Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to pro...Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.展开更多
Objective:The presence of complex components in Chinese herbal medicine(CHM)hinders identification of the primary active substances and understanding of pharmacological principles.This study was aimed at developing a ...Objective:The presence of complex components in Chinese herbal medicine(CHM)hinders identification of the primary active substances and understanding of pharmacological principles.This study was aimed at developing a big-data-based,knowledgedriven in silico algorithm for predicting central components in complex CHM formulas.Methods:Network pharmacology(TCMSP)and clinical(GEO)databases were searched to retrieve gene targets corresponding to the formula ingredients,herbal components,and specific disease being treated.Intersections were determined to obtain diseasespecific core targets,which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component.The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula,and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components.The established method was tested on the traditional CHM formula Danggui Sini Decoction(DSD)for gastric cancer.Finally,the effects of the predicted critical component were experimentally validated in gastric cancer cells.Results:An algorithm called Chinese Herb Medicine-Formula vs.Ingredients Efficacy Fitting&Prediction(CHM-FIEFP)was developed.Ferulic acid was identified as having the highest fitting score among all tested DSD components.The pharmacological effects of ferulic acid alone were similar to those of DSD.Conclusions:CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases.This approach may also be practical for solving other similarly complex problems.The algorithm is available at http://chm-fiefp.net/.展开更多
基金supported by the project funded by International Research Center of Big Data for Sustainable 740 Development Goals[Grant Number CBAS2022GSP07]Fundamental Research Funds for the Central Universities,Chongqing Natural Science Foundation[Grant Number CSTB2022NSCQMSX 2069]Ministry of Education of China[Grant Number 19JZD023].
文摘Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.
基金funded by the National Natural Science Foundation of China(Grant Nos.82204694 and 81572416)the Tianjin Health and Family Planning Commission Program(Grant No.ZC20169)the Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A)。
文摘Objective:The presence of complex components in Chinese herbal medicine(CHM)hinders identification of the primary active substances and understanding of pharmacological principles.This study was aimed at developing a big-data-based,knowledgedriven in silico algorithm for predicting central components in complex CHM formulas.Methods:Network pharmacology(TCMSP)and clinical(GEO)databases were searched to retrieve gene targets corresponding to the formula ingredients,herbal components,and specific disease being treated.Intersections were determined to obtain diseasespecific core targets,which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component.The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula,and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components.The established method was tested on the traditional CHM formula Danggui Sini Decoction(DSD)for gastric cancer.Finally,the effects of the predicted critical component were experimentally validated in gastric cancer cells.Results:An algorithm called Chinese Herb Medicine-Formula vs.Ingredients Efficacy Fitting&Prediction(CHM-FIEFP)was developed.Ferulic acid was identified as having the highest fitting score among all tested DSD components.The pharmacological effects of ferulic acid alone were similar to those of DSD.Conclusions:CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases.This approach may also be practical for solving other similarly complex problems.The algorithm is available at http://chm-fiefp.net/.
