The incidence of prostate cancer(PCa)within Asian population used to be much lower than in the Western population;however,in recent years the incidence and mortality rate of PCa in some Asian countries have grown rapi...The incidence of prostate cancer(PCa)within Asian population used to be much lower than in the Western population;however,in recent years the incidence and mortality rate of PCa in some Asian countries have grown rapidly.This collaborative report summarized the latest epidemiology information,risk factors,and racial differences in PCa diagnosis,current status and new trends in surgery management and novel agents for castration-resistant prostate cancer.We believe such information would be helpful in clinical decision making for urologists and oncologists,health-care ministries and medical researchers.展开更多
Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial ...Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society.Advances in observation technology have improved the ability to acquire long-term,cross-scale,massive,heterogeneous,and multi-source data.Ecological resource research is entering a new era driven by big data.Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data.Deep learning is a method for automatically extracting complex high-dimensional nonlinear features,which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data.To promote the application of deep learning in the field of ecological resource research,here,we first introduce the relationship between deep learning theory and research on ecological resources,common tools,and datasets.Second,applications of deep learning in classification and recognition,detection and localization,semantic segmentation,instance segmentation,and graph neural network in typical spatial discrete data are presented through three cases:species classification,crop breeding,and vegetation mapping.Finally,challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning.It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms,and enrich applications with the development of hardware.展开更多
基金supported by the Program for Changjiang Scholars and Innovative Research Team in University scheme of the Ministry of Education of China(NO.IRT1111)the National Basic Research Program of China(2012CB518300)+2 种基金the National Natural Science Foundation of China(81101946)the Shanghai Pujiang Program(12PJD008)Prostate Cancer Foundation Young Investigator Award,Shanghai Municipal Health and Family Planning Commission Outstanding Young Investigator(XYQ2013077).
文摘The incidence of prostate cancer(PCa)within Asian population used to be much lower than in the Western population;however,in recent years the incidence and mortality rate of PCa in some Asian countries have grown rapidly.This collaborative report summarized the latest epidemiology information,risk factors,and racial differences in PCa diagnosis,current status and new trends in surgery management and novel agents for castration-resistant prostate cancer.We believe such information would be helpful in clinical decision making for urologists and oncologists,health-care ministries and medical researchers.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19050401)the National Natural Science Foundation of China(Grant Nos.31971575&41871332)。
文摘Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society.Advances in observation technology have improved the ability to acquire long-term,cross-scale,massive,heterogeneous,and multi-source data.Ecological resource research is entering a new era driven by big data.Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data.Deep learning is a method for automatically extracting complex high-dimensional nonlinear features,which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data.To promote the application of deep learning in the field of ecological resource research,here,we first introduce the relationship between deep learning theory and research on ecological resources,common tools,and datasets.Second,applications of deep learning in classification and recognition,detection and localization,semantic segmentation,instance segmentation,and graph neural network in typical spatial discrete data are presented through three cases:species classification,crop breeding,and vegetation mapping.Finally,challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning.It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms,and enrich applications with the development of hardware.
基金The project of Hebei Administration of Traditional Chinese Medicine (Grant No. 2020167)the project of the Second Hospital of Hebei Medical University (Grant No. 2HS202014)the Chinese Medicine Association (Grant No. CMEI2019KPYJ00142)
