RESUMO
Plant-derived natural compounds are significant resources for the discovery of potential anticancer drugs. While research in the plant-based anticancer field has surged in recent years, systematic bibliometric analyses covering a longer period and containing up-to-date publications remain scarce. Here, we conducted a bibliometric analysis of literature on the anticancer properties of plant natural compounds over the past three decades, leveraging the bibliometric framework and open-access platform, KNIME. Our findings showed that the number of plant anticancer-related publications underwent an accelerating growth from 1992 to 2023. The country and institution analyses revealed that countries with traditional medical systems contributed a large portion of publications in the plant anticancer field, such as India, China, and South Korea. This study also highlighted the top ten eminent researchers and publications, assisting researchers in identifying pivotal literature. The primary publications were domains of chemistry and biology-related fields, such as Pharmacology & Pharmacy, Plant Sciences, and Biochemistry & Molecular Biology. Additionally, we noted that flavonoids have been focal plant compounds in anticancer, with strong anticancer potential. Our study provides new insights into the progress and trends in the plant anticancer field and will assist researchers in grasping the future research direction.
RESUMO
Accurately capturing the urbanization process is essential for planning sustainable cities and realizing the United Nations Sustainable Development Goal 11. However, until recently, most of the studies on urban expansion in the world have focused on area growth but have little knowledge of height dynamics. This study mapped the spatial distribution of urban built-up areas (UBA) in the Yangtze River Delta (YRD), one of the most urbanized regions in China, to investigate the spatio-temporal evolution in both the horizontal and vertical directions from 1990 to 2020. We coupled and analyzed the horizontal and vertical urban expansion from the 3-D perspective and identified the dominant types. The results showed that 30 cities (73.17 % of the total number of cities) were increasing in the 3-D combined expansion intensity. The decreasing cities were mainly located in Anhui Province. Despite the increasing number of skyscrapers, horizontal growth has dominated urban expansion over the past three decades. The UBA area of the YRD has grown from 4,855.30 km2 to 44,447.15 km2, while the average building height has slowly decreased by 1.26 m. Significant unevenness and differences existed in horizontal and vertical expansions of varying provinces and cities. Our study can accurately grasp the 3-D urban expansion process in the YRD and could promote the efficient development and sustainable utilization of urban land resources in China and beyond.
RESUMO
Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.
Assuntos
Triticum , Zea mays , Tecnologia de Sensoriamento Remoto/métodos , Grão Comestível , ChinaRESUMO
It is objective needs during utilization and management of regional cultivated land resource to use remote sensing to accurately and efficiently retrieve the status of cultivated land fertility at county level and realize the gradation of cultivated land rapidly. In this study, with Dongping County as a case, using Landsat TM satellite imagery and cultivated land fertility evaluation data, the moisture vegetation fertility index (MVFI) was constructed based on surface water capacity index (SWCI) and normalized difference vegetation index (NDVI), and then the optimal inversion model was optimized to obtain the best inversion model, which was further applied and verified at the county scale. The results showed that the correlation coefficient between MVFI and integrated fertility index (IFI) was -0.753, which could comprehensively reflect the growth of winter wheat, soil moisture and land fertility, and had clear biophysical significance. The best inversion model was the quadratic model, with high inversion accuracy. This model was suitable for the inversion of cultivated land fertility in the county. The spatial distribution and uniformity of the inversion results were similar to the results of soil fertility evaluation. The area differences between the high, medium and low grades were all less than 2.9%. This study provided a remote sensing inversion method of cultivated land fertility based on the feature space theory, which could effectively improve the evaluation efficiency and prediction accuracy of cultivated land fertility at the county scale.
