The Journal of Computational Science Education最新文献_第7页

Teaching and Learning Graph Algorithms Using Animation 使用动画教学和学习图算法

The Journal of Computational Science Education Pub Date : 2018-12-01 DOI: 10.22369/ISSN.2153-4136/9/2/3

Y. Liang

引用次数: 0

Physics Conceptual Understanding in a Computational Science Course 计算科学课程中的物理概念理解

The Journal of Computational Science Education Pub Date : 2018-12-01 DOI: 10.22369/ISSN.2153-4136/9/2/1

Rivka Taub, M. Armoni, M. Ben-Ari

{"title":"Physics Conceptual Understanding in a Computational Science Course","authors":"Rivka Taub, M. Armoni, M. Ben-Ari","doi":"10.22369/ISSN.2153-4136/9/2/1","DOIUrl":"https://doi.org/10.22369/ISSN.2153-4136/9/2/1","url":null,"abstract":"Students face many difficulties dealing with physics principles and concepts during physics problem solving. For example, they lack the understanding of the components of formulas, as well as of the physical relationships between the two sides of a formula. To overcome these difficulties some educators have suggested integrating simulations design into physics learning. They claim that the programming process necessarily fosters understanding of the physics underlying the simulations. We investigated physics learning in a high-school course on computational science. The course focused on the development of computational models of physics phenomena and programming corresponding simulations. The study described in this paper deals with the development of students' conceptual physics knowledge throughout the course. Employing a qualitative approach, we used concept maps to evaluate students' physics conceptual knowledge at the beginning and the end of the model development process, and at different stages in between. We found that the students gained physics knowledge that has been reported to be difficult for high-school and even undergraduate students. We use two case studies to demonstrate our method of analysis and its outcomes. We do that by presenting a detailed analysis of two projects in which computational models and simulations of physics phenomena were developed.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114449994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parsing Next Generation Sequencing Data in Parallel Environments for Downstream Genetic Variation Analysis 在并行环境中解析下一代测序数据用于下游遗传变异分析

The Journal of Computational Science Education Pub Date : 2018-12-01 DOI: 10.22369/issn.2153-4136/9/2/5

Mariana Vasquez, J. Mohl, M. Leung

{"title":"Parsing Next Generation Sequencing Data in Parallel Environments for Downstream Genetic Variation Analysis","authors":"Mariana Vasquez, J. Mohl, M. Leung","doi":"10.22369/issn.2153-4136/9/2/5","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/9/2/5","url":null,"abstract":"With the recent advances in next generation sequencing technology, analysis of prevalent DNA sequence variants from patients with a particular disease has become an important tool for understanding the associations between the disease and genetic mutations. A publicly accessible bioinformatics pipeline, called OncoMiner (http://oncominer.utep.edu), was implemented in 2016 to help biomedical researchers analyze large genomic datasets from patients with cancer. However, the current version of OncoMiner can only accept input files with a highly specific format for sequence variant description. In order to handle data from a broader range of sequencing platforms, a data preprocessing tool is necessary. We have therefore implemented the OncoMiner Preprocessing (OP) program for parsing data files in the popular FastQ and BAM formats to generate an OncoMiner input file. OP involves using the open source Bowtie2 and SAMtools software, followed by a python script we developed for genetic sequence variant identification. To preprocess very large datasets efficiently, the OP program has been parallelized on two local computers and the Blue Waters system at the National Center for Supercomputing Applications using a multiprocessing approach. Although reasonable parallelization efficiency has been obtained on the local computers, the OP program’s speedup on Blue Waters has been limited, possibly due to I/O issues and individual node memory constraints. Despite these, Blue Waters has provided the necessary resources to process 35 datasets from patients with acute myeloid leukemia and demonstrated significant correlation of OP runtimes with the BAM input size and chromosome diversity.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125744530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Identification of Active Oligonucleotide Sequences Using Artificial Neural Network 利用人工神经网络识别活性寡核苷酸序列

The Journal of Computational Science Education Pub Date : 2018-12-01 DOI: 10.22369/ISSN.2153-4136/9/2/4

A. Luke, Sarah Fergione, Riley Wilson, B. Gunn, S. Svojanovsky

{"title":"Identification of Active Oligonucleotide Sequences Using Artificial Neural Network","authors":"A. Luke, Sarah Fergione, Riley Wilson, B. Gunn, S. Svojanovsky","doi":"10.22369/ISSN.2153-4136/9/2/4","DOIUrl":"https://doi.org/10.22369/ISSN.2153-4136/9/2/4","url":null,"abstract":"In this project we designed an Artificial Neural Network (ANN) computational model to predict the activity of short oligonucleotide sequences (octamers) with important biological role as exonic splicing enhancers (ESE) motifs recognized by human SR protein SC35. Since only active sequences were available from the literature as our initial data set, we generated an additional set of complementary sequences to the original set. We used back-propagation neural network (BPNN) with MATLAB® Neural Network ToolboxTM on our research designated computer. In Stage I of our project we trained, validated and tested the BPNN prototype. We started with 20 samples in the training and 8 samples in the validation sets. Trained and validated BPNN prototype was then used to test the unique set of 10 octamer sequences with 5 active samples and their 5 complementary sequences. The test showed 2 classification errors, one false positive and the other false negative. We used the test data and moved into Stage II of the project. First, we analyzed the initial DNA numerical representation (DNR) and changed the scheme to achieve higher difference between the subsets of active and complementary sequences. We compared the BPNN results with different numbers of nodes in the second hidden layer to optimize model accuracy. To estimate future model performance we needed to test the classifier on newly collected data from another paper. This practical application included the testing of 41 published, non-repeating SC35 ESE motif octamers, together with 41 complementary sequences. The test showed high BPNN accuracy in the predictive power for both (active and inactive) categories. This study shows the potential for using a BPNN to screen SC35 ESE motif candidates.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133130299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Feature Selection in Markov State Models Using Genetic Algorithm 基于遗传算法的马尔可夫状态模型特征自动选择

The Journal of Computational Science Education Pub Date : 2018-06-01 DOI: 10.22369/issn.2153-4136/9/2/2

Qihua Chen, Jiangyan Feng, S. Mittal, D. Shukla

引用次数: 6

Scientific Computing, High-Performance Computing and Data Science in Higher Education 高等教育中的科学计算、高性能计算和数据科学

The Journal of Computational Science Education Pub Date : 2016-04-19 DOI: 10.22369/issn.2153-4136/10/1/5

Marcelo Ponce, Erik Spence, D. Gruner, R. Zon

引用次数: 5

Potential Influence of Prior Experience in an Undergraduate-Graduate Level HPC Course 先前经验对本科研究生水平HPC课程的潜在影响

The Journal of Computational Science Education Pub Date : 1900-01-01 DOI: 10.22369/issn.2153-4136/10/1/15

C. Fietkiewicz

引用次数: 0

Student Outcomes in Parallelizing Recursive Matrix Multiply 并行递归矩阵乘法的学生结果

The Journal of Computational Science Education Pub Date : 1900-01-01 DOI: 10.22369/issn.2153-4136/10/1/4

C. Fietkiewicz

引用次数: 1

Extending XSEDE Innovations to Campus Cyberinfrastructure - The XSEDE National Integration Toolkit 将XSEDE创新扩展到校园网络基础设施——XSEDE国家集成工具包

The Journal of Computational Science Education Pub Date : 1900-01-01 DOI: 10.22369/issn.2153-4136/10/1/3

Eric Coulter, J. Sprouse, Resa Reynolds, R. Knepper

{"title":"Extending XSEDE Innovations to Campus Cyberinfrastructure - The XSEDE National Integration Toolkit","authors":"Eric Coulter, J. Sprouse, Resa Reynolds, R. Knepper","doi":"10.22369/issn.2153-4136/10/1/3","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/10/1/3","url":null,"abstract":"XSEDE Service Providers (SPs) and resources have the benefit of years of testing and implementation, tuning and configuration, and the development of specific tools to help users and systems make the best use of these resources. Cyberinfrastructure professionals at the campus level are often charged with building computer resources which are compared to these national-level resources. While organizations and companies exist that guide cyberinfrastructure configuration choices down certain paths, there is no easy way to distribute the long-term knowledge of the XSEDE project to campus CI professionals. The XSEDE Cyberinfrastructure Resource Integration team has created a variety of toolkits to enable easy knowledge and best-practice transfer from XESDE SPs to campus CI professionals. The XSEDE National Integration Toolkit (XNIT) provides the software used on most XSEDE systems in an effort to propagate the best practices and knowledge of XSEDE resources. XNIT includes basic tools and configuration that make it simpler for a campus cluster to have the same software set and many of the advantages and XSEDE SP resource affords. In this paper, we will detail the steps taken to build such a library of software and discuss the challenges involved in disseminating awareness of toolkits among cyberinfrastructure professionals. We will also describe our experiences in updating the XNIT to be compatible with the OpenHPC project, which forms the basis of many new HPC systems, and appears situated to become the de-facto choice of management software provider for many HPC centers.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115747819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Training Computational Scientists to Build and Package Open-Source Software 培训计算科学家构建和打包开源软件

The Journal of Computational Science Education Pub Date : 1900-01-01 DOI: 10.22369/ISSN.2153-4136/10/1/12

P. Bisbal

引用次数: 2