Despite its apparently simple genetics,cystic fibrosis(CF) is a rather complex genetic disease.A lot of variability in the steps of the path from the cystic fibrosis transmembrane conductance regulator(CFTR) gene to t...Despite its apparently simple genetics,cystic fibrosis(CF) is a rather complex genetic disease.A lot of variability in the steps of the path from the cystic fibrosis transmembrane conductance regulator(CFTR) gene to the clinical manifestations originates an uncertain genotype- phenotype relationship.A major determinant of this uncertainty is the incomplete knowledge of the CFTR mutated genotypes,due to the high number of CFTR mutations and to the higher number of their combinations in trans and in cis.Also the very limited knowledge of functional effects of CFTR mutated alleles severely impairs our diagnostic and prognostic ability.The final phenotypic modulation exerted by CFTR modifier genes and interactome further complicates the framework.The next generation sequencing approach is a rapid,lowcost and high-throughput tool that allows a near complete structural characterization of CFTR mutated genotypes,as well as of genotypes of several other genes cooperating to the final CF clinical manifestations.This powerful method perfectly complements the new personalized therapeutic approach for CF.Drugs active on specific CFTR mutational classes are already available for CF patients or are in phase 3 trials.A complete genetic characterization has been becoming crucial for a correct personalized therapy.However,the need of a functional classification of each CFTR mutation potently arises.Future big efforts towards an ever more detailed knowledge of both structural and functional CFTR defects,coupled to parallel personalized therapeutic interventions decisive for CF cure can be foreseen.展开更多
文摘具有混合记忆的自步对比学习(Self-paced Contrastive Learning,SpCL)通过集群聚类生成不同级别的伪标签来训练网络,取得了较好的识别效果,然而该方法从源域和目标域中捕获的行人数据之间存在典型的分布差异,使得训练出的网络不能准确区别目标域和源域数据域特征。针对此问题,提出了双分支动态辅助对比学习(Dynamic Auxiliary Contrastive Learning,DACL)框架。该方法首先通过动态减小源域和目标域之间的局部最大平均差异(Local Maximum Mean Discrepancy,LMMD),以有效地学习目标域的域不变特征;其次,引入广义均值(Generalized Mean,GeM)池化策略,在特征提取后再进行特征聚合,使提出的网络能够自适应地聚合图像的重要特征;最后,在3个经典行人重识别数据集上进行了仿真实验,提出的DACL与性能次之的无监督域自适应行人重识别方法相比,mAP和rank-1在Market1501数据集上分别增加了6.0个百分点和2.2个百分点,在MSMT17数据集上分别增加了2.8个百分点和3.6个百分点,在Duke数据集上分别增加了1.7个百分点和2.1个百分点。
文摘Despite its apparently simple genetics,cystic fibrosis(CF) is a rather complex genetic disease.A lot of variability in the steps of the path from the cystic fibrosis transmembrane conductance regulator(CFTR) gene to the clinical manifestations originates an uncertain genotype- phenotype relationship.A major determinant of this uncertainty is the incomplete knowledge of the CFTR mutated genotypes,due to the high number of CFTR mutations and to the higher number of their combinations in trans and in cis.Also the very limited knowledge of functional effects of CFTR mutated alleles severely impairs our diagnostic and prognostic ability.The final phenotypic modulation exerted by CFTR modifier genes and interactome further complicates the framework.The next generation sequencing approach is a rapid,lowcost and high-throughput tool that allows a near complete structural characterization of CFTR mutated genotypes,as well as of genotypes of several other genes cooperating to the final CF clinical manifestations.This powerful method perfectly complements the new personalized therapeutic approach for CF.Drugs active on specific CFTR mutational classes are already available for CF patients or are in phase 3 trials.A complete genetic characterization has been becoming crucial for a correct personalized therapy.However,the need of a functional classification of each CFTR mutation potently arises.Future big efforts towards an ever more detailed knowledge of both structural and functional CFTR defects,coupled to parallel personalized therapeutic interventions decisive for CF cure can be foreseen.
