例谈解决高中生物学教学中“内容多”与“课时少”矛盾的策略

结合教学案例,提出下列策略来解决高中生物学教学中"内容多"与"课时少"矛盾:规划好模块的整体教学计划;制定详细、具体、可检测的教学目标,同时教学时要突出主要目标;设计好学生"学"的活动;处理好高中初中生物学教学和不同模块教学的衔接。     

浅谈高中生物教学中分层走班的实践策略

在传统班级授课模式下,教师主要是组织统一化、标准化和同步化的教学,忽略了学生之间的个体差异,影响了课堂教学的有效性。随着"因材施教"观念的深入人心,关于"分层走班教学"模式的研究越来越多。对此,在分析国内外"分层走班教学"研究现状的基础上,结合高中生物教学的实际,重点就"分层走班教学"模式的构建与实施进行重点探讨。     

“动物微生物学”课程体系的建设与教学改革探索

针对动物科学学院多专业、多方向的特点,进行了"动物微生物学"课程体系建设与改革的探索。从组建跨专业的教学团队入手,通过新的教学理念和教学方法,并充分利用网络教学资源,以激发学生的学习兴趣,全面培养学生的综合素质和创新能力,适合时代发展的需要。     

基于情境模式的“核技术生态安全”专题设计

在课程设计的"情境模式"理论指导下,从学科、学生、教师、学校等层面进行全面分析,尝试对"核技术应用的生态安全"专题进行开发设计,并通过专题教学后的自我反思、学生反馈和同行评价,不断改进专题设计和教学实施。结合该专题的开发,总结了专题开发的"多轮循环"模式。     

“细胞的增殖”(第1课时)的教学设计

针对浙科版"细胞的增殖"教学中染色体、DNA等知识点多且抽象难懂,在教学中采用视频动画、图片展示、构建模型和小组合作等形式,体现以学定教的教学理念,发挥教师的主导作用,促进学生的自主学习。     

利用图表突破“血液循环”教学重、难点

心脏结构和血液循环途径的知识是"血液循环"一节的教学重、难点,概念多且抽象,学生易混淆、难理解。在教学中采用挂图、模型、图表等直观教具辅助教学,使学生的学习由抽象变为具体,既帮助学生学习和理解,又突出教学重点、突破教学难点,使教师易教、学生易学,取得较好的教学效果。     

基础医学实验教学的三段式多学科整合改革

分析了基础实验课教学存在的问题,提出并实施了"三段式多学科融合性"基础医学实验教学改革,通过医德修养渗透和临床技能初步规范、基础性实践能力培养、整合式基础医学临床技能培养三个阶段,真正贯彻了"以学生为中心,早临床、多临床和反复临床,基础与临床交叉融通"的教学理念。     

创建多层次微生物学实验教学体系培养科研创新型人才

"微生物学"课程是高等教育生命科学教学中一门重要的基础课程。微生物学实验作为独立于微生物学理论课的基础实验课,对学生基本实验技能的培养和综合素质的提高,培养学生探索精神、创新意识和创新能力方面起着至关重要的作用。南开大学微生物学教学实验室依托"微生物学"国家重点学科、"教育部分子微生物学重点实验室"和"国家级生物实验教学示范中心",建立了多级别分层次的微生物学实验教学体系;确立了由基础性实验、综合性实验、创新性实验相结合的实验内容;同时充分利用本校教学科研资源优势,为学生搭建了多级别的科研创新训练实践平台,着重培养学生的科研素质、创新意识和创新能力,在微生物学科研创新型人才的培养中发挥了重要的作用。     

网络教学平台在研究生“高级微生物学”教学中的应用实践

网络教学平台资源丰富,功能强大,越来越多地运用于高校教学。探索将网络教学平台引入研究生"高级微生物学"课程的教学改革。结果表明,"高级微生物学"网络教学平台激发了研究生的学习兴趣,提高了学生的自主学习能力,增加了师生互动,增强了学生的创新意识。     

不同教学方法在“糖代谢”教学中的探讨与应用

教学方法是实现知识传递、能力培养及价值观树立等不同层次教学目标的有效措施保证。糖代谢包括代谢反应过程、代谢生理意义及代谢过程调节,其中代谢反应过程涉及复杂的化学反应式,教学内容多且难点突出。以"糖代谢"教学为研究对象,探讨不同教学方法的应用与教学效果。分析比较教学法对于知识传递教学目标的重要性,问题教学法在提升能力培养教学目标中的可行性及案例教学法是学习态度和价值观教学目标实现的重要手段,旨在提高《生物化学》课程的教学质量。     

整合现代教育技术与建构主义学习理论的基因组学创新教学模式

为了克服传统教学方式的各种弊端, 作为广东省分子生物学精品课程体系建设的内容之一, 对当今生命科学研究的热点领域—— 基因组学的教学模式进行了改革, 其中包括采用外国原版教材、进行双语教学以及以建构主义为指导的专题讨论教学法与多媒体辅助教学。在考试形式和实验教学方面, 则采用了多元化考核方式和增添了设计性实验。通过对基因组学教学的改革, 逐步完善了现代分子生物学课程体系的建设。     

The dysregulation of miRNAs in epilepsy and their regulatory role in inflammation and apoptosis

Functional & Integrative Genomics - As alcohol consumption increases, alcoholic liver disease (ALD) has become more popular and is threating our human life. In this study, we found mulberry...     

Oral Session Abstracts

For more than two decades Genomics has grown as a discipline separately from Genetics. During this period the HGP was completed, other species were analyzed and the technology dramatically incremented. Methods for nucleic acids analysis now include the NexGen sequencing technologies for analysis of whole genomes and new DNA capture methods that allow sequencing of every human exon in a single sample. As a consequence, we are beginning to accrue the raw sequence of the whole genomes of multiple individuals and cell types. These methods are also being directed at finding specific disease alleles, and to characterize the full spectrum of variation in human populations as well as to study cancer. In combination these technologies provide unprecedented opportunity to study fundamental questions in biology. At long last the two disciplines of Genetics and Genomics are coming back together.     

Classification and Error Estimation for Discrete Data

Discrete classification is common in Genomic Signal Processing applications, in particular in classification of discretized gene expression data, and in discrete gene expression prediction and the inference of boolean genomic regulatory networks. Once a discrete classifier is obtained from sample data, its performance must be evaluated through its classification error. In practice, error estimation methods must then be employed to obtain reliable estimates of the classification error based on the available data. Both classifier design and error estimation are complicated, in the case of Genomics, by the prevalence of small-sample data sets in such applications. This paper presents a broad review of the methodology of classification and error estimation for discrete data, in the context of Genomics, focusing on the study of performance in small sample scenarios, as well as asymptotic behavior.Key Words: Genomics, classification, error estimation, discrete histogram rule, sampling distribution, resubstitution, leave-one-out, ensemble methods, coefficient of determination.     

Data mining of transcriptional biomarkers at different cotton fiber developmental stages

Functional & Integrative Genomics - Advancement of the gene expression study provides comprehensive information on pivotal genes at different cotton fiber development stages. For the betterment...     

Ecological genomics—changing perspectives on Darwin’s basic concerns

Ecological Genomics is an interdisciplinary field that seeks to understand the genetic and physiological basis of species interactions for evolutionary inferences. At the 7th annual Ecological Genomics Symposium, November 13–15, 2009, members of the Ecological Genomics program at Kansas State University invited 13 speakers and 56 poster presentations.     

Single Cell Genomics meeting in Stockholm: from single cells to cell types

A report on the second Single Cell Genomics conference held in Stockholm, Sweden, September 9–11, 2014.The second Single Cell Genomics conference was held in Stockholm and hosted by the Karolinska Institute. The ‘Venice of the North’ was the setting for an exciting and intense meeting, with scientists from very different disciplines tackling the numerous challenges that single cell genomics presents.During three days, 35 talks and more than 60 posters addressed many aspects of single cell genomics, from new experimental techniques to computational strategies for data analysis. We report on some of the main themes that emerged and, in our opinion, best illustrate the progress made and the new directions being undertaken by the field.     

The 4th Bologna Winter School: hot topics in structural genomics

The 4th Bologna Winter School on Biotechnologies was held on 9-15 February 2003 at the University of Bologna, Italy, with the specific aim of discussing recent developments in bioinformatics. The school provided an opportunity for students and scientists to debate current problems in computational biology and possible solutions. The course, co-supported (as last year) by the European Science Foundation program on Functional Genomics, focused mainly on hot topics in structural genomics, including recent CASP and CAPRI results, recent and promising genomewide predictions, protein-protein and protein-DNA interaction predictions and genome functional annotation. The topics were organized into four main sections (http://www.biocomp.unibo.it).     

Return of Genomic Results to Research Participants: The Floor,the Ceiling,and the Choices In Between

As more research studies incorporate next-generation sequencing (including whole-genome or whole-exome sequencing), investigators and institutional review boards face difficult questions regarding which genomic results to return to research participants and how. An American College of Medical Genetics and Genomics 2013 policy paper suggesting that pathogenic mutations in 56 specified genes should be returned in the clinical setting has raised the question of whether comparable recommendations should be considered in research settings. The Clinical Sequencing Exploratory Research (CSER) Consortium and the Electronic Medical Records and Genomics (eMERGE) Network are multisite research programs that aim to develop practical strategies for addressing questions concerning the return of results in genomic research. CSER and eMERGE committees have identified areas of consensus regarding the return of genomic results to research participants. In most circumstances, if results meet an actionability threshold for return and the research participant has consented to return, genomic results, along with referral for appropriate clinical follow-up, should be offered to participants. However, participants have a right to decline the receipt of genomic results, even when doing so might be viewed as a threat to the participants’ health. Research investigators should be prepared to return research results and incidental findings discovered in the course of their research and meeting an actionability threshold, but they have no ethical obligation to actively search for such results. These positions are consistent with the recognition that clinical research is distinct from medical care in both its aims and its guiding moral principles.