Biophysicist (Rockville, Md.)最新文献_第4页

Reflections on COVID-19–induced online teaching in biophysics courses 新冠肺炎诱发生物物理在线教学的思考

Biophysicist (Rockville, Md.) Pub Date : 2021-06-29 DOI: 10.35459/tbp.2021.000183

S. Klumpp, Sarah Köster, A. Pawsey, Yvonne Lips, M. Wenderoth, P. Klein

引用次数: 1

Updating MoonProt From Home: An Online Student Research Project During the COVID-19 Pandemic 在家更新MoonProt: COVID-19大流行期间的在线学生研究项目

Biophysicist (Rockville, Md.) Pub Date : 2021-06-29 DOI: 10.35459/tbp.2021.000190

C. Jeffery

引用次数: 1

An Interactive Framework for Teaching Viscoelastic Modeling 一个交互式的粘弹性建模教学框架

Biophysicist (Rockville, Md.) Pub Date : 2021-06-25 DOI: 10.35459/tbp.2020.000169

Delf Kah, B. Fabry, Richard C. Gerum

{"title":"An Interactive Framework for Teaching Viscoelastic Modeling","authors":"Delf Kah, B. Fabry, Richard C. Gerum","doi":"10.35459/tbp.2020.000169","DOIUrl":"https://doi.org/10.35459/tbp.2020.000169","url":null,"abstract":"\u0000 Rheologic models consisting of combinations of linear elements, such as springs and dashpots, are widely used in biophysics to describe the mechanical and, in particular, the viscoelastic behavior of proteins, cells, tissue, and soft matter. Even simple arrangements with few elements often suffice to recapitulate the experimental data and to provide biophysical insights, making them an ideal subject for educational purposes. To provide students with an intuitive understanding of the mechanical behavior of spring and dashpot models, we describe a computer simulation tool, elastic viscous system simulator (ElViS), written in the JavaScript programming language for designing viscoelastic models via a graphical user interface and simulating the mechanical response to various inputs. As an example application, we designed a virtual laboratory course using ElViS that teaches the basic principles of viscoelastic modeling in a gamelike manner. We then surveyed 50 undergraduate students of a 1-semester course in biophysics who participated in the virtual laboratory course. Students felt that the course was a helpful addition to the lecture and that it improved learning success.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41857465","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

Reaching Behind the Black Screen 触及黑屏背后

Biophysicist (Rockville, Md.) Pub Date : 2021-06-25 DOI: 10.35459/tbp.2021.000179

Shira Passentin

引用次数: 0

Report on BASICS: Lesson Plan on Aerosols and Infection BASICS报告：气溶胶和感染课程计划

Biophysicist (Rockville, Md.) Pub Date : 2021-05-27 DOI: 10.35459/TBP.2021.000176

Rebecca D. Hogewood, S. Endow

引用次数: 0

What Worked, What Did Not: University Instruction during a Pandemic 什么有效，什么无效：大流行病期间的大学教学

Biophysicist (Rockville, Md.) Pub Date : 2021-05-14 DOI: 10.35459/TBP.2021.000181

Neil A. Manson, R. M. Wadkins

引用次数: 0

Beyond Ticking Boxes: Holistic Assessment of Travel Award Programs is Essential for Inclusivity 超越复选框:全面评估旅游奖励计划对包容性至关重要

Biophysicist (Rockville, Md.) Pub Date : 2021-05-10 DOI: 10.31219/osf.io/fsrpb

C. Etson, Kirsten F. Block, Michael D. Burton, Ashanti Edwards, Sonia C. Flores, Catherine Fry, Ashley N. Guillory, S. Ingram, Richard McGee, D. Neely-Fisher, Stephanie Paxson, Laura Phelan, C. Primus, Kirsta Suggs, Leticia Vega, Elizabeth Vuong, L. Hammonds-Odie, Michael J. Leibowitz, M. Zavala, J. Lujan, M. Ramirez-Alvarado, Verónica A. Segarra

引用次数: 3

Quantifying Planarian Behavior as an Introduction to Object Tracking and Signal Processing 量化Planarian行为作为目标跟踪和信号处理的引子

Biophysicist (Rockville, Md.) Pub Date : 2021-04-27 DOI: 10.35459/TBP.2020.000159

N. Stowell, Tapan Goel, Vir Shetty, Jocelyne Noveral, Eva-Maria S. Collins

{"title":"Quantifying Planarian Behavior as an Introduction to Object Tracking and Signal Processing","authors":"N. Stowell, Tapan Goel, Vir Shetty, Jocelyne Noveral, Eva-Maria S. Collins","doi":"10.35459/TBP.2020.000159","DOIUrl":"https://doi.org/10.35459/TBP.2020.000159","url":null,"abstract":"\u0000 Answers to mechanistic questions about biological phenomena require fluency in a variety of molecular biology techniques and physical concepts. Here, we present an interdisciplinary approach to introducing undergraduate students to an important problem in the areas of animal behavior and neuroscience—the neuronal control of animal behavior. In this lab module, students explore planarian behavior by quantitative image and data analysis with freely available software and low-cost resources. Planarians are ∼1–2-cm-long aquatic free-living flatworms famous for their regeneration abilities. They are inexpensive and easy to maintain, handle, and perturb, and their fairly large size allows for image acquisition with a webcam, which makes this lab module accessible and scalable. Our lab module integrates basic physical concepts such as center of mass, velocity and speed, periodic signals, and time series analysis in the context of a biological system. The module is designed to attract students with diverse disciplinary backgrounds. It challenges the students to form hypotheses about behavior and equips them with a basic but broadly applicable toolkit to achieve this quantitatively. We give a detailed description of the necessary resources and show how to implement the module. We also provide suggestions for advanced exercises and possible extensions. Finally, we provide student feedback from a pilot implementation.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42604824","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: 2

Introductory Models of COVID-19 in the United States 美国COVID-19的介绍模型

Biophysicist (Rockville, Md.) Pub Date : 2021-04-18 DOI: 10.35459/tbp.2021.000200

P. Nelson

{"title":"Introductory Models of COVID-19 in the United States","authors":"P. Nelson","doi":"10.35459/tbp.2021.000200","DOIUrl":"https://doi.org/10.35459/tbp.2021.000200","url":null,"abstract":"\u0000 Students develop and test simple kinetic models of the spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Microsoft Excel is used as the modeling platform because it is nonthreatening to students and it is widely available. Students develop finite difference models and implement them in the cells of preformatted spreadsheets following a guided inquiry pedagogy that introduces new model parameters in a scaffolded step-by-step manner. That approach allows students to investigate the implications of new model parameters in a systematic way. Students fit the resulting models to reported cases per day data for the United States using least squares techniques with Excel's Solver. Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the susceptible-infected-recovered (SIR) model. They also discover that the effects of social distancing can be modeled using a Gaussian transition function for the infection rate coefficient and that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing. Students then model the effect of vaccinations and validate the resulting susceptible-infected-recovered-vaccinated (SIRV) model by showing that it successfully predicts the reported cases per day data from Thanksgiving through the holiday period up to 14 February 2021. The same SIRV model is then extended and successfully fits the fourth peak up to 1 June 2021, caused by further relaxation of social distancing measures. Finally, students extend the model up to the present day (27 August 2021) and successfully account for the appearance of the delta variant of the SARS-CoV-2 virus. The fitted model also predicts that the delta variant peak will be comparatively short, and the cases per day data should begin to fall off in early September 2021, counter to current expectations. This case study makes an excellent capstone experience for students interested in scientific modeling.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48736570","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

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. PyRosetta Jupyter笔记本教授生物分子结构预测和设计。

Biophysicist (Rockville, Md.) Pub Date : 2021-04-01 Epub Date: 2021-04-14 DOI: 10.35459/tbp.2019.000147

Kathy H Le, Jared Adolf-Bryfogle, Jason C Klima, Sergey Lyskov, Jason Labonte, Steven Bertolani, Shourya S Roy Burman, Andrew Leaver-Fay, Brian Weitzner, Jack Maguire, Ramya Rangan, Matt A Adrianowycz, Rebecca F Alford, Aleexsan Adal, Morgan L Nance, Yuanhan Wu, Jordan Willis, Daniel W Kulp, Rhiju Das, Roland L Dunbrack, William Schief, Brian Kuhlman, Justin B Siegel, Jeffrey J Gray

{"title":"PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.","authors":"Kathy H Le, Jared Adolf-Bryfogle, Jason C Klima, Sergey Lyskov, Jason Labonte, Steven Bertolani, Shourya S Roy Burman, Andrew Leaver-Fay, Brian Weitzner, Jack Maguire, Ramya Rangan, Matt A Adrianowycz, Rebecca F Alford, Aleexsan Adal, Morgan L Nance, Yuanhan Wu, Jordan Willis, Daniel W Kulp, Rhiju Das, Roland L Dunbrack, William Schief, Brian Kuhlman, Justin B Siegel, Jeffrey J Gray","doi":"10.35459/tbp.2019.000147","DOIUrl":"https://doi.org/10.35459/tbp.2019.000147","url":null,"abstract":"Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":"2 1","pages":"108-122"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813091/pdf/nihms-1767020.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39593184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0