交通噪音对中缅树鼩行为、学习记忆和氧化应激的影响
Acta Theriologica Sinica(2021)
Abstract
为探讨噪音刺激对中缅树鼩行为、学习记忆和氧化应激的影响,将中缅树鼩在噪音0 dB、40 dB和80 dB条件下持续刺激8h,连续28 d,测定其体重、摄食量、Y型迷宫正确反应率和行为变化,并测定脑中丙二醛(Malondialdehyde,MDA)、超氧化物歧化酶(Superoxide dismutase,SOD)、乙酰胆碱酯酶(Acetylcholinesterase,AChE)和一氧化氮合酶(Nitric oxide synthase,NOS)浓度,以及SOD、AChE和NOS活性.结果 表明:不同噪音对中缅树鼩的体重影响不同,第28天时3组之间体重差异显著,其中80 dB条件下中缅树鼩体重较低.不同噪音对中缅树鼩摄食量的变化趋势和体重变化相似.强噪音会显著降低中缅树鼩的取食行为和活动行为,增加休息行为,但是对修饰行为的影响不显著.第28天时,不同噪音正确反应率差异显著,其中80 dB条件下正确反应率最低.不同噪音对MDA浓度、SOD浓度和AChE浓度影响差异显著,80 dB条件下MDA浓度与SOD浓度较高,AChE浓度较低;不同噪音对NOS浓度、NOS活性、SOD活性和AChE活性影响极显著,80 dB条件下SOD活性较高,NOS浓度、NOS活性与AChE活性较低.以上结果说明强噪音可降低中缅树鼩的体重、摄食量、取食行为和活动行为,增加休息行为,可诱发中缅树鼩的认知障碍.此外,强噪音刺激会导致中缅树鼩脑组织内氧化应激水平增加和出现不同程度的氧化损伤.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined