Spatial learning associated
- 格式:pdf
- 大小:1.72 MB
- 文档页数:11
2024年高等教育自学考试自考《英语二》自测试题与参考答案一、阅读判断(共10分)第一题Read the following passage and then answer the questions below.The rise of e-learning has transformed the way people access education. With the advent of the internet, individuals now have the opportunity to learn at their own pace and from the comfort of their homes. One of the most popular forms of e-learning is the self-study examination for higher education, commonly known as the Self-study Examination for English Level Two (Self-study Examination for English Two). This examination is designed to test the English proficiency of students who wish to pursue further education or career opportunities.1、The self-study examination for higher education is gaining popularity due to its convenience and flexibility.2、The Self-study Examination for English Two is only available online.3、The examination is specifically for those who want to advance their education or career.4、The passage mentions that the self-study examination is only for English learning.5、The self-study examination is meant to be taken at a specific location.1、True2、False3、True4、False5、FalseSecond Question: Reading Comprehension and JudgmentPassage:In today’s rapidly evolving world, the role of technology in education cannot be overstated. Technological advancements have not only transformed how knowledge is imparted but also revolutionized the learning experience for students across the globe. The integration of digital tools into classrooms has enabled educators to personalize learning, catering to the diverse needs of individual students. Furthermore, online platforms and digital resources have made education more accessible, breaking down barriers of time and location. However, it is crucial to recognize that these benefits come with challenges such as ensuring equitable access to technology and maintaining the quality of educational content in a digital format. Despite these hurdles, the potential for technology to enhance teaching and learning is immense, making it an indispensable part of modern educational practices.Questions:1、The passage suggests that technology has changed the way education isdelivered.•Answer: True2、According to the text, personalized learning experiences are now possible due to the use of digital tools.•Answer: True3、The integration of technology in education has created insurmountable barriers for learners.•Answer: False4、Online platforms have made it difficult for students to access educational materials.•Answer: False5、The passage acknowledges both the advantages and challenges of incorporating technology into education.•Answer: True二、阅读理解(共10分)Title: Reading ComprehensionPassage:The global pandemic has highlighted the importance of digital literacy in today’s society. Many countries have implemented online learning platforms to ensure that education continues during times of crisis. One such platform, Edutalk, has gained popularity for its interactive and engaging approach to teaching. Edutalk offers a variety of courses, including a specialized courseon digital literacy. This course is designed to teach individuals the skills needed to navigate the digital world effectively and responsibly.The course covers several key topics, such as online communication, internet safety, and digital privacy. It also includes practical exercises that help students apply their knowledge in real-life situations. One of the most innovative features of the course is its virtual classroom, where students can interact with each other and with the instructor through live video calls and chat forums.Despite the benefits of online learning, there are challenges that come with it. One major challenge is the potential for isolation and reduced social interaction. However, Edutalk has addressed this by incorporating social features into its platform, such as discussion groups and peer support networks. These features help to foster a sense of community among the students.Questions:1.What is the primary focus of the specialized course offered by Edutalk?A) Online communicationB) Internet safetyC) Digital privacyD) All of the above2.Which of the following is NOT a key topic covered in the digital literacy course?A) Online communicationB) History of the internetC) Internet safetyD) Digital privacy3.What is one innovative feature of Edutalk’s digital literacy course?A) Traditional classroom settingB) Virtual classroom with live video callsC) Self-paced learning modulesD) Only text-based lessons4.What is a potential challenge associated with online learning, according to the passage?A) High cost of educationB) Reduced social interactionC) Limited access to resourcesD) Lack of hands-on experience5.How does Edutalk address the challenge of reduced social interaction in online learning?A) By offering only self-paced coursesB) By incorporating social features such as discussion groupsC) By encouraging students to attend physical classesD) By providing one-on-one tutoring sessionsAnswers:1.D) All of the above2.B) History of the internet3.B) Virtual classroom with live video calls4.B) Reduced social interaction5.B) By incorporating social features such as discussion groups三、概况段落大意和补全句子(共10分)第一题Reading Passage:In recent years, the importance of lifelong learning has been increasingly recognized in the field of education. Higher education self-study examinations, such as the National Self-Study Examination for English Level Two, have become a popular method for individuals to enhance their English proficiency independently. This essay discusses the benefits and challenges of self-study examinations in higher education.Questions:1、The passage mainly focuses on the topic of __________.A)The benefits of traditional classroom learningB)The challenges of self-study in higher educationC)The role of self-study examinations in enhancing English proficiencyD)The decline of traditional educational methods2、According to the passage, self-study examinations are particularly beneficial for__________.A)Students who prefer a more structured learning environmentB)Working professionals seeking to improve their language skillsC)Young learners who are eager to learn new subjectsD)Teachers who want to teach English more effectively3、The passage suggests that self-study examinations can be challenging due to__________.A)The lack of immediate feedback from teachersB)The difficulty of maintaining a consistent study scheduleC)The need for self-discipline and motivationD)The limited availability of study materials4、Which of the following is NOT mentioned as a challenge of self-study examinations?A)The potential for procrastinationB)The risk of not receiving a formal degreeC)The need for a strong support systemD)The difficulty of accessing advanced learning resources5、The author concludes the passage by suggesting that __________.A)Self-study examinations should be replaced with traditional classroom learningB)Self-study examinations can be effective when combined with online resourcesC)The challenges of self-study examinations outweigh their benefitsD)Self-study examinations are only suitable for individuals with exceptionalself-disciplineAnswers:1、C2、B3、C4、B5、B第二题阅读内容:In recent years, online learning has gained immense popularity due to its convenience and flexibility. This essay discusses the advantages and disadvantages of online learning, comparing it with traditional classroom teaching.概况段落大意和补全句子:1、The paragraph mainly focuses on the increasing popularity of online learning, its benefits, and its comparison with traditional classroom teaching.2、Online learning is popular because it offers convenience and flexibility.3、One of the advantages of online learning is its accessibility from anywhere.4、However, one disadvantage of online learning is the lack of face-to-face interaction.5、Compared to traditional classroom teaching, online learning provides more convenience but may lack some social aspects.四、填空补文(共10分)Four. Fill in the blanks with the appropriate options.Read the following passage:The ancient Egyptians had a profound influence on the world we live in today. Their achievements in mathematics, architecture, and religion are still celebrated. One of their greatest contributions was the development of the first known calendar. This calendar was based on the observation of the stars and the cycles of the Nile River. The Egyptians divided the year into three seasons: Inundation, Growth, and Harvest. Each season was further divided into four months, with each month consisting of 30 days. This system was used for agricultural planning and religious festivals.Choose the correct word to fill in the blanks from the options below:1.The Egyptians divided the year into three distinct __________.a) seasonsb) monthsc) daysd) nights2.The calendar they developed was based on __________.a) the moon’s phasesb) the Nile River’s flowc) the Gregorian calendard) the Chinese zodiac3.Each month in the Egyptian calendar had__________days.a) 28b) 30c) 31d) 294.The__________season was characterized by the flooding of the Nile.a) Growthb) Inundationc) Harvestd) Autumn5.The Egyptian calendar was important for __________.a) astronomical observationsb) religious ceremoniesc) trade and commerced) all of the aboveAnswer Key:1.a) seasons2.b) the Nile River’s flow3.b) 304.b) Inundation5.d) all of the above五、填词补文(共15分)第一题阅读以下短文,根据上下文填入合适的单词,每空一词。
名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱(共包含二级节点15 个,三级节点93 个)间序列分析)监督学习)领域 二级分类 三级分类。
提名人简介仇子龙博士,男,1976年12月出生。
2009年回国后担任中国科学院上海生命科学研究院神经科学研究所研究员至今,主要从事自闭症、瑞特综合征等神经发育疾病的生物学研究,研究成果阐述了神经发育疾病的遗传、分子与神经环路机制,并建立了自闭症的非人灵长类动物模型。
在Nature, Developmental Cell, Molecular Psychiatry, Current Opinion in Neurobiology等国际生物学权威期刊上发表研究论文与应邀综述十余篇,引用逾两千余次。
自闭症的非人灵长类动物模型工作入选科技部2016年“中国科学十大进展”,中国科协2016年“中国生命科学十大进展”。
仇子龙研究员的工作围绕MECP2基因,从非人灵长类动物模型到分子细胞机制,获得了一系列原创性成果,代表性工作包括:1、自闭症相关基因MeCP2调控microRNA核内剪切加工与神经系统发育仇子龙研究员的工作发现MeCP2蛋白直接参与小RNA (microRNA)的核内剪切加工过程,而与其传统的转录调控功能无关。
此工作为自闭症相关蛋白MeCP2的功能研究提供了崭新的角度,进而提出神经发育性疾病的致病机理很可能与大脑中microRNA表达失调密切相关,为DNA甲基化与microRNA两种表观遗传学调控建立联系的同时,也为开展转化医学研究提供了理论依据。
2、自闭症的非人灵长类动物模型仇子龙研究员与神经所非人灵长类转基因平台合作,开展了自闭症的非人灵长类动物模型构建工作。
通过构建携带人类自闭症基因MECP2的转基因猴及对转基因猴进行分子遗传学与行为学分析,历时5年的工作发现MECP2转基因猴表现出类人类自闭症病人的重复运动模式、焦虑水平上升、刻板行为与社交障碍等行为表型。
研究团队还通过精巢异体移植与体外受精等方法,成功的得到了携带人类MECP2基因的第二代转基因猴,且发现其在社交行为方面也表现出了严重障碍。
山东省卫生计生系统人员“针对性普法”系统考试时间60分钟;总分100分;剩余时间分秒您的考试成绩:0分1护士执业资格考试成绩于考试结束后____个工作日内公布。
()A.30B.45C.60D.90标准答案:B考生答案:2医疗、保健机构应当为孕产妇提供下列医疗保健服务外,不应____。
( )A.定期进行产后访视,指导产妇科学喂养婴儿B.提供避孕咨询指导和技术服务C.对产妇及其家属进行生殖健康教育和科学育儿知识教育D.为了增加医院收入可放宽对孕产妇进行不必要的医学检查标准答案:D考生答案:3乙型肝炎的主要传播途径有____。
()A.经血传播、围生期母婴传播B.性传播C.密切接触传播D.以上均包括标准答案:A考生答案:4申请举办美容医疗机构的单位或者个人,应按照本办法以及《医疗机构管理条例》和《医疗机构管理条例实施细则》的有关规定办理设置审批和登记注册手续。
卫生行政部门自收到合格申办材料之日起____日内做出批准或不予批准的决定,并书面答复申办者。
()A.10B.15C.20D.30标准答案:D考生答案:5医疗机构的印章、银行帐户、牌匾以及医疗文件中使用的名称应当与核准登记的医疗机构名称相同;使用两个以上的名称的,应当____。
( )A.与第一名称相同B.与第二名称相同C.合并第一与第二名称D.同时使用两个名称标准答案:A考生答案:6医疗美容服务实行____,医疗美容项目必须由主诊医师负责或在其指导下实施。
()A.首诊医师负责制B.主诊医师负责制C.上级医师负责制D.主任医师负责制标准答案:B考生答案:7术后首次病程记录完成时限为____。
()A.术后6小时B.术后8小时C.术后10分钟D.术后即刻标准答案:D考生答案:8对考核不合格的医师,卫生行政部门可以责令其暂停执业活动____个月,并接受培训和继续医学教育。
()A.1-2个月B.1-3个月C.2-3个月D.3-6个月标准答案:D考生答案:9人体器官移植技术临床应用与伦理委员会中从事人体器官移植的医学专家不超过委员人数的____。
介绍记忆宫殿的英语文章作文The method of loci, often referred to as the Memory Palace technique, is a powerful mnemonic device dating back to ancient Roman and Greek times. It leverages the spatial memory of our brains to enhance recall by associating information with specific locations in a mental journey. This technique has been used for centuries by scholars, orators, and students to memorize vast amounts of information, from speeches and presentations to entire books.To understand how the Memory Palace technique works, imagine yourself entering a familiar place—a house, for instance, with several rooms. Each room represents a "locus" or a stop in your mental journey. To memorize a list of items, you mentally place each item at a different location within this house. For example, if you need to remember a shopping list consisting of milk, eggs, and bread, you might visualize a carton of milk on the doorstep, eggs on the dining table, and bread in the kitchen sink.The key to this technique lies in the vividness of your mental imagery and the association between the item you want to remember and the location where you place it. Our brains are naturally adept at remembering spatial details, so by linking new information with familiar spatial contexts, we significantly enhance our ability to recall that information later.Historically, the Memory Palace technique was famously used by the ancient Greek poet Simonides. Legend has it that Simonides was able to identify all the guests at a banquet after the roof collapsed by mentally retracing his steps through the room and remembering where each guest had been seated. This incident marked a pivotal moment in the development of mnemonic techniques, demonstrating the power of spatial memory in aiding recall.In practical terms, constructing a Memory Palace involves selecting a place you know well, such as your home or a familiar route you walk frequently. You mentally divide this space into specific locations or "loci" where you will place the informationyou want to remember. The more detailed and distinctive each location is in your mind, the easier it becomes to recall the information associated with it.Moreover, the Memory Palace technique is not limited to spatial locations alone; it can also incorporate sensory details such as sounds, smells, and textures. By engaging multiple senses in your mental journey, you create a richer and more robust network of associations, further reinforcing your memory retrieval.Neuroscientific research supports the effectiveness of the Memory Palace technique. Studies have shown that spatial memory activates different regions of the brain, particularly the hippocampus, which plays a crucial role in memory formation and spatial navigation. By harnessing these cognitive processes, individuals can significantly improve their memory retention and retrieval capabilities.In conclusion, the Memory Palace technique is a proven method for enhancing memory through spatial and associative learning. By leveraging the natural strengths of our brain's spatial memory, this technique allows us to memorize and recall vast amounts of information with remarkable accuracy. Whether you are a student preparing for exams, a professional memorizing a speech, or simply looking to improve your memory skills, the Memory Palace offers a structured and effective approach to mastering the art of memory.。
Neuroscience Letters404(2006)208–212Enriched environment experience overcomes the memory deficits and depressive-like behavior induced by early life stress Minghu Cui a,1,Ya Yang b,c,1,Jianli Yang a,Jichuan Zhang b,c,Huili Han b,c,Wenpei Ma b,c,d, Hongbin Li b,c,Rongrong Mao b,c,Lin Xu a,b,c,Wei Hao a,∗,Jun Cao b,c,∗∗a Mental Health Institute and WHO Collaborating Center for Psychosocial Factors,Drug Abuse and Health,the2nd Hospital ofCentral South University,Changsha410011,PR Chinab Key Laboratory of Animal Models and Human Disease Mechanisms,and Laboratory of Learning and Memory,Kunming Institute of Zoology,the Chinese Academy of Sciences,Kunming650223,PR Chinac Graduate School of the Chinese Academy of Sciences,Beijing100039,PR Chinad Department of Physiology,Kunming Medical College,Kunming650031,PR ChinaReceived16January2006;received in revised form23May2006;accepted26May2006AbstractStress in early life is believed to cause cognitive and affective disorders,and to disrupt hippocampal synaptic plasticity in adolescence into adult, but it is unclear whether exposure to enriched environment(EE)can overcome these effects.Here,we reported that housing rats in cages with limited nesting/bedding materials on postnatal days2–21reduced body weight gain,and this type of early life stress impaired spatial learning and memory of the Morris water maze and increased depressive-like behavior of the forced swim test in young adult rats(postnatal days53–57).Early life stress also impaired long-term potentiation in hippocampal CA1area of slices of young adult rats.Remarkably,EE experience on postnatal days 22–52had no effect on spatial learning/memory and depressive-like behavior,but it significantly facilitated LTP in control rats,and completely overcame the effects of early life stress on young adult rats.Thesefindings suggest that EE experience may be useful for clinical intervention in preventing cognitive and affective disorders during development.©2006Elsevier Ireland Ltd.All rights reserved.Keywords:Enriched environment;Memory;Long-term potentiation;Hippocampus;Forced swim;Stress;Morris water mazeStress is known to influence synaptic plasticity[13,16,28]and hippocampal functions,leading to deficits of learning and mem-ory[7]and affective disorders[8],which can be overcome by the treatment of nicotine or antidepressant[1,24].The stress effects on adult rats are generally transient[18],while early life stress impairs hippocampal functions in adolescence and the impair-ments are even worsened in adult[6].Social/environmental stress in early life evoked by daily handling,repeated maternal separation or reared in limiting nesting/bedding material causes ∗Corresponding author.Tel.:+867315292156.∗∗Corresponding author at:Key Laboratory of Animal Models and Human Disease Mechanisms,and Laboratory of Learning and Memory,Kunming Insti-tute of Zoology,the Chinese Academy of Sciences,Kunming650223,PR China. Tel.:+868715139165;fax:+868715191823.E-mail addresses:weihao57@(W.Hao),juncao@ (J.Cao).1These authors equally contributed to this work.neuroendocrine changes and/or cognitive deficits[3,12].Ani-mal model and human studies implicate that early life stress increases vulnerability of the adolescent or adult to depres-sion[11,22].In addition,brain imaging studies demonstrate that smaller hippocampal volume is associated with the patients who suffered stress-related psychiatric illnesses,including depres-sion and posttraumatic stress disorder[23].Synaptic plasticity such as long-term potentiation(LTP)is believed to be the mechanism underlying certain type of learn-ing and memory[5].And the hippocampus-dependent spatial learning and memory are correlated closely with hippocam-pal LTP,impairment of which often leads to memory deficits. Environmental enrichment(EE)is defined as a combination of“complex inanimate objects and social stimulation”[26], and evidence demonstrates that EE experience enhances hip-pocampal LTP and spatial learning in adult rats[9,14].How-ever,whether EE experience can overcome memory deficits and higher depressive-like behavior caused by early life stress is0304-3940/$–see front matter©2006Elsevier Ireland Ltd.All rights reserved. doi:10.1016/j.neulet.2006.05.048M.Cui et al./Neuroscience Letters404(2006)208–212209unclear.Here,we studied the effects of early life stress on spa-tial memory,depressive-like behavior and hippocampal LTP,and then examined whether EE experience can prevent these effects.Experimental protocols were approved by Chinese Academy of Sciences,PR China.Male littermates were divided into groups to enable each group to have the same genetic background. The early life stress protocol was modified from previous report in which it could raise plasma corticosterone and reduce body weight gain of the offspring[27].Briefly,pups and dam were housed in standard cages(60cm×40cm×25cm)with lim-ited nesting/bedding materials for postnatal days2–21(early life stress,Str).The nesting/bedding material was one paper towel used by the dam to construct a rudimentary nest area. Control group(Ctr)was housed in standard condition contained about0.33cubic feet of chips.On day22to52,Ctr(n=46)and Str(n=45)were further divided into two subgroups,continu-ously housed in standard condition(Ctr,n=24;Str,n=22)or in enriched environment(EE)(Ctr-EE,n=22;Str-EE,n=23). EE consisted of a larger cage(60×50×70cm)with an extra level constructed by galvanized wire mesh that was connected tofloor by ramps of the same material,and contained wood shavings,a running wheel,a shelter,plastic color toys and small constructions(chain,swing and tunnels).The shelter and run-ning wheel were kept in the cage,while toys and constructions were changed once a week.Moreover,feeding boxes and water bottles were placed in different location once a week to trigger foraging and explorative behaviors.On postnatal days53–57, the four groups of young adult rats(Ctr,Str,Ctr-EE and Str-EE) were divided into three subgroups and subjected to the measure-ments of Morris water maze,forced swim test and hippocampal long-term potentiation,respectively.The Morris water maze[17]consisted of a circular pool(250cm diameter,60cm deep at the side),filled with water(25±1◦C)to a depth of20cm.A Perspex platform (13cm×13cm)was placed at the middle of a quadrant,1–2cm below water surface that was covered withfloating tiny black resin beads.Yellow curtains were drawn around the pool(50cm from the pool periphery)and contained distinctive visual marks severed as distal cues.Animals(Ctr,Str,Ctr-EE,Str-EE,n=8 per group)were trained in spatial learning task of the Morris water maze,120s per trial and4trials per day with30min inter-trial intervals for consecutive4days[30].If a rat did not climb onto the hidden platform within120s,the rat was guided to it.If a rat climbed onto the platform within120s,the rat was allowed to stay on the platform for30s before being returned to its home cage.The hidden platform was removed on day5,and mem-ory retrieval was examined by a probe trial that lasted for180s. An automatic tracking system was used to record swim path, latency and distance escaped to the hidden platform or crossed the quadrant where the hidden platform had been placed.Depressive-like behavior was examined in forced swim test [20].Each rat(Ctr,n=10;Str,n=9;Ctr-EE,n=9;Str-EE,n=9) was placed into a vertical Plexiglas cylinder(65cm high,25cm in diameter)contained40cm depth of water at25◦C,for15min. Twenty-four hours later,each rat was replaced into the cylinder for5min,and the total time when rats was not struggling for escape but remained minimal activity(head and forepaw)to keep respiration,passivelyfloating in the water,was measured as immobility.Immobility was used to indicate depressive-like behavior of rats[20].The hippocampal long-term potentiation(LTP)was exam-ined in CA1area of the young adult slices(Ctr,n=6;Str, n=5;Ctr-EE,n=5;Str-EE,n=6),similar as those described previously[25].Brains were rapidly removed and placed in a vibroslicer chamber with ice-cold artificial cerebral spinalfluid (ACSF).Coronary slices(400-m thick)were cut and trans-ferred into an incubation chamber submersed with300ml ACSF at35±1◦C for1h recovery.The ACSF contained(in mM): NaCl120,KCl2.5,NaHCO326,NaH2PO41.25,CaCl21.19, MgSO42.0,and d-glucose10and saturated by gas mixture of 95%O2and5%CO2.Then,the slice was gently transferred into a recording chamber,and was held submerged between two nylon nets and maintained at room temperature(22–25◦C). The recording chamber consisted of a circular well of low vol-ume(1–2ml)and constantly perfused by ACSF at aflow rate of 4–5ml/min.Recording electrode(2–3M resistance)filled with3M NaCl was placed at the stratum radiatum of the hippocampal CA1area to record thefield excitatory postsynaptic poten-tials(fEPSPs),in response to stimulation of the Commis-sural/Schafferfibers.Input/output curves were measured and baseline recordings were adjusted to evoke approximately50% of the maximum fEPSP amplitude.After30min baseline record-ings(one sweep per30s),a single train of high frequency stimulation(HFS,100Hz,1s)was applied to induce LTP that was recorded for60min.The magnitude of LTP was the average of the last5min(55–60min after HFS)of the fEPSPs ampli-tude and expressed as mean±S.E.M.%of the baseline fEPSPs amplitude.Student’s t-test was used to analyze the significance within group,and LSD following one-way ANOV A was used to ana-lyze the difference among groups.Repeated-measures analysis of variance(ANOV A)was used to analyze Morris water maze performance of all experimental groups.Significant level was set at p<0.05.Early life stress on postnatal days2–21decreased body weight gain compared with control(Str,n=46,32.51±5.05g; Ctr,n=45,45.73±4.25g,p<0.01).This effect was prevented by EE experience on postnatal days23–53(Str-EE,n=23, 85.50±6.75g;Ctr-EE,n=22,92.08±7.02g,p>0.05).Fur-thermore,early life stress impaired spatial learning task of the Morris water maze as indicated by longer latencies to escape onto a hidden platform(n=8per group,F(3,31)=45.93,p<0.05, Ctr versus Str,Fig.1A).Although EE experience had no effect on spatial learning(p>0.05,Ctr versus Ctr-EE),but it restored the spatial learning impaired by early life stress,to an extent similar as that of control(p>0.05,Ctr versus Str-EE).Early life stress also impaired memory retrieval in probe trial(Ctr, 71.43±6.73s;Str,50.05±6.07s,F(3,31)=5.512,*p<0.05,Ctr versus Str,Fig.1B)as indicated by shorter time spent in target quadrant where the hidden platform was placed during train-ing.Similarly,EE experience had no effect on memory retrieval (Ctr-EE,82.07±3.18s,p>0.05,Ctr versus Ctr-EE,Fig.1B), but it completely restored the memory retrieval impaired by early210M.Cui et al./Neuroscience Letters404(2006)208–212Fig.1.Early life stress impaired learning and memory while EE experience overcame this effect.(A)Early life stress caused rats to spend longer time to escape onto a hidden platform.EE experience did not influence the escape laten-cies of control,but restored the early life stress-induce longer latencies to control levels.(B)Early life stress impaired memory retrieval as indicated by less time spent in the target quadrant.EE experience had no effect on control,but reversed the early life stress-induce impairment of memory retrieval to an extent similar as control.(C)Swim speed was not different among groups.*p<0.05vs.Ctr. Ctr:control;Str:early life stress;EE:enriched environment experience;Ctr-EE: control rats exposed to EE;Str-EE:early life stressed rats exposed to EE.life stress to control level(Str-EE,72.96±7.53s,p>0.05Ctr versus Str-EE;Fig.1B).The effects of early life stress and/or EE experience on spatial learning and memory were not due to the changes in motor activity because the swim speed was not different among groups(F(3,31)=0.508,p>0.05;Fig.1C).Depressive-like behavior was measured for5min on post-natal day54by immobility time,after forced swim training for15min on postnatal day53.We found that early life stress enhanced depressive-like behavior as indicated by longer immo-bility compared with control(Ctr,n=10,222.25±10.08s;Str, n=9,256.75±9.12s,F(3,48)=4.205,*p<0.05Ctr versus Str, Fig.2).EE experience had no effect on immobility(Ctr-EE, n=9,210.02±8.05s,p>0.05Ctr versus Ctr-EE),but it restored the immobility increased by early life stress to control level(Str-EE,n=9,218.50±10.10s,p>0.05Ctr versus Str-EE).The induction of hippocampal LTP in CA1area of young rat slices was examined on postnatal days53–57.We found that early life stress impaired the induction of LTP compared with control(Ctr,n=6,123.9±5.2%;Str,n=5,104.3±4.5%, p<0.05Ctr versus Str,Fig.3).EE experience facilitated LTP (Ctr-EE,n=5,140.2±9.8%,p<0.05Ctr versus Ctr-EE),and restored the LTP impaired by early life stress to control level (Str-EE,n=6,121.8±5.2%,p>0.05Ctr versus Str-EE).Social/environmental stress in early life such as maternal separation,isolation,poverty,etc.is not avoidable in many children.Cognitive deficits progressively emerging with devel-opment are the results of complex interactions between genetic and environmental factors[4,15],and evidence suggests that EE experience can attenuate or reverse a variety of cognitive deficits[10].Here,we found that housing rats in cages with lim-ited nesting/bedding materials evoked a type of early life stress, indicated by lower body weight gain compared with control. Furthermore,the early life stress impaired learning and memory, increased depressive-like behavior,and impaired hippocampal LTP,which may potentially lead to congnitive deficits.Remark-ably,EE experience completely overcame these effects,which is consistent with a recent report that social defeat reduces but EE experience enhances hippocampal LTP[2].The effects of early life stress on behavioral experiments are correlated with that on the hippocampal LTP,although different animals were used.All these results suggest that EE experience may be a useful inter-vention for developmental cognitive and affectivedisorders. Fig.2.Early life stress increased immobility time but EE experience overcame this effect.Early life stress caused longer immobility time compared with con-trol.EE experience had no effect on immobility of control,but overcame the effect of early life stress on immobility.*p<0.05vs.Ctr.Ctr:control;Str:early life stress;EE:enriched environment experience;Ctr-EE:control rats exposed to EE;Str-EE:early life stressed rats exposed to EE.M.Cui et al./Neuroscience Letters404(2006)208–212211Fig.3.Early life stress impaired hippocampal LTP but EE experience prevented the impairment.High frequency stimulation(HFS)failed to induce LTP in the group of early life stressed rats.EE experience facilitated LTP in control and prevented the impairment of LTP induced by early life stress.Representative traces of fEPSPs were the average of2min recordings(4sweeps)at the time indicated by numbers.Calibration bars:horizontal=5ms,vertical=0.5mV.Ctr: control;Str:early life stress;EE:enriched environment experience;Ctr-EE: control rats exposed to EE;Str-EE:early life stressed rats exposed to EE.In control rats,EE experience had no effect on memory retrieval,depressive-like behavior,but enhanced hippocampal LTP,which may be due to relative short period of EE experi-ence.On the other hand,it implicates that hippocampal LTP may be more sensitive to EE experience.A recent report sug-gests that stress disrupts the homeostasis of synaptic plasticity in the inputs from Schaffer collaterals and Temporoammonic fibers[29].Thus,early life stress could disrupt the information processing in the developing hippocampus,leading to cognitive and affective disorders.Conversely,EE experience could rescue the early life stress induced developing disruptions by triggering the release of nerve growth factors,activating neurotransmitter receptors,or enhancing neurogenesis[19,21,26].In summary,this work demonstrates that cognitive deficits and depressive behavior in rats experienced early life stress can be reversed by EE experience.The reversed cognitive deficits and affective disorder-like behavior are associated with the recovery of LTP in the hippocampal CA1region.These results may be helpful for elucidating cellular and molecular mecha-nisms involved in cognitive deficits and affective disorder caused by early life stress.On the other hand,it suggests that EE may be useful to prevent these devastating effects in young adults followed childhood stress.AcknowledgementsThis work was supported by grants from National Science Foundation of China(t30370522to W.H.)and Basic Research 973Program(2003CB515404to W.H.).References[1]A.M.Aleisa,K.H.Alzoubi,N.Z.Gerges,K.A.Alkadhi,Nicotine blocksstress-induced impairment of spatial memory and long-term potentiation of the hippocampal CA1region,Int.J.Neuropsychopharmacol.(2005) 1–10[Epub ahead of print].[2]A.Artola,J.C.von Frijtag,P.C.Fermont,W.H.Gispen,L.H.Schrama,A.Kamal,B.M.Spruijt,Long-lasting modulation of the induction ofLTD and LTP in rat hippocampal CA1by behavioural stress and envi-ronmental enrichment,Eur.J.Neurosci.23(2006)261–272.[3]S.Avishai-Eliner, E.E.Gilles,M.Eghbal-Ahmadi,Y.Bar-El,T.Z.Baram,Altered regulation of gene and protein expression of hypothalamic-pituitary-adrenal axis components in an immature rat model of chronic stress,J.Neuroendocrinol.13(2001)799–807. [4]X.Bi,A.P.Yong,J.Zhou,C.E.Ribak,G.Lynch,Rapid induction ofintraneuronal neurofibrillary tangles in apolipoprotein E-deficient mice, Proc.Natl.Acad.Sci.U.S.A.98(2001)8832–8837.[5]T.V.Bliss,G.L.Collingridge,A synaptic model of memory:long-termpotentiation in the hippocampus,Nature361(1993)31–39.[6]K.L.Brunson,Y.Chen,S.vishai-Eliner,T.Z.Baram,Stress and thedeveloping hippocampus:a double-edged sword?Mol.Neurobiol.27 (2003)121–136.[7]D.J.de Quervain,B.Roozendaal,J.L.McGaugh,Stress and glucocorti-coids impair retrieval of long-term spatial memory,Nature394(1998) 787–790.[8]D.M.Diamond,A.Campbell,C.R.Park,R.M.V ouimba,Preclinicalresearch on stress,memory,and the brain in the development of phar-macotherapy for depression,Eur.Neuropsychopharmacol.14(Suppl.5) (2004)S491–S495.[9]S.N.Duffy,K.J.Craddock,T.Abel,P.V.Nguyen,Environmental enrich-ment modifies the PKA-dependence of hippocampal LTP and improves hippocampus-dependent memory,Learn.Mem.8(2001)26–34. [10]T.R.Guilarte,C.D.Toscano,J.L.McGlothan,S.A.Weaver,Environ-mental enrichment reverses cognitive and molecular deficits induced by developmental lead exposure,Ann.Neurol.53(2003)50–56.[11]C.Heim,P.M.Plotsky,C.B.Nemeroff,Importance of studying the con-tributions of early adverse experience to neurobiologicalfindings in depression,Neuropsychopharmacology29(2004)641–648.[12]R.L.Huot,P.M.Plotsky,R.H.Lenox,R.K.McNamara,Neonatal mater-nal separation reduces hippocampal mossyfiber density in adult Long Evans rats,Brain Res.950(2002)52–63.[13]J.J.Kim,D.M.Diamond,The stressed hippocampus,synaptic plasticityand lost memories,Nat.Rev.Neurosci.3(2002)453–462.[14]M.G.Leggio,L.Mandolesi,F.Federico,F.Spirito,B.Ricci,F.Gelfo,L.Petrosini,Environmental enrichment promotes improved spatial abilities and enhanced dendritic growth in the rat,Behav.Brain Res.163(2005) 78–90.[15]M.P.Mattson,S.L.Chan,Dysregulation of cellular calcium homeostasisin Alzheimer’s disease:bad genes and bad habits,J.Mol.Neurosci.17 (2001)205–224.[16]B.S.McEwen,Stress and hippocampal plasticity,Annu.Rev.Neurosci.22(1999)105–122.[17]R.Morris,Developments of a water-maze procedure for studying spatiallearning in the rat,J.Neurosci.Methods11(1984)47–60.[18]C.Pavlides,L.G.Nivon,B.S.McEwen,Effects of chronic stress onhippocampal long-term potentiation,Hippocampus12(2002)245–257.[19]T.M.Pham,B.Ickes,D.Albeck,S.Soderstrom,A.C.Granholm,A.H.Mohammed,Changes in brain nerve growth factor levels and nerve growth factor receptors in rats exposed to environmental enrichment for one year,Neuroscience94(1999)279–286.[20]R.D.Porsolt,P.M.Le,M.Jalfre,Depression:a new animal model sen-sitive to antidepressant treatments,Nature266(1977)730–732. [21]C.Rampon,C.H.Jiang,H.Dong,Y.P.Tang,D.J.Lockhart,P.G.Schultz,J.Z.Tsien,Y.Hu,Effects of environmental enrichment on gene expres-sion in the brain,Proc.Natl.Acad.Sci.U.S.A.97(2000)12880–12884.[22]M.M.Sanchez,dd,P.M.Plotsky,Early adverse experience asa developmental risk factor for later psychopathology:evidence fromrodent and primate models,Dev.Psychopathol.13(2001)419–449.212M.Cui et al./Neuroscience Letters404(2006)208–212[23]R.M.Sapolsky,Why stress is bad for your brain,Science273(1996)749–750.[24]A.C.Shakesby,R.Anwyl,M.J.Rowan,Overcoming the effects of stresson synaptic plasticity in the intact hippocampus:rapid actions of sero-tonergic and antidepressant agents,J.Neurosci.22(2002)3638–3644.[25]X.Sun,J.Zhang,H.Li,Z.Zhang,J.Yang,M.Cui,B.Zeng,T.Xu,J.Cao,L.Xu,Propofol effects on excitatory synaptic efficacy in the CA1 region of the developing hippocampus,Brain Res.Dev.Brain Res.157 (2005)1–7.[26]P.H.van,G.Kempermann,F.H.Gage,Neural consequences of environ-mental enrichment,Nat.Rev.Neurosci.1(2000)191–198.[27]S.vishai-Eliner,E.E.Gilles,M.Eghbal-Ahmadi,Y.Bar-El,T.Z.Baram,Altered regulation of gene and protein expression of hypothalamic-pituitary-adrenal axis components in an immature rat model of chronic stress,J.Neuroendocrinol.13(2001)799–807.[28]L.Xu,R.Anwyl,M.J.Rowan,Behavioural stress facilitates the induc-tion of long-term depression in the hippocampus,Nature387(1997) 497–500.[29]J.L.Yang,H.L.Han,M.H.Cui,L.P.Wang,J.Cao,L.J.Li,L.Xu,Acutebehavioural stress facilitates long-term depression in Temporoammonic-CA1pathway,Neuroreport17(2006)753–757.[30]Y.Yang,J.Cao,W.Xiong,J.Zhang,Q.Zhou,H.Wei,C.Liang,J.Deng,T.Li,S.Yang,L.Xu,Both stress experience and age determine the impairment or enhancement effect of stress on spatial memory retrieval, J.Endocrinol.178(2003)45–54.。
网络在线学习外文翻译中英文英文Online learning: Adoption, continuance, and learning outcome—A review ofliteratureRitanjali Panigrahi, Praveen Srivastava, Dheeraj Sharma AbstractThe use of Technology to facilitate better learning and training is gaining momentum worldwide, reducing the temporal and spatial problems associated with traditional learning. Despite its several benefits, retaining students in online platforms is challenging. Through a literature review of the factors affecting adoption, the continuation of technology use, and learning outcomes, this paper discusses an integration of online learning with virtual communities to foster student engagement for obtaining better learning outcomes. Future directions have been discussed, the feedback mechanism which i s an antecedent of students’ continuation intention has a lot of scopes to be studied in the virtual community context. The use of Apps in m-learning and the use of cloud services can boost the ease and access of online learning to users and organizations.Keywords: Online learning, Virtual community, Technology adoption, Technology continuation, Learning outcomeIntroductionOnline learning and training are gaining popularity worldwide, reducing the temporal and spatial problems associated with the traditional form of education. The primary factors behind using online learning are not only to improve access to education and training, and quality of learning, but also to reduce the cost and improve the cost-effectiveness of education (Bates, 1997). Online learning is mainly provided in two ways—in synchronous and asynchronous environments (Jolliffe, Ritter, & Stevens, 2012). The time lag attributes of asynchronous learning unlike synchronous learning in online platforms take the advantage of accessing materials anytime and anywhere, ability to reach a greater mass at the same time, and uniformity of content. Online learning along with face-to-face learning is successfullyused in industry as well as academia with positive outcomes (Chang, 2016). The geographically distributed team in an organization can get their skill training through online platforms at the same time, gaining a greater level of competitiveness. Online learning is also beneficial for students as they can learn at their own pace with the availability of online materials. The e-learning market is becoming popular and widely adopted by the education sector and industry. The growth of the e-learning market can be demonstrated by the fact that the global e-learning market is expected to reach 65.41 billion dollars by 2023 growing at a cumulative average growth rate of 7.07% (Research and Markets, 2018a). In addition to this, the global learning management system (LMS) is expected to increase from 5.05 billion USD in 2016 to 18.44 billion USD by 2025 growing at a rate of 15.52% (Research and Markets, 2018b).Despite several advantages of online learning such as improving access to education and training, improving the quality of learning, reducing the cost and improving the cost-effectiveness of education, retaining students in such platforms is a key challenge with a high attrition rate (Perna et al., 2014). Several strategies such as briefing, buddying, and providing feedback on the platform are proposed to retain and engage students (Nazir, Davis, & Harris, 2015). It is also noted that more self-discipline is required by students in online education, unlike traditional classroom education (Allen & Seaman, 2007). Keeping users enrolled and engaged is a challenging job as a personal touch by the instructor is missing or limited. The learning engagement which is an important antecedent for learning outcome is lower for technology-mediated learning than face-to-face learning (Hu & Hui, 2012). As a higher amount of money is spent on infrastructure, staff training, etc., organizations seek to take maximum benefit from online learning which requires an understanding of the factors that drive the adoption, continuation intention, and learning outcome of users on online learning platforms. Therefore, the primary focus of research remains on how to retain online learning users, and increase the efficiency of the online learning.Users may learn inside and outside the classroom; inside classroom learning isthrough instructors either from face-to-face, pure online or blended learning (combination of face-to-face and pure online learning) whereas outside classroom learning is conducted by users anytime and anywhere after the class. The exponential growth of the Internet has enabled individuals to share information, participate, and collaborate to learn from virtual communities (VC) anytime and anywhere (Rennie & Morrison, 2013). In a virtual community, people do everything that they do in real life but leaving their bodies behind (Rheingold, 2000). Virtual communities keep its users engaged based on familiarity, perceived similarity, and trust by creating a sense of belongingness (Zhao, Lu, Wang, Chau, & Zhang, 2012). It is essential to assess the role of a less constrained informal mode of learning (Davis & Fullerton, 2016) like virtual communities in the formal learning to engage and retain students.DiscussionGetting a new idea adopted even when it has obvious advantages is often very difficult (Rogers, 2003). Consistent with the previous statement, despite the advantages of online learning such as improving accessibility, quality, and reducing cost, it has a long way to go to be adopted and used by organizations because of the resistance at different levels (Hanley, 2018). The reasons for resistances offered by the employees in an organizations include perceived poor focus of the e-learning initiative, lack of time to learn new way of working, too much effort to change, lack of awareness, and resistance to change (Ali et al., 2016; Hanley, 2018). It is crucial from an institutional point of view to overcome the resistance to adopt and implement the online learning systems successfully.Understanding the factors of online learning adoption, continuation use intention, and learning outcomes are vital for an e-learning platform providing organization because the success of the platform depends on the successful adoption, continuation use, and finally achieving the desired outcomes. From the literature, it is found that the national culture affects the adoption and moderates the relationship between variables of adoption and use. Therefore, the results of adoption and use of technology might differ in different counties with different cultural dimensions. At a broader level, the perceived characteristics of innovation (of online learning) such as relativeadvantage, compatibility, complexity, trialability, and observability play a significant role in adoption. At an individual level, the primary factors of adoption are the individual expectancies such as the perceived usefulness, perceived ease of use, perceived enjoyment, performance expectancy, effort expectancy, etc., and the external influences such as subjective norm, social norms, surrounding conditions, national culture, social network characteristic, etc. On the other hand, the primary factors of continuation of technology use are the experiences of the individuals in the technology such as satisfaction, confirmation, self-efficacy, flow, trust, we-intention, sense of belongingness, immersion, IS qualities, etc. The perceived usefulness and perceived ease of use are found to be vital for both the technology adoption and continuation use. This implies that the usefulness of the technology and how easy the technology to use determines the adoption and continuation of technology. Apart from these technology enablers, the platform providers should consider the technology inhibitors which negatively impact the acceptance of the technology. The factors of the learning outcomes such as self-efficacy, virtual competence, engagement, design interventions, etc. should be considered before designing and delivering the content in the online learning platform to achieve optimum learning outcomes. The learners’ intention to use full e-learning in developing countries depends on the lea rners’ characteristics, and learners’ adoption of blended learning (Al-Busaidi, 2013). Studies for example by Verbert et al. (2014) have shown that blended learning yields the best outcome in terms of grade when students learn in online collaborative learning with teacher’s initiation and feedback. On the contrary, some studies have shown that contents such as business games do not need the interaction with the instructor; in fact, they are negatively related to perceived learning (Proserpio & Magni, 2012). MOOC (Massive Open Online Course) users have organized face-to-face meetings to fulfill their belongingness or social connectedness as a part of their learning activity (Bulger, Bright, & Cobo, 2015). This indicates that not everyone is good with a digitized form of learning, and hence both face-to-face and online components should be integrated for better outcomes.Lack of human connection is one of the limitations of online learning (Graham,2006) which may reduce the satisfaction level. To address this limitation, personalization functions of e-learning systems began. The satisfaction level, perceived and actual performance, self-efficacy scores increase in personalized online learning where learning materials are provided according to the cognitive capability and style of each individual (Xu, Huang, Wang, & Heales, 2014). Although personalization of e-learning systems is beneficial, they are socially and ethically harmful, and special attention should be given to issues such as privacy compromisation, lack of control, the commodification of education, and reduced individual capability (Ashman et al., 2014). Personal e-learning systems collect user information to understand the users’ interests and requirements for the learning which violates the privacy of individuals. The system utilizes the user information to show the personal content where the individuals do not have control over the learning content. Hence they are limited to certain personal contents which reduce their individual capabilities.Studies, for example, Zhao et al. (2012) have shown that VCs create a sense of belongingness and keeps the members engaged which results in improving the learning outcome, and users with same age groups are less likely to attrite (Freitas et al., 2015). Studies have shown that engagement is promoted when criteria such as problem-centric learning with clear expositions, peer interaction, active learning, instructor accessibility and passion, and using helpful course resources are met (Hew, 2015). Social interactions through social networking produce an intangible asset known as social capital (Coleman, 1988) in terms of the trust, collective action, and communication. Social capital is positively related to online learning satisfaction in group interactions, class interactions, learner-instructor interactions, as well as increasing students’ e-learning performance in groups (Lu, Yang, & Yu, 2013).The continuous development of mobile technology has expanded the opportunity to learn from mobile devices anywhere, anytime. M-Learning is much more beneficial for accessing education in remote areas and developing countries. The success of M-learning in organizations depends on organizational, people, and pedagogical factors apart from technological factors (Krotov, 2015). A range of mobiletechnologies such as laptops, smartphones, and tablets are embraced by students to support informal learning (Murphy, Farley, Lane, Hafeez-Baig, & Carter, 2014). Learning through mobile devices poses both opportunities as well as challenges; it provides flexibility in learning, on the other hand, it is a limitation for those who do not have connectivity and access to these devices. In student-centered learning especially collaborative and project-based learning, the use of mobile devices can be promoted by the mobile apps (Leinonen, Keune, Veermans, & Toikkanen, 2014). The use of mobile apps along with guidance from teachers integrates reflection in the classroom learning (Leinonen et al., 2014).Cloud computing provides organizations with a way to enhance their IT capabilities without a huge investment in infrastructure or software. The benefits of cloud computing are low cost, scalability, centralized data storage, no maintenance from user side (no software needed), easy monitoring, availability and recovery, and the challenges include it requires fast and reliable internet access, and privacy and security issues (El Mhouti, Erradi, & Nasseh, 2018). The primary factors for adoption of cloud computing in e-learning are ease of use, usefulness, and security (Kayali, Safie, & Mukhtar, 2016). Private cloud inside educational institutes can acquire the additional benefits in non-compromising the security and data privacy concerns associated with cloud computing (Mousannif, Khalil, & Kotsis, 2013). Cloud computing provides support to the online learning platforms to store and process the enormous amount of data generated. The problem of managing the increasing growth of online users, contents, and resources can be resolved by using cloud computing services (Fernandez, Peralta, Herrera, & Benitez, 2012).Future directionsFuture directions of research in online learning are as follows: First, the feedback mechanism used in online learning in institutions has not been used to measure the continuation intention in VCs. Feedback enables learners to define goals and track their progress through dashboard applications to promote awareness, reflection, and sense-making (Verbert et al., 2014). The students who received teachers’ feedback along with online learning achieved better grades than those who did not receivefeedback (Tsai, 2013) and students positively perceive the feedback systems more than the educators (Debuse & Lawley, 2016). Although immediate feedback is one of the dimensions of the flow (Csikszentmihalyi, 2014), the factor has not been studied in a VC context. It is vital for managers to check if feedback on a community post fosters the members’ continuation intention, and they should design user interfaces which encourage providing feedback. Second, it is high time to develop an integrated model for formal learning (online and blended) with VCs for students’ engagement. Informal learning as itself, not limited to the body of knowledge, rather, is the result of the interaction of people via communities of practice, networks, other forms, etc. (Rennie & Mason, 2004). The networked communities build intimacy and support which helps in self-directed learning (Rennie & Mason, 2004) which is an important parameter for online learning. Community commitment (Bateman et al., 2011), immersion (Shin et al., 2013), we-intentions (Tsai & Bagozzi, 2014), sense of belongingness (Zhao et al., 2012), etc. from the VC would help students to continue the engagement for a better learning outcome. Moreover, it is found that collaborative chat participation in MOOCs slows down the rate of attrition over time (Ferschke, Yang, Tomar, & Rosé, 2015). It is of great importance to check if learning outcome improves when the virtual community is integrated or embedded in the learning environment (online and blended). The educators and managers should encourage their students and employees to participate in VCs. Third, the growth of the adoption of mobile devices has expanded the arena of e-learning platforms. Integrating the virtual communities via a mobile platform with online learning can foster the student engagement resulting in higher learning outcome. Fourth, cloud computing has great potential in dealing with the scalability issues arising from the rise in numbers of users, content, and resources in online learning. Furthermore, it can provide tremendous benefits to organizations as well as users in terms of ease of access, flexibility, and lower cost. Although a few studies cover cloud computing infrastructure in education and pedagogic processes, the empirical research on the cloud computing for education is very shallow (Baldassarre, Caivano, Dimauro, Gentile, & Visaggio, 2018). As the mobile devices are often limited by storage space,future researchers are invited to carry out effective research on the integration of cloud computing and mobile learning to understand the factors affecting learning outcome.ConclusionUnderstanding the antecedents of e-learning adoption, continuance, and learning outcomes in online platforms are essential in ensuring the successful implementation of technology in learning and achieving the maximum benefits. This study shows factors such as PU, PEoU, PE, culture, attitude, subjective norms, system and information inhibitors, etc. contribute to the adoption of technology. Factors such as satisfaction, confirmation, user involvement, system quality, information quality, feedback, self-efficacy, social identity, perceived benefits, etc. determine the continuation of technology use. This implies factors for adoption, and continuation intentions vary; the attitude and usefulness of a system are essential for adoption while the experience and satisfaction in the environment lead to continuation intention. It is also found from the literature that the learning outcomes depend on the self-efficacy, collaborative learning, team cohesion, technology fit, learning engagement, self-regulation, interest, etc.The contribution of the paper can be summarized as: understanding the factors that are studied for adoption, continuance, learning outcomes in an online environment, and the provision of future research directions for educators and managers for successful implementation of technology in online platforms to achieve maximum benefits.中文在线学习的采用,持续性和学习成果:文献综述摘要在全世界范围内,使用技术促进更好的学习和培训的势头正在增强,减少了与传统学习相关的时空问题。
45篇语境串记初中英语词汇The acquisition and retention of vocabulary is a critical component of language learning, particularly for middle school students who are transitioning from basic to more advanced English proficiency. Contextual vocabulary lessons provide an effective approach to vocabulary development, allowing students to engage with words in meaningful, real-world scenarios. This essay will explore 45 such lessons, designed to enhance the vocabulary skills of middle school English language learners.Lesson 1: Describing Everyday Household ItemsIn this lesson, students will learn vocabulary related to common household objects, such as furniture, appliances, and utensils. They will practice describing the physical characteristics, functions, and locations of these items within a home setting.Lesson 2: Expressing Emotions and FeelingsStudents will explore a range of emotion-related vocabulary, including words that describe happiness, sadness, anger, fear, and other emotional states. They will learn to use these words in context,discussing situations that evoke specific feelings.Lesson 3: Discussing Daily Routines and SchedulesThis lesson focuses on vocabulary related to daily activities, such as waking up, getting ready for school, attending classes, and engaging in leisure pursuits. Students will practice using these words to describe their own daily routines.Lesson 4: Describing the Natural WorldStudents will learn vocabulary related to the natural environment, including terms for different types of weather, seasons, plants, and animals. They will apply this knowledge to discuss their observations and experiences in the natural world.Lesson 5: Discussing Transportation and TravelIn this lesson, students will explore vocabulary associated with various modes of transportation, such as cars, buses, trains, and airplanes. They will also learn words related to travel planning, including destinations, accommodations, and travel activities.Lesson 6: Expressing Opinions and PreferencesStudents will expand their vocabulary to express personal opinions, preferences, and attitudes. They will learn words and phrases to convey agreement, disagreement, likes, dislikes, and other subjective responses.Lesson 7: Describing Physical CharacteristicsThis lesson focuses on vocabulary related to physical appearance, including terms for facial features, body parts, and physical attributes. Students will practice using these words to describe themselves and others.Lesson 8: Discussing Health and WellnessStudents will learn vocabulary related to health, including terms for common illnesses, injuries, and medical treatments. They will also explore words associated with healthy habits, such as exercise, nutrition, and personal hygiene.Lesson 9: Describing the School EnvironmentIn this lesson, students will expand their vocabulary to discuss the various components of a school, such as classrooms, facilities, and educational materials. They will also learn words related to academic subjects and school-related activities.Lesson 10: Expressing Time and DatesStudents will explore vocabulary related to time, including words for days, months, seasons, and time-related expressions. They will practice using these words to discuss schedules, deadlines, and the passage of time.Lesson 11: Discussing Food and CuisineThis lesson focuses on vocabulary related to food, including terms for different types of dishes, ingredients, and dining experiences. Students will learn to describe their food preferences, dietary requirements, and culinary experiences.Lesson 12: Describing Clothing and FashionStudents will expand their vocabulary to discuss various types of clothing, accessories, and fashion trends. They will practice using these words to describe their own personal style and make observations about the fashion choices of others.Lesson 13: Expressing Ability and SkillIn this lesson, students will learn vocabulary to discuss their own abilities, skills, and talents, as well as those of others. They will practice using words that convey proficiency, competence, and expertise.Lesson 14: Describing the Weather and SeasonsStudents will explore vocabulary related to weather conditions, including terms for precipitation, temperature, and atmospheric phenomena. They will also learn words associated with the changing seasons and their characteristics.Lesson 15: Discussing Hobbies and Leisure ActivitiesThis lesson focuses on vocabulary related to hobbies, sports, and other leisure pursuits. Students will learn to describe their own interests and engage in discussions about the recreational activities they enjoy.Lesson 16: Expressing Quantity and MeasurementStudents will expand their vocabulary to discuss concepts of quantity, size, and measurement, including words for numbers, units of measurement, and comparative language.Lesson 17: Describing Family and RelationshipsIn this lesson, students will learn vocabulary related to family members, personal relationships, and social connections. They will practice using these words to describe their own family structures and interpersonal dynamics.Lesson 18: Discussing Technology and Digital DevicesStudents will explore vocabulary associated with various technological devices, software, and digital applications. They will learn to describe the features, functions, and uses of these tools in their daily lives.Lesson 19: Expressing Spatial RelationshipsThis lesson focuses on vocabulary related to spatial awareness, including words for direction, location, and relative position.Students will practice using these terms to describe the placement and orientation of objects and people.Lesson 20: Describing the Arts and EntertainmentStudents will expand their vocabulary to discuss different forms of art, including visual arts, performing arts, and media. They will also learn words related to entertainment, such as genres, venues, and cultural events.Lesson 21: Discussing Occupations and ProfessionsIn this lesson, students will explore vocabulary related to various occupations, job titles, and professional roles. They will practice using these words to describe the work that people do and the skills required for different careers.Lesson 22: Expressing Preferences and ChoicesStudents will learn vocabulary to convey their personal preferences, choices, and decision-making processes. They will practice using these words to discuss their likes, dislikes, and the reasoning behind their decisions.Lesson 23: Describing the Community and NeighborhoodThis lesson focuses on vocabulary related to the local community, including terms for different types of buildings, public spaces, and community services. Students will learn to describe the features andcharacteristics of their own neighborhoods.Lesson 24: Discussing Travel and TourismStudents will expand their vocabulary to discuss travel experiences, including words for tourist attractions, cultural landmarks, and travel-related activities. They will practice using these terms to plan and describe their own travel adventures.Lesson 25: Expressing Opinions on Current EventsIn this lesson, students will learn vocabulary to discuss and express their opinions on current events, including political, social, and environmental issues. They will practice using these words to engage in informed discussions and debates.Lesson 26: Describing Personality Traits and Characteristics Students will explore vocabulary related to personality traits, individual characteristics, and behavioral tendencies. They will practice using these words to describe themselves and others, as well as to analyze fictional characters.Lesson 27: Discussing Environmental Issues and ConservationThis lesson focuses on vocabulary related to environmental topics, such as natural resources, sustainability, and conservation efforts. Students will learn to discuss these issues and express their views on the importance of environmental stewardship.Lesson 28: Expressing Gratitude and AppreciationStudents will expand their vocabulary to convey gratitude, appreciation, and thankfulness. They will practice using these words in various contexts, such as expressing appreciation for others, acknowledging achievements, or showing gratitude for received favors.Lesson 29: Describing Emotions and Feelings in DepthIn this lesson, students will delve deeper into vocabulary related to emotional experiences, exploring more nuanced and complex feelings. They will learn to use these words to articulate their own emotional states and empathize with the emotions of others.Lesson 30: Discussing Global Cultures and DiversityStudents will explore vocabulary related to different cultures, traditions, and forms of diversity around the world. They will practice using these words to describe cultural practices, celebrate diversity, and engage in cross-cultural dialogue.Lesson 31: Expressing Uncertainty and Hypothetical ScenariosThis lesson focuses on vocabulary that conveys uncertainty, speculation, and hypothetical situations. Students will learn to use these words to discuss possibilities, make predictions, and engage in imaginative thinking.Lesson 32: Describing Conflict Resolution and Negotiation Students will expand their vocabulary to discuss conflict resolution, negotiation, and problem-solving strategies. They will practice using these words to describe effective communication, compromise, and collaborative problem-solving.Lesson 33: Discussing Ethical Principles and ValuesIn this lesson, students will explore vocabulary related to ethical principles, moral values, and philosophical concepts. They will practice using these words to engage in discussions about ethical dilemmas and the importance of ethical decision-making.Lesson 34: Expressing Creativity and ImaginationStudents will learn vocabulary to describe creative processes, imaginative thinking, and the appreciation of artistic expression. They will practice using these words to discuss their own creative endeavors and the creative works of others.Lesson 35: Describing Leadership and TeamworkThis lesson focuses on vocabulary related to leadership, teamwork, and collaborative efforts. Students will learn to use these words to discuss the characteristics of effective leaders, the dynamics of successful teams, and the importance of cooperation.Lesson 36: Discussing Mental Health and Well-beingStudents will explore vocabulary related to mental health, including terms for emotional states, coping strategies, and mental health resources. They will practice using these words to discuss the importance of self-care and supporting the well-being of themselves and others.Lesson 37: Expressing Empathy and CompassionIn this lesson, students will learn vocabulary to convey empathy, compassion, and understanding towards the experiences and perspectives of others. They will practice using these words to engage in meaningful dialogues and foster interpersonal connections.Lesson 38: Describing Scientific Concepts and Discoveries Students will expand their vocabulary to discuss scientific principles, technological advancements, and groundbreaking discoveries. They will practice using these words to explore the wonders of the natural world and the impact of scientific progress.Lesson 39: Discussing Social Issues and AdvocacyThis lesson focuses on vocabulary related to social issues, human rights, and advocacy. Students will learn to use these words to engage in discussions about social justice, inequality, and the importance of civic engagement.Lesson 40: Expressing Resilience and Overcoming Challenges Students will explore vocabulary that conveys resilience, perseverance, and the ability to overcome challenges. They will practice using these words to discuss their own experiences with adversity and the strategies they employ to build personal strength and adaptability.Lesson 41: Describing Globalization and Intercultural ExchangeIn this lesson, students will learn vocabulary related to globalization, international cooperation, and cross-cultural interactions. They will practice using these words to discuss the opportunities and challenges presented by an increasingly interconnected world.Lesson 42: Expressing Ambition and Goal-SettingStudents will expand their vocabulary to discuss personal ambitions, goal-setting, and the pursuit of success. They will practice using these words to articulate their aspirations, develop action plans, and reflect on their progress.Lesson 43: Discussing Sustainability and Environmental Stewardship This lesson focuses on vocabulary related to sustainability, environmental conservation, and eco-friendly practices. Students will learn to use these words to engage in discussions about the importance of preserving natural resources and promotingsustainable living.Lesson 44: Expressing Civic Engagement and Community InvolvementStudents will explore vocabulary that describes active participation in civic life, community service, and social responsibility. They will practice using these words to discuss the importance of civic engagement and the ways in which individuals can contribute to the betterment of their communities.Lesson 45: Describing the Digital Age and Technological AdvancementsIn this final lesson, students will expand their vocabulary to discuss the impact of technology, digital media, and the rapidly evolving digital landscape. They will practice using these words to analyze the opportunities and challenges presented by technological progress and the implications for their own lives and the world around them.。
1.Mutual exclusivity bias It is a cognitive constraint which refers to the fact that a child who knows the name of a particular object will then generally reject applying a second name to that object.2. Motherese Adult-to-child language, which has been called motherese, differ in a number of ways from adult-to-adult language. In general, speech to children learning language is shorter, more concrete, more directive, and more intonationally exaggerated than adult-directed speech.3. Critical period hypothesis The view that there is a period early in life in which we are especially prepared to acquire a language is referred to as the critical period hypothesis. Many investigators who favor the critical period hypothesis suggest that there are neurological changes in the brain that leave a learner less able to acquire a language, although the nature of these supposed changes is not well understood. Most commonly, these changes are assumed to occur near puberty.nguage bioprogram hypothesis On version of how innate processes operate in child language has been called the Language bioprogram hypothesis by Bickerton. Bickerton’s claim, in brief, is that we, as children, have an innate grammar that is available biological if our language input is insufficient to acquire the language of our community. It is something like a linguistic backup system.5. Pidgin A pidgin is “an auxiliary language that arises when speakers of several mutually unintelligible languages are in close contact”. Typically this occurs when workers from diverse countries are brought in as cheap labor in an agricultural community. Immigrant workers come to speak a simpler form of the dominant language of the area—just enough to get by.6. Language transfer In second-language acquisition, the process in which the first language influences the acquisition of a subsequent language.7. Overregularization An overregularization is the child’s use of a regular morpheme in a word that is irregular, such as the past-tense morpheme in breaked and goed. There are two theories about how children acquire overregularizations: the rule-and-memory model and the parallel distributed processing model.8.Holophrase A holophrase has been defined as a single-word utterance that is used by a child to express more than the meaning usually attributed to that single word by adults.9.Idiomorph A sound or sound sequence used consistently by a child to refer to someone or something, objects or events in their environment even though it is not the sound sequence conventionally used in the language for that purpose.10. Coalescence Coalescence occurs when phonemes from different syllables are combined into a single syllable.11. Reduction A phonological process in child language in which one or more phonemes are deleted. Also called cluster reduction because consonant clusters are often reduced, such as saying take for steak.12. Assimilation Assimilation is a phonological process. Assimilation occurs when children change one sound to make it similar to another sound in the same word, such as saying nance for dance or fweet for sweet. In the latter case, the f is articulated closer to the front of the mouth than s, making it more similar to the bilabial w.13. Common ground Common ground refers to the shared understanding of those involved in the conversation. For knowledge to qualify as common ground, person A must know a given information X, and person B must know X, and A must know that B knows, and B knows that A knows, and so on. That is, both parties are aware that they share the information.14.Semantic bootstrapping The process of using semantics to acquire syntax. (Ultimately children must grasp categories that are defined in syntactic terms, and there has been much debate concerning how they do this. One suggestion is that they use their knowledge of semantic relations to learn syntactic relations. This process is known as semantic bootstrapping )15. Accommodation A phonological process in which elements that are shifted or deleted are adapted to their error-induced environments.16. Incremental processing The notion that we are planning one portion of our utterance as we articulate another portion.17. Speech errors =slip of tongue Speech errors refer to faults made by speakers during the production of sounds, words and sentences. Both native and non-native speakers of a language make mistakes when speaking. There are eight types of speech errors: exchange, substitution, addition, deletion, anticipation, perseveration, blend, and shift.18. Assemblage errors The correct choice or word has been made, but the utterance has been faultily assembled.Eg. writtening threat letters---writing threatening letters19. Selection errors A wrong item (or items) is chosen, where something has gone wrong with the selection process. Eg. tooth hache---tooth paste20.Psycholinguistics Psycholinguistics is the study of how individuals comprehend, produce, and acquire language.The psychological study of language is called psycholinguistics. The study of psycholinguistics is part of the field of cognitive science. It deals with the mental processes that are involved in language use. Psycholinguistics stresses the knowledge of language and the cognitive processes involved in ordinary language use. Psycholinguists are also interested in the social rules involved in language use and the brain mechanisms associated with language. Contemporary interest in psycholinguistics began in the 1950s, although important precursors existed earlier in the 20th century.21.Aphasia A language disorder produced by brain damage is called an aphasia.we beginby examining some of the more common types of aphasia. One type is Broca’s Aphasia. The disorder Broca’s aphasia,also known as expressive aphasia, was discovered by and named after the French surgeon Paul Broca. The second type is Wernicke’s Aphasia.It results from damage to a region in the left temporal lobe near the auditory cortex.A third major type of aphasia is conduction aphasia,which is a disturbance of repetition, and other aphasias.22. BehaviorismBy the 1920s, behaviorism took over the mainstream of experimental psychology. Behaviorist favored the study of objective behavior, often in laboratory animals, as opposed to the study of mental processes. Moreover, behaviorists had a strong commitment to the role of experience in shaping behavior. Emphasis was placed on the role of environmental contingencies (such as reinforcement and punish-ment) and on models present in the immediate environment.23. Distinctive features A distinctive feature is a characteristic of a speech sound whose presence or absence distinguishes the sound from other sounds.24. Observational adequacy First, the grammar must specify what is and what is not an acceptable sequence in the language. This criterion, referred to as observational adequacy, applies at several levels of language. A grammar is observationally adequate if it generates all of the acceptable sequences in a language and none of the unacceptable sequences.25. Descriptive adequacy The second criterion is that the grammar must specify the relationships between various sequences in the language, a criterion known as descriptive adequacy. It is not enough for the grammar to mark a sequence as permissible; it must also explain how it relates to other sentences that are similar in meaning, opposite in meaning and so on.26. Explanatory adequacy The extent to which a grammar can explain the facts of language acquisition. See also descriptive adequacy and observational adequacy. The third criterion is called explanatory adequacy. That children choose one particular grammar implies that certain innate language constraints enable the child to deduce the correct grammar. This level of adequacy involves the ability to explain the role of linguistic universals in language acquisition.27. Transformational-generative grammar T ransformational grammar discusses a historically significant theory of grammar. Transformational grammar assumes that sentences have a deep structure and a surface structure. The deep structure is derived by a series of phrase-structure rules, and the surface structure is derived from the deep structure by a series of transformational rules.28. Psychological reality A grammar or theory of language that takes psychological or processing considerations into account.29. Core grammar Core grammar is the grammar that rules the essence of the syntax of a language(principle and parameters). It is an innate ability.30. Working memory Working memory has been defined as referring to “the temporary storage of information that is being processed in any range of cognitive tasks” (Baddeley, 1986, p. 34). Working memory is measured in several ways. The most simple is a memory span test (or simple span test) in which participants are given a series of items (words, letters, numbers, and so forth) and asked to recall the items in the order presented. Sometimes they are asked to recall them in backward order.31.Memory span :it is the number of items that can be reliably recalled in the correct order. This simple test not only is a common method in psychological experiments but also is included in most commonly used intelligence tests.32.Episodic memory The division of permanent memory in which personally experienced information is stored.It dealt with personally experienced facts33.Semantic memory It dealt with general facts.Semantic memory refers to our organized knowledge of words, concepts, symbols, and objects. It includes such broad classes of information as motor skills (typing, swimming, bicycling), general knowledge (grammar, arithmetic), spatial knowledge (the typical layout of a house), and social skills (how to begin and end conversations, rules for self-confidence).34. Parallel processing If two or more of the processes take place simultaneously, it is called parallel processing.35.Categorical perception Categorical perception refers to a failure to discriminate speech sounds any better than you can identify them. This may be illustrated with an experimental example. On a speech spectrometer, it is possible to identify the difference between the voiced sound [ba] and the voiceless sound [pa] as due to the time between when the sound is released at the lips and when the vocal cords begin vibrating. It suggests that categorical perception is a reflection of the phonetic level of processing in which a phonetic identity is imposed and all other acoustic features are lost (thus leading to especially poor performance on within-category discrimination).36.Semantic network A semantic network is an interconnected web of concepts connected by various relations. In the hierarchical model, we store our knowledge of words in the form of a semantic network, with some words represented at higher nodes in the network than others. Although the hierarchical network model can explain some results, it is too rigid to capture all of our tacit knowledge of the lexicon.37.Typicality effect The fact that it takes longer to verify a statement of the form An A is aB when A is no t typical or characteristic of B. This has generally been called the typicality effect: Items that are more typical of a given subordinate take less time to verify than atypical items in true statements; the opposite is true for false statements.38.Logogen : Morton (1969) proposed one of the earliest activation models. In Morton’s model,each word (or morpheme) in the lexicon is represented as a logogen, which specifies the word’s various attributes (semantic, orthographic, phonological, and so on).The logogen is activated in either of two ways: by sensory input or by contextual information. Consider first the sensory route. As orthographic or phonological features of the input stimulus are detected, they are matched to the logogen. The logogen functions as a scoreboard or counter; when the counter rises above a predesignated threshold, the item is recognized.39.Cohort Model A model of auditory word recognition in which listeners are assumed to developa group of candidates, a word initial cohort, and then determine which member of that cohort corresponds to the presented word. Marslen-Wilson (1987,1990) and colleagues noticed several aspects of spoken word recognition that needed to be accounted for in a model of lexical access. First, listeners recognize words very rapidly, perhaps within 200 to 250 milliseconds of the beginning of the word. Second, listeners are sensitive to the recognition point of a word- the point at which the word diverges from other possible words.40. Semantic priming Semantic priming occurs when a word presented earlier activates another, semantically related word. The priming task consists of two phases. The priming task consistsof two phases. In the first phase, a priming stimulus is presented. Often no response tothe prime is required or recorded; in any event, the response to the prime itselfis of little interest. In the second phase, a second stimulus (the target) is presented, the participant makes some response to it, and the time taken to make this response is recorded.An experimental procedure in which one word is presented in advance of another, target word, which reduces the time needed to retrieve or activate the target word.41.Parsing Parsing is the process of assigning elements of surface structure to linguistic categories. Because of limitations in processing resources, we begin to parse sentences as we see or hear each word in a sentence.A first step in the process of understanding a sentence is to assign elements of its surface structure to linguistic categories, The result of parsing is an internal representation of the linguistic relationships within a sentence, usually in the form of a tree structure or phrase marker.42.Minimal attachment strategy A principle used in parsing. It states that we prefer attaching new items into the phrase marker being constructed using the fewest syntactic nodes consistent with the rules of the language43.Coherence The degree to which different parts of a text are connected to one another. Coherence exits at both local and global levels of discourse.44.Anaphoric reference A form of reference cohesion in which one linguistic expression refers back to prior information in discourse.In all of these examples, cohesion consists of relating some current expression to one encountered earlier. This is called anaphoric reference. When we use an expression to refer back to something previously mentioned in discourse, the referring expression is called an anaphor, and the previous referent is called an antecedent.45. Schema A schema (plural: schemata) is a structure in semantic memory that specifies the general or expected arrangement of a body of information. The notion of a schema is not new in psychology. it is generally associated with the early work on story recall by Bartlett(1932).. An alternative perspective on cognitive development, one that challenges the notion of invariance, has been described by the Swiss scholar Jean Piaget,Piaget (1952) claimed that children’s thinking processes are qualitatively different from those of adults. Adults do not merely think faster or more accurately than children, but in a different way. Piaget referred to the concepts that we use to organize our experience as schemata.欢迎您的下载,资料仅供参考!致力为企业和个人提供合同协议,策划案计划书,学习资料等等打造全网一站式需求。
International Journal of Remote Sensing, in press, 2006.“Parameter Selection for Region-Growing Image Segmentation Algorithms usingSpatial Autocorrelation”G. M. ESPINDOLA, G. CAMARA*, I. A. REIS, L. S. BINS, A. M.MONTEIROImage Processing Division, National Institute for Space Research (INPE), P.O. Box 515, 12201-001 São José dos Campos, SP, BrazilRegion-growing segmentation algorithms are useful for remote sensingimage segmentation. These algorithms need the user to supply controlparameters, which control the quality of the resulting segmentation. Thisletter proposes an objective function for selecting suitable parameters forregion-growing algorithms to ensure best quality results. It considers that asegmentation has two desirable properties: each of the resulting segmentsshould be internally homogeneous and should be distinguishable from itsneighbourhood. The measure combines a spatial autocorrelation indicatorthat detects separability between regions and a variance indicator thatexpresses the overall homogeneity of the regions.Keywords: Region-growing segmentation, spatial autocorrelation.2000 Mathematics Classification Index:62H11 Spatial statistics. 68U10Image processing. 62H35 Image analysis.1.IntroductionMethods of image segmentation are important for remote sensing image analysis. Image segmentation tries to divide an image into spatially continuous, disjunctive and homogenous regions (Pekkarinen 2002). Segmentation algorithms have many advantages over pixel-based image classifiers. The resulting maps are usually much more visually consistent and more easily converted into a geographical information system. Among the image segmentation techniques in the literature, region-growing techniques are being widely used for remote sensing applications, since they guarantee creating closed regions (Tilton and Lawrence 2000). Since most region-growing segmentation algorithms for remote sensing imagery need user-supplied parameters, one of the challenges for using these algorithms is selecting suitable parameters to ensure best quality results. This letter addresses this problem, proposing an objective function for measuring the quality of a segmentation. By applying the proposed function to the segmentation results, the user has guidance for selection of parameter values.The issue of measuring segmentation quality has been addressed in the literature (Zhang, 1996). For closed regions, Liu and Yang (1994) propose a function that considers the number of regions in the segmented image, the number of the pixels in each region and the colour error of each region. Similarly, Levine and Nazif (1985) use a function that combines measures of region uniformity and region contrast. None of these proposals makes direct use of spatial autocorrelation. Spatial autocorrelation is an inherent feature of remote sensing data (Wulder and Boots, 1998) and it is a reliable indicator of statistical separability between spatial objects (Fotheringham et al., 2000). Using spatial autocorrelation for measurement of image segmentation quality is particularly suited for region-growing algorithms, which produce closed regions.The proposed objective function considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The function combines a spatial autocorrelation index, which detects separability between regions, with a variance indicator, which expresses the overall homogeneity of the regions. The main advantage of the proposed method is its robustness, since it uses established statistical methods (spatial autocorrelation and variance).2. A typical region-growing image segmentation algorithmThe assessment of the proposed objective function used the region-growing segmentation used in the SPRING software (Bins, Fonseca et al. 1996). As a recent survey shows (Meinel and Neubert 2004), this algorithm is representative of the current generation of segmentation techniques and it ranked second in quality out of the seven algorithms surveyed by the authors. This algorithm uses two parameters: a similarity threshold and an area threshold . It starts by comparing neighbouring pixels and merging them into regions if they are similar. The algorithm then tries iteratively to merge the resulting regions. Two neighbouring regions, R i and R j , are merged if they satisfy the following conditions:(1) Threshold Condition: (,)(2) Neighbourhood Condition 1: () and (,)(,), ()(3) Neighbourhood Condition 2: () and (,)(,), ()i j j i j i k i k i i j j j k j k j dist R R TR N R dist R R dist R R R N R R N R dist R R dist R R R N R ≤∈≤∈∈≤∈In the above, T is the chosen similarity threshold , dist(R i , R j ) is the Euclidian distance between the mean grey levels of the regions and N(R) is the set of neighbouring regions of region R . Also, regions smaller than the chosen area threshold are removed by merging them with its most similar neighbour (Bins, Fonseca et al. 1996). The results of the segmentation algorithm are sensitive to the choice of similarity and area thresholds. Low values of area threshold result in excessive partitioning, producing a confusing visual picture of the regions. High values of similarity threshold force the union of spectrally distinct regions, resulting in undersegmentation. In addition, the right thresholds vary depending on the spectral range of the image.The need for user-supplied control parameters, as required by SPRING, is typical of region-growing algorithms (Meinel and Neubert 2004). For example, the segmentation algorithm used in the e-Cognition ® software (Baatz and Schape 2000) needs similar parameters: scale and shape factors, compactness and smoothness criterion. Therefore, the objective function is useful for region-growing algorithms in general.3. An indicator of segmentation qualityGiven the sensitivity of region-growing segmentation algorithms to user-supplied parameters, this letter proposes an objective function for measurement of the quality of the resulting segmentation. The function aims at maximizing intrasegment homogeneity and intersegment heterogeneity. It has two components: a measure of intrasegment homogeneity and one of intersegment heterogeneity. The first component is the intrasegment variance of the regions produced by a segmentation algorithm. It is calculated by the formula:11n ii i ni i a vv a ==⋅=∑∑ (1)In equation (1), v i is the variance and a i is the area of region i . The intrasegment variance v is a weighted average, where the weights are the areas of each region. This approach puts more weight on the larger regions, avoiding possible instabilities caused by smaller regions.To assess the intersegment heterogeneity, the function uses Moran’s I autocorrelation index (Fotheringham et al., 2000), which measures the degree of spatial association as reflected in the data set as a whole. Spatial autocorrelation is a well-known property of spatial data. Similar values for a variable will occur in nearby locations, leading to spatial clusters. The algorithm for computing Moran’s I index (the spatial autocorrelation of a segmentation) uses the fact that region-growing algorithms generate closed regions. For each region, the algorithm calculates its mean grey value and determines all adjacent regions. In this case, Moran’s I is expressed as:()()()()1121n nij i j i j n i ij i j i n w y y y y I y y w ==≠=−−= − ∑∑∑∑∑ (2)In equation (2), n is the total number of regions, w ij is a measure of the spatial proximity, y i is the mean grey value of region R i , and y is the mean grey value of the image. Each weight w ij is a measure of the spatial adjacency of regions R i and R j . If regions R i and R j are adjacent, w ij is one. Otherwise, it is zero. Thus, Moran’s I applied to segmented images will capture how, in average, the mean values of each region differfrom the mean values of its neighbours. Small values of Moran’s I indicate low spatial autocorrelation. In this case, the neighbouring regions are statistically different. Local minima of this index signal locations of large intersegment heterogeneity. Such minima are associated to segmentation results that show clear boundaries between regions.The proper choice of parameters is the one that combines a low intersegment Moran’s I index (adjacent regions are dissimilar) with a low intrasegment variance (each region is homogenous). The proposed objective function combines the variance measure and the autocorrelation measure in an objective function given by:(,)()()F v I F v F I =+ (3)Functions F(v) and F(I) are normalization functions, given by:max max min()X X F x X X −=− (4) 4. Results and discussionTo assess the validity of the proposed measure, we conducted two experiments. The first experiment used a 100x100 pixel image of band 3 (0.63-0.69 µm) of the LANDSAT-7/ETM+ sensor (WRS 220/74, 14 August 2001). We created 2500 segmentations, with similarity and area thresholds ranging from one to 50. The values of the objective function are shown in figure 1a and the image is shown in Figure 1b. The maximum value occurs for an area threshold of 22 and a similarity threshold of 25. This maximum value matches the visual interpretation of the result, which achieves a balance between undersegmentation and oversegmentation.Figure 1. Left: the objective function for test image, whose maximum value occurs when the similarity threshold is 25 and area threshold is 22. Right: Resulting segmentedimage.The weighted variance for the 2500 segmentations is shown in figure 2a. Small values of similarity and area thresholds produce few regions and the weighted variance will have small values. The weighted variance increases with the similarity and area thresholds. The values of Moran’ I are shown in figure 2b, which indicates the local minima. These local minima are cases where each region is internally homogenous and is dissimilar from its neighbours.Figure 2. Left: weighted variance for 2500 segmentations of test image. Right: Moran’sI for 2500 segmentations for test image.Figure 3 shows how Moran’s I index varies, given a fixed area threshold of 22 and a similarity threshold ranging from one to 50. Visual comparison of three results (with similarities of 19, 29, and 36) shows the segmentation with smallest value of Moran’s I matches a more visually pleasing segmentation result.Figure 3. Top: values of Moran’s I for a fixed area threshold (22) and a similarity value ranging from 1 to 50. Bottom (left to right): Segmentations with different similaritythresholds (19, 29 and 36).The second experiment used a synthesized image of 426x426 pixels, as suggested by Liu and Yang (1994). Figure 4 shows and the variation of its objective function. The maximum value of the objective function matches visual interpretation of the results. The best segmentation has a high homogeneity of the segments, and a clear distinction between neighbouring segments.Figure 4. Left: objective function for synthesized image. Right: Best segmentation (similarity parameter is 20 and area parameter is 22).5.ConclusionThe emerging use of region-growing segmentation algorithms for remote sensing imagery requires methods for guiding users as to the proper application of these techniques. This letter proposes an objective function that uses inherent properties of remote sensing data (spatial autocorrelation and variance) to support the selection of parameters for these algorithms. The proposed method allows users to benefit from the potential of region-growing methods for extracting information from remote sensing data.AcknowledgmentsGilberto Camara’s work is partially funded by CNPq (grants PQ - 300557/1996-5 and 550250/2005-0) and FAPESP (grant 04/11012-0). Giovana Espindola’s work is funded by CAPES. This support is gratefully acknowledged. The authors would also like to thank the referees for their useful comments.ReferencesBAATZ, M., and SCHAPE, A., 2000, Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation: XII Angewandte Geographische Informationsverarbeitung.BINS, L., FONSECA, L., and ERTHAL, G., 1996, Satellite Imagery Segmentation: a region growing approach: VIII Brazilian Symposium on Remote Sensing, p.677-680.CÂMARA, G., SOUZA, R., FREITAS, U., and GARRIDO, J., 1996, SPRING: Integrating Remote Sensing and GIS with Object-Oriented Data Modelling.Computers and Graphics, 15, 13-22.FOTHERINGHAM, A. S., BRUNSDON, C., and CHARLTON, M., 2000, Quantitative Geography: Perspectives on Spatial Analysis (London: Sage).LEVINE, M. D., and NAZIF, A. M., 1985, Dynamic measurement of computer generated image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 7, 155.LIU, J., and YANG, Y.-H., 1994, Multiresolution color image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16, 689.MEINEL, G., and NEUBERT, M., 2004, A comparison of segmentation programs for high resolution remote sensing data. International Archives of Photogrammetry and Remote Sensing, XXXV, 1097-1105.PEKKARINEN, A., 2002, A method for the segmentation of very high spatial resolution images of forested landscapes. International Journal of Remote Sensing, 23, 2817-2836.TILTON, J., and LAWRENCE, W., 2000, Interactive analysis of hierarchical image segmentation: International Geoscience and Remote Sensing Symposium IGARSS-2000.WULDER, M., and BOOTS, B., 1998, Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic. International Journal of Remote Sensing, 19, 2223 - 2231.ZHANG, Y. J., 1996, A survey on evaluation methods for image segmentation. Pattern Recognition, Vol. 29, 1335-1346.。
Spatial learning associated with stimulus responsein goldfish Carassius auratus:relationship to activation of CREB signallingKoilmani Emmanuvel Rajan•Subramanian Thangaleela•Chellam BalasundaramReceived:1May2014/Accepted:23February2015ÓSpringer Science+Business Media Dordrecht2015Abstract Earlier,we reported spatial learning abil-ity in goldfish(Carassius auratus)by using spatial paradigm with food reward.Therefore,we hy-pothesized that goldfish may use associated cue to integrate‘‘where’’and‘‘what’’for spatial memory.To test our hypothesis,wefirst trained goldfish to learn to cross the gate1,which is associated with spatial task. Subsequently,they were trained to learn to enter the task chamber and to identify the food reward chamber associated with visual cue(red/green light).Red and green lights were positioned randomly for each trial but always the food reward was kept in green chamber. In addition,to elucidate the role of the signalling cascade in spatial memory associated with visual cue, nicotinamide(NAM,1000mg/kg,i.p),a NAD? precursor,was used to inhibit the Sirtuin1(SIRT1) cyclic AMP response element binding protein(CREB) pathway.Fishes were trained for5days in a maze after treating with either vehicle(VEH,DD H2O)or NAM, and then,they were individually tested for memory. We found that VEH-treatedfish learned and recalled the task successfully by showing less latency and making more correct choices than NAM-treated group.Subsequent analysis showed that NAM treat-ment significantly down-regulated the phosphoryla-tion of extracellular signal-regulated kinase(ERK1/2),CREB,expression of SirT1and brain-derived neu-rotrophic factor(Bdnf)in telencephalon.Taken to-gether,our results provide behavioural evidence of spatial memory associated with visual cue in C. auratus,which could be regulated by ERK1/2–CREB–SirT1–Bdnf pathway.Keywords Spatial memoryÁVisual cueÁCarassius auratusÁNicotinamideÁERK1/2ÁCREBÁSirT1ÁBdnfIntroductionLike mammals,fishes are able to learn new task from unfamiliar start point using spatial representation and visual cues(Rodrı´guez et al.1994;Ingle and Sahagian 1973;Salas et al.1996).This indispensable capacity allows them to develop adoptive behaviour in a challenging environment for promoting their survival (Pause et al.2013).Infishes,food reward task has been used successfully to assess different forms of memory such as spatial learning(Warburton1990;Lo´pez et al. 1999),associative spatial learning(Portavella and Vargas2005;Sison and Gerlai2010;Dura´n et al. 2010)and colour discrimination(Zerbolio1985; Colwill et al.2005).Earlier study reported that goldfish can remember the food patches on the basis of landmarks and adopt patch sampling method when the food location was changed(Warburton1990), K.E.Rajan(&)ÁS.ThangaleelaÁC.BalasundaramDepartment of Animal Science,School of Life Sciences,Bharathidasan University,Tiruchirappalli620024,Indiae-mail:emmanuvel1972@123 Fish Physiol BiochemDOI10.1007/s10695-015-0038-9which help to perform spatial tasks and remember to locate their food type and place in the habitat.Several studies have shown that the cyclic AMP response element binding protein(CREB)critically regulates spatial memory(Alberini2009;Barco and Marie2011;Suzuki et al.2011).CREB can be activated through the activation/phosphorylation of mitogen-activated protein kinase(MAPK)and extra-cellular signal-regulated kinase(ERK1/2)(Impey et al. 1999;Sweatt2001;Cavanaugh et al.2001;Bozon et al. 2003).Phosphorylated/activated CREB stimulates the recruitment of CREB binding protein(CBP)and induces the expression of CREB-regulated genes (Tao et al.1998;Wu et al.2001;Impey et al.2002; Fusco et al.2012).It has been reported that brain-derived neurotrophic factor(BDNF)is considered as a neuronal marker reporting the activation of CREB in hippocampus-dependent memory(Kawashima et al. 2009),which is involved in synaptic transmission and synaptic plasticity(Izquierdo and Medina1997;Mor-ris2003;Whitlock et al.2006).Of note,a class of functional interplay describes that CREB regulates the transcription of Sirtuin1(SIRT1),and in turn,SIRT1 regulates transcriptional activity of CREB(Fusco et al. 2012).SIRT1is a mammalian homologue of yeast Sir2,nicotinamide(NAD?)-dependent deacetylases that are highly conserved from bacteria to human (Kaeberlein et al.1999;Imai et al.2000).SIRT1 expression has been reported in different brain regions, which is implicated in different cellular pathways that include synaptic plasticity and memory formation (Vaquero et al.2004;Gao et al.2010).Therefore,we designed the experiment to test the role of SIRT1–CREB pathway in spatial learning with reference to associative stimulus in goldfish(Carassius auratus). Here,we present the data describing that inhibition of SIRT1down-regulates ERK1/2–CREB and Bdnf ex-pression in telencephalon,and impairing learning and memory formation in goldfish.Materials and methodsAnimalsExperimentally naive goldfishes(C.auratus,body length±6.5–7.5cm)were purchased from a local supplier and housed as small groups(n=6per tank at a time)at home tanks(circular tank with40cm diameter and30cm heights)with sufficient aeration and constant room temperature(20±2°C,12-h light/dark cycle).Food pellets(Taiyo pet products Pvt.Ltd.,India)were provided daily(9:00,14:00and 18:00)ad libitum,during the habituation and explo-ration period at their home tank.Coherently,fishes were fed at the experimental tank during the training sessions.Both the home and experiment tank water was changed every alternative day to ward off the debris from the water.Individualfishes’phenotype was noted for identification.ApparatusExperimental tank(90930930cm,see Fig.1) was constructed with glass,which consists of a starting chamber(SC)and a portable task chamber(TC, 25928930cm).The’‘gate(gate1,G1) was placed between SC and TC,and the space between G1and TC was considered as exploration chamber(EC).Task chamber was divided into two compartments with a black sheet to provide two different visual cues(green and red LED lights; voltage,AC90–240V,50–60Hz;current,AC0.1A, 0.8W,power, 2.5W).These two chambers were designated as green compartment(GC)and red compartment(RC),and respective(green/red)LED lights were placed perpendicularly.Drug administrationNicotinamide(NAM,#BCBD0222V,Sigma USA)is the precursor for the coenzyme b-nicotinamide adenine dinucleotide(NAD?)and an inhibitor of Sirtuin1-/class III NAD?-dependenthistoneFig.1Schematic diagram of experimental apparatus with SC, G1,EC and portable TC.The dark-shaded box at the right side of the apparatus indicates the position of visual cues.Numbers (in cm)show the linear dimensions of the apparatusFish Physiol Biochem123deacetylase(HDAC)(Bitterman et al.2002).NAM was dissolved in autoclaved double-distilled water (DD H2O,VEH).NAM(1000mg/kg,10l l)was administered intra-peritoneally(i.p)to goldfish using a Hamilton syringe with a3-mm needle(Ko¨ppen et al. 1996;Yang et al.2004).Experimental subjectsBehavioural analysisFishes were randomly assigned into two groups that were treated with either NAM(n=25)or VEH (n=26)an hour before the behavioural training.Behavioural studyFishes were allowed to explore the experimental tank for5days(15min/day)without gates and visual cues. Exploration started with a shoal size,which was gradually decreased to individual on thefinal day of exploration.This will help to minimize stress on individuals.After exploration,training was provided to eachfish individually for5days(2trials/day, 15min/trial).Fish was released at SC and trained to cross G1and to enter the TC(GC/RC)using visual cues(red/green LED lights).On trials,both cues were presented simultaneously andfishes were allowed to enter both chambers to search for food.For each trial, red and green LED lights were positioned randomly, but food pellets were provided only at GC.This allowed us to test the memory associated with where (green-light compartment)and what(food).Initially,fishes showed exploratory behaviour to cross G1by made bumping against G1,which is considered as an attempt.Further,the latency(time taken by thefish to completely cross respective barrier)to cross the G1or enter to GC/RC was calculated.Attempts made at the walls of G1were only taken for analysis,and those made at the walls of the experimental apparatus were not included for analysis.The trial was considered successful when thefish crossed G1and entered into GC/RC.If thefish poked into GC,it will be considered as correct choice;if they poked into RC,it will be considered as wrong choice.Once they made correct choice,they received food reward;if they made wrong choice,they received food only at their home tank. After the behavioural training,thefishes were trans-ferred back to their home tank and allowed to stay for 5days;this tranquil period was considered as the inter-experimental period.During this period,food pellet was provided at their home tank three times/day. Retention test was conducted following the same procedure as used in the training session.The entire process was video recorded;parameters such as individual’s attempts,latency to cross G1,GC and correct/wrong choices were analysed.Gene expression studiesFishes were randomly assigned into two groups (n=15for each group),and they were treated with either NAM or VEH.Again,NAM and VEH groups were categorized as untrained(VEH,NAM)and trained groups(vehicle trained,VT;NAM trained, NT).Untrained groups were killed1h after VEH/ NAM treatment.Trained groups were subjected to behavioural training1h after VEH/NAM treatment and were killed30min(VT-300and NT-300)and 60min(VT-600and NT-600)after behavioural training.RNA isolation and cDNA synthesisUntrained and trained groupfishes were anaesthetized by immersing in water containing tricainemethanosul-fonate(1:20,000).Once the movement ended,fish was placed in the surgical board partially submerged in water with anaesthetic andfixed in position by lateral holders;dorsal skin and skull were carefully detached, and then,telencephalon was dissected out(Dura´n et al. 2000).Total RNA was isolated using TRIzol(Merck Specialties Pvt.Ltd.,India),according to the manufac-turer’s instructions and stored with RNase inhibitor (GeNei TM,Merck Specialties Pvt.Ltd.,India)at -70°C.Total RNA(1.0l g/sample)was reverse transcribed into cDNA using random/oligo-dT pri-mers(iScript TM cDNA Synthesis Kit,Bio-Rad Laboratories Inc.).Quantitative real-time PCRExpression level of specific genes was quantified by quantitative real-time PCR(qRT-PCR)in a total volume of10l l containing an aliquot of RT mixture (SsoAdvanced TM SYBRÒGreen Supermix,Bio-Rad Laboratories Inc.),specific primers(10l M)and cDNA(0.1l g).The qRT-PCRs were performed usingFish Physiol Biochem123最及时的文献传递服务丆pubmed,ovid,embase,medline,highwire,thieme,karger,cochrane library,F1000,infomahealthcare,neurosurgery等数据库丆五分钟内及时传递服务丆帮你快速找到所需文档丆6元/篇丆欢迎咨询丆qq•F51665111医学文献资源互助交流QQ群 421662488医学电子书文献资源交流。