To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure ...To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.展开更多
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ...Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.展开更多
BACKGROUND The poly(ADP-ribose)polymerase(PARP)inhibitor olaparib has displayed superior clinical effect in metastatic castration-resistant prostate cancer(mCRPC)patients with the homologous recombination repair(HRR)g...BACKGROUND The poly(ADP-ribose)polymerase(PARP)inhibitor olaparib has displayed superior clinical effect in metastatic castration-resistant prostate cancer(mCRPC)patients with the homologous recombination repair(HRR)genes mutations.However,when a patient’s tumor tissue volume is insufficient for genomic profiling of HRR gene mutations,circulating tumor DNA(ctDNA)may be useful in helping to determine and monitor the efficacy of olaparib,as well as in abiraterone-combination treatment,and for understanding any resistance mechanism related to such mutations.CASE SUMMARY A 61-year-old man who was diagnosed with metastatic prostate adenocarcinoma was initially hormone sensitivity,showing high Gleason score(5+5=10)and absolute positive rate(14/14 biopsied specimens).Following failure of several standard therapies,the patient progressed to mCRPC.Surprisingly,the patient showed good response to olaparib-abiraterone-prednisone combination treatment(an androgen-deprivation therapy,provided as the‘final choice’in China).Serum total prostate-specific antigen(TPSA)level reduced and symptoms remitted for 4 months.However,thereafter,serum TPSA levels began slowly increasing,indicating development of olaparib resistance.Subsequent comprehensive genomic profiling of ctDNA, screening 508 cancer-related genes by next-generation sequencing,identified 10 somatic variants as well as 3 copy number alterations. Two identified reversemissense mutations in partner and localizer of BRCA2 (PALB2) may have recovered the readingframe, restoring function of the primary germline PALB2 mutation and causing resistance to thePARP inhibitor olaparib.CONCLUSIONReverse mutations in PALB2, discovered via genomic profiling of ctDNA, may represent apotential resistance mechanism against olaparib in mCRPC.展开更多
基金Supported by the Shanxi Natural Science Foundation under contract number 20041070 and Natural Science Foundation of north u-niversity of China .
文摘To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.
基金supported in part by the Research on the Application of Multimodal Artificial Intelligence in Diagnosis and Treatment of Type 2 Diabetes under Grant No.2020SK50910in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ60020.
文摘Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.
基金Supported by the Natural Science Foundation of Chongqing,No. cstc2018jcyj AX0781the Major Project of Chongqing Health Committee,No. cstc2016 shmszx130033031+1 种基金the National Natural Science Foundation of China,No. 81302316the Chongqing technological innovation and application development-Major theme projects,No. cstc2019jscxfxydx0008
文摘BACKGROUND The poly(ADP-ribose)polymerase(PARP)inhibitor olaparib has displayed superior clinical effect in metastatic castration-resistant prostate cancer(mCRPC)patients with the homologous recombination repair(HRR)genes mutations.However,when a patient’s tumor tissue volume is insufficient for genomic profiling of HRR gene mutations,circulating tumor DNA(ctDNA)may be useful in helping to determine and monitor the efficacy of olaparib,as well as in abiraterone-combination treatment,and for understanding any resistance mechanism related to such mutations.CASE SUMMARY A 61-year-old man who was diagnosed with metastatic prostate adenocarcinoma was initially hormone sensitivity,showing high Gleason score(5+5=10)and absolute positive rate(14/14 biopsied specimens).Following failure of several standard therapies,the patient progressed to mCRPC.Surprisingly,the patient showed good response to olaparib-abiraterone-prednisone combination treatment(an androgen-deprivation therapy,provided as the‘final choice’in China).Serum total prostate-specific antigen(TPSA)level reduced and symptoms remitted for 4 months.However,thereafter,serum TPSA levels began slowly increasing,indicating development of olaparib resistance.Subsequent comprehensive genomic profiling of ctDNA, screening 508 cancer-related genes by next-generation sequencing,identified 10 somatic variants as well as 3 copy number alterations. Two identified reversemissense mutations in partner and localizer of BRCA2 (PALB2) may have recovered the readingframe, restoring function of the primary germline PALB2 mutation and causing resistance to thePARP inhibitor olaparib.CONCLUSIONReverse mutations in PALB2, discovered via genomic profiling of ctDNA, may represent apotential resistance mechanism against olaparib in mCRPC.
