【目的】前期研究发现经吡虫啉处理的意大利蜜蜂Apis mellifera ligustica(简称“意蜂”)工蜂学习能力下降,转录组学分析表明王浆主蛋白1(major royal jelly protein 1,MRJP1)基因在吡虫啉处理的蜜蜂脑中显著下调,MRJP1可能参与调控蜜...【目的】前期研究发现经吡虫啉处理的意大利蜜蜂Apis mellifera ligustica(简称“意蜂”)工蜂学习能力下降,转录组学分析表明王浆主蛋白1(major royal jelly protein 1,MRJP1)基因在吡虫啉处理的蜜蜂脑中显著下调,MRJP1可能参与调控蜜蜂学习能力。本研究旨在采用RNA干扰(RNA inference,RNAi)技术将Mrjp1特异性沉默,验证MRJP1在意蜂工蜂嗅觉学习中的关键作用。【方法】通过克隆技术获得Mrjp1基因cDNA序列,经测序验证后,设计引物,合成用于RNAi干扰Mrjp1基因表达的dsRNA。注射dsMrjp1的意蜂工蜂作为处理组(dsMrjp1注射组),注射dsEGFP的意蜂工蜂作为对照组(dsEGFP注射组),随后通过伸吻反应(proboscis extension response,PER)实验比较两组的嗅觉学习与记忆能力差异。最后采用实时荧光定量PCR(quantitative real-time PCR,qRT-PCR)检测注射dsMrjp1后意大利蜜蜂工蜂脑中Mrjp1的相对表达量。【结果】dsMrjp1注射组与dsEGFP注射组意蜂工蜂学习能力差异显著,dsMrjp1注射组意蜂工蜂的学习能力显著降低。学习后2 h,两组意蜂工蜂的记忆力无显著差异。qRT-PCR结果显示Mrjp1的表达水平在dsMrjp1注射组意蜂工蜂脑中显著低于dsEGFP注射组,表明学习能力降低的处理组意蜂脑内对应的Mrjp1表达水平也降低。【结论】通过RNAi抑制意蜂工蜂Mrjp1基因的表达后,其嗅觉学习能力受到显著性抑制,但记忆力未受到显著影响,提示Mrjp1可能是调控意蜂学习的重要基因之一。本研究结果有助于后续进一步研究蜜蜂嗅觉学习相关的分子机制。展开更多
In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of...In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 60874098 and 60911130129)the High-Tech Research and Development Program (863) of China (No. 2007AA042103)+1 种基金the National Creative Research Groups Science Foundation of China (No. 60721062)the Project of Introducing Talents for Chinese University Disciplinal Innovation (111 Project, No. B07031)
文摘In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.
