinants of chronic non-communicable diseases, providing important reference for future research, facilitating the development of health geography to eventually achieve precise health management over the lifecourse.
Artificial Intelligence ; Epidemiology ; Forecasting ; Geography ; Health Status ; Humans ; Noncommunicable Diseases/epidemiology*

This paper established the identification technology of the main root origin of three-year-old spring Panax notoginseng aiming at providing theoretical basis for the protection and traceability of geographical indication products of P. notoginseng. Forty-four samples of three-year-old spring P. notoginseng from Guangxi Baise, Yunnan Wenshan, Yunnan new cultivating regions. The stable isotopic ratios of carbon, nitrogen, hydrogen and oxygen were determined by elemental analysis and stable isotope mass spectrometer. Combined with Duncan multiple comparative analysis, fisher discriminant analysis and sequential discriminant analysis, a origin discriminant model for the main root of three-year-old spring P. notoginseng was established for 3 production areas of P. notoginseng. The geographical climate and environment of three production areas of P. notoginseng are obviously different. From Guangxi Baise-Yunnan Wenshan-Yunnan new cultivating regions, the longitude, average annual temperature and annual precipitation gradually decrease, and the elevation and latitude are increasing. The results of multiple comparative analysis showed that there were significant or very signi-ficant differences in the δ~(13)C,δ~(15)N,δ~2H,δ~(18)O of the main roots of P. notoginseng in three regions. The results of fisher's discriminant analysis and sequential discriminant analysis showed that the correct discriminant rates of the main roots of P. notoginseng for three regions were 80.05%,76.47% and 90.91%, respectively, based on four stable isotope ratios, with an average of 84.09%. Using stable isotope fingerprint and chemometrics method, we can distinguish the origin of the main raw materials and products of P. notoginseng.
China ; Geography ; Isotopes ; Panax notoginseng ; Seasons

Phylogeography is a research hotspot in the field of the genetic diversity and core germplasm construction of endangered rare plants. Paris polyphylla var. yunnanensis is a rare plant species mainly distributed in China. Wild individuals have been overexploited for the last few decades because of increasing demand for such medicines. Therefore, it is great significance to study the phylogeography of P. poliphylla var. yunnanensis based on chloroplast gene trnL-trnF sequences. In this study, chloroplast genes trnL-trnF were used in the phylogeography analysis of 15 wild and 17 cultivated populations of P. polyphylla var. yunnanensis. This study revealed that based on the results of neutrality tests and mismatch analysis, the rapid expansion of wild population has not been detected in P. polyphylla var. yunnanensis. After aligning and sorting the obtained cpDNA sequences, a total of 15 haplotypes were detected in all 32 populations. One haplotype was unique to the wild population, and 5 haplotypes were unique to the cultivated population. It can be seen that the haplotype richness of cultivated population was higher than that of wild population. The wild populations of P. polyphylla var. yunnanensis were divided into two groups according to evolutionary relationship of haplotypes and distribution map of haplotypes. The haplotype of branch Ⅰ was mainly distributed in Guizhou, and the haplotype of branch Ⅱ was located in Yunnan and Huidong, Sichuan. Therefore, it's speculated that Guizhou and the west Yunnan region may be glacial refuge in the evolutionary history of wild populations of P. polyphylla var. yunnanensis, and in order to protect the wild resources more effectively, wild populations of P. polyphylla var. yunnanensis in these two areas should be included in the protection zone.
China ; Genes, Chloroplast ; Humans ; Liliaceae/genetics* ; Melanthiaceae ; Phylogeography

The research on the historical production areas of Dao-di herbs is of great value to the quality evaluation, production base and protection for geographic indications of Dao-di herbs. Current studies mostly focused on the sorting of written sources, but neglected the excavation of image records such as historical maps and topographic maps. This paper aims at exploreing the geographical scope and evolution mode of historical production area of the Dao-di herbs by combining the method of historical map labelling with the traditional literature and historical research methods. It can be divided into three steps: production area name extraction, historical map labelling and textual research on the historical production area of Dao-di herbs. This method may provide a better way to show the geographical scope and topographic features of the historical production areas of Dao-di herbs through historical maps labelling, which is essential to further explore the evolution of production areas of Dao-di herbs from ancient times to the present by comparing historical maps of different periods, and may be helpful to discover the reasons for the formation and evolution of historical producing areas of Dao-di herbs from different perspectives such as environment, climate, humanities, economy, policy, etc. In addition, the historical map database can be used for map labelling to help establish the relationship between the dynasties, historical names, and change cha-racteristics of the scope of the historical production areas of Dao-di herbs in the following research.
Drugs, Chinese Herbal ; Geography ; Medicine, Chinese Traditional ; Plants, Medicinal ; Research Design

BACKGROUND: Current studies on the COVID-19 depicted a general incubation period distribution and did not examine whether the incubation period distribution varies across patients living in different geographical locations with varying environmental attributes. Profiling the incubation distributions geographically help to determine the appropriate quarantine duration for different regions. METHODS: This retrospective study mainly applied big data analytics and methodology, using the publicly accessible clinical report for patients (n = 543) confirmed as infected in Shenzhen and Hefei, China. Based on 217 patients on whom the incubation period could be identified by the epidemiological method. Statistical and econometric methods were employed to investigate how the incubation distributions varied between infected cases reported in Shenzhen and Hefei. RESULTS: The median incubation period of the COVID-19 for all the 217 infected patients was 8 days (95% CI 7 to 9), while median values were 9 days in Shenzhen and 4 days in Hefei. The incubation period probably has an inverse U-shaped association with the meteorological temperature. The warmer condition in the winter of Shenzhen, average environmental temperature between 10 °C to 15 °C, may decrease viral virulence and result in more extended incubation periods. CONCLUSION Case studies of the COVID-19 outbreak in Shenzhen and Hefei indicated that the incubation period of COVID-19 had exhibited evident geographical disparities, although the pathological causality between meteorological conditions and incubation period deserves further investigation. Methodologies based on big data released by local public health authorities are applicable for identifying incubation period and relevant epidemiological research.
Adolescent ; Adult ; Aged ; COVID-19/prevention & control* ; Child ; China/epidemiology* ; Female ; Geography ; Humans ; Infectious Disease Incubation Period ; Male ; Middle Aged ; Quarantine ; Retrospective Studies ; SARS-CoV-2 ; Young Adult

The present study aimed to provide the protection strategies for wild germplasm resources of original plants of Viticis Fructus and a theoretical basis for the sustainable use of Viticis Fructus. The genetic diversity and genetic structures of the 232 indivi-duals in 19 populations of Vitex rotundifolia and V. trifolia were analyzed by eight SSR markers with tools such as Popgene32, GenAlex 6.502, and STRUCTURE. Bottleneck effect was detected for the population with more than 10 individuals. The results indicated that 42 and 26 alleles were detected from the populations of V. rotundifolia and V. trifolia, respectively, with average expected heterozygo-sities of 0.448 6 and 0.583 9, which are indicative of low genetic diversity. AMOVA revealed the obvious genetic variation of V. rotundifolia and V. trifolia within population(84.43%, P<0.01; 60.37%, P<0.01). Furthermore, in eight SSR loci, six from V. rotundifolia populations and two from V. trifolia populations failed to meet Hardy-Weinberg equilibrium expectations(P<0.05), which confirmed that the populations experienced bottleneck effect. As assessed by Mantel test, geographical distance posed slight impacts on the genetic variation between the populations of V. rotundifolia and V. trifolia. Principal component analysis(PCA) and STRUCTURE analysis demonstrated evident introgression of genes among various populations. The original plants of Viticis Fructus were confirmed low in genetic diversity and genetic differentiation level. Therefore, the protection of wild resources of original plants of Viticis Fructus should be strengthened to ensure its sustainable use.
Alleles ; Fruit/genetics* ; Genetic Variation ; Geography ; Microsatellite Repeats ; Vitex/genetics*

In this study, data of amino acids of Cordyceps samples from Qinghai and Tibet was analyzed with self-organizing map neural network. A model of XY-Fused network was established with the content of 8 major amino acids and total amino acids for the identification of geographical origins of Cordyceps from Qinghai and Tibet. It had the prediction accuracy of 83.3% for the test set. In addition, data mining indicated that methionine was a special kind of amino acid in Cordyceps which could serve as a marker to identify its geographical origins. On this basis, the content ratio of methionine to total amino acids was proposed to be a quantifiable indicator to distinguish Cordyceps from Qinghai and Tibet.
Amino Acids ; Cordyceps/genetics* ; Geography ; Neural Networks, Computer ; Tibet

BACKGROUND: Regional disparities in the working conditions of medical doctors have not been fully assessed in Japan. We aimed to clarify these differences in hospital characteristics: doctors' workload, wages, and popularity among medical students by city population sizes. METHODS: We targeted 423 teaching hospitals certified by the Japanese Society of Internal Medicine and assessed the working conditions of physicians specializing in internal medicine. We calculated their workload (the annual number of discharged patients per physician) and retrieved data on junior residents' monthly wages from the Resinavi Book which is popular among medical students in Japan to know the teaching hospital's information and each hospital's website. Furthermore, we explored the interim matching rate of each hospital as its popularity among medical students. Next, we classified cities in which all hospitals were located into eight groups based on their population size and compared the characteristics of these hospitals using a one-way analysis of variance. RESULTS: The average workload was 110.3, while the average workload in hospitals located in most populated cities (≥ 2,000,000) was 88.4 (p < 0.05). The average monthly wage was 351,199 Japanese yen, while that in most populated cities was 305,635.1 Japanese yen. The average popularity (matching rate) was 101.9%, and the rate in most populated areas was 142.7%, which was significantly higher than in other areas. CONCLUSIONS Hospitals in most populated areas had significantly lower workloads and wages; however, they were more popular among medical students than those in other areas. This study was the first to quantify the regional disparities in physicians' working conditions in Japan, and such disparities need to be corrected.
Cities/statistics & numerical data* ; Geography ; Hospitals, Teaching/statistics & numerical data* ; Japan ; Physicians/statistics & numerical data* ; Population Density ; Salaries and Fringe Benefits/statistics & numerical data* ; Students, Medical/psychology* ; Workload/statistics & numerical data*

PURPOSE: Geographical analysis is becoming a powerful tool for evaluating the quality of medical services and acquiring fundamental data for medical decision-making. Using geographical analysis, we evaluated the impact of the distance from patients' homes to the hospital on their participation in outpatient cardiac rehabilitation (OCR). METHODS: All patients hospitalized for percutaneous coronary intervention, coronary artery bypass grafting, valvular surgery, congestive heart failure, and aortic diseases were advised to participate in an OCR program after discharge. Using the dataset of our cohort study of OCR from 2004 to 2015 (n = 9,019), we used geographical analysis to investigate the impact of the distance from patients' homes to hospital on their participation in our OCR program. RESULTS: Patients whose road distance from home to hospital was 0-10 km, 10-20 km, and 20-30 km participated more in OCR than those whose road distance was ≧ 30 km (OR 4.34, 95% CI 3.80-4.96; OR 2.98, 95% CI 2.61-3.40; and OR 1.90, 95% CI 1.61-2.23, respectively). Especially in patients with heart failure, the longer the distance, the lesser the participation rate (P < .001). CONCLUSIONS: Using geographical analysis, we successfully evaluated the factors influencing patients' participation in OCR. This illustrates the importance of using geographical analysis in future epidemiological and clinical studies. TRIAL REGISTRATION UMIN000028435.
Aged ; Aged, 80 and over ; Cardiac Rehabilitation/statistics & numerical data* ; Female ; Geography ; Humans ; Japan ; Male ; Middle Aged ; Outpatients/statistics & numerical data* ; Patient Participation/statistics & numerical data* ; Prospective Studies ; Spatial Analysis