The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-sympto...The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.展开更多
为获得发酵特征满足肉类发酵剂要求同时具有抗氧化能力的潜力乳酸菌,从采集于四川省眉山等5市的7个传统腌腊肉样品中筛选乳酸菌,经16S r DNA鉴定,挑选出8株(L1-L8)乳酸菌,对其主要发酵特征和抗氧化能力进行评估。结果表明:有6株菌的主...为获得发酵特征满足肉类发酵剂要求同时具有抗氧化能力的潜力乳酸菌,从采集于四川省眉山等5市的7个传统腌腊肉样品中筛选乳酸菌,经16S r DNA鉴定,挑选出8株(L1-L8)乳酸菌,对其主要发酵特征和抗氧化能力进行评估。结果表明:有6株菌的主要发酵特征满足肉类发酵剂要求,即具有蛋白酶活性,不产NH3、生物胺、H2S和CO2,能够耐受6%Na Cl和150 mg/kg Na NO2,并能够在10~30℃生长;该6株菌的细胞悬浮液和无细胞提取液均具有清除羟自由基和DPPH自由基的能力,尤以L6的菌体作用最强,清除率分别达52.48%和24.16%;该6株菌细胞悬浮液和无细胞提取液均具有较强还原能力,而L2、L3、L4和L5则具有较强的抑制脂质过氧化能力,以L5最高,可达63.36%。综合来看,筛选菌株中L4既满足肉类发酵剂的要求,又具有较强抗氧化能力,有望开发成具有抗氧化能力的肉类发酵剂。展开更多
基金the National Key Research and Development Program(No.2018YFB1307005)the Smart Medical Project of Shanghai Municipal Commission of Health and Family Planning(No.2018ZHYL0226)。
文摘The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.
文摘为获得发酵特征满足肉类发酵剂要求同时具有抗氧化能力的潜力乳酸菌,从采集于四川省眉山等5市的7个传统腌腊肉样品中筛选乳酸菌,经16S r DNA鉴定,挑选出8株(L1-L8)乳酸菌,对其主要发酵特征和抗氧化能力进行评估。结果表明:有6株菌的主要发酵特征满足肉类发酵剂要求,即具有蛋白酶活性,不产NH3、生物胺、H2S和CO2,能够耐受6%Na Cl和150 mg/kg Na NO2,并能够在10~30℃生长;该6株菌的细胞悬浮液和无细胞提取液均具有清除羟自由基和DPPH自由基的能力,尤以L6的菌体作用最强,清除率分别达52.48%和24.16%;该6株菌细胞悬浮液和无细胞提取液均具有较强还原能力,而L2、L3、L4和L5则具有较强的抑制脂质过氧化能力,以L5最高,可达63.36%。综合来看,筛选菌株中L4既满足肉类发酵剂的要求,又具有较强抗氧化能力,有望开发成具有抗氧化能力的肉类发酵剂。
