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Mih, Nathan, Elizabeth Brunk, Ke Chen, Edward Catoiu, Anand Sastry, Erol Kavvas, Jonathan M. Monk, Zhen Zhang, and Bernhard O. Palsson. "ssbio: a Python framework for structural systems biology." Bioinformatics 34, no. 12 (February 12, 2018): 2155–57. http://dx.doi.org/10.1093/bioinformatics/bty077.
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Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.
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Davies, Alan, Frances Hooley, Peter Causey-Freeman, Iliada Eleftheriou, and Georgina Moulton. "Using interactive digital notebooks for bioscience and informatics education." PLOS Computational Biology 16, no. 11 (November 5, 2020): e1008326. http://dx.doi.org/10.1371/journal.pcbi.1008326.
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Interactive digital notebooks provide an opportunity for researchers and educators to carry out data analysis and report the results in a single digital format. Further to just being digital, the format allows for rich content to be created in order to interact with the code and data contained in such a notebook to form an educational narrative. This primer introduces some of the fundamental aspects involved in using Jupyter notebooks in an educational setting for teaching in the bio/health informatics disciplines. We also provide 2 case studies that detail how we used Jupyter notebooks to teach non-coders programming skills on a blended Master’s degree module for a Health Informatics programme and a fully online distance learning unit on Programming for a postgraduate certificate (PG Cert) in Clinical Bioinformatics with a more technical audience.
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C. A. P. and E. B. Wilson. "Laboratory notebooks: Sacred works." Plant Molecular Biology Reporter 8, no. 4 (November 1990): 220–22. http://dx.doi.org/10.1007/bf02668757.
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Bavins, Terry. "Paper weight?: Electronic lab notebooks." Biochemist 24, no. 6 (December 1, 2002): 19–22. http://dx.doi.org/10.1042/bio02406019.
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The ongoing pressure to find the next blockbuster drug is felt nowhere more keenly than in the area of drug discovery, in which ever-increasing volumes of data need to be thoroughly analysed and likely candidates must be rapidly identified and patented. IT is already essential to the generation and processing of raw data in most drugdiscovery labs (the huge forward leaps in genomics, for example, would be unthinkable without it), and this was the area where the need for automation was initially most obvious and pressing.
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Medley, J. Kyle, Kiri Choi, Matthias König, Lucian Smith, Stanley Gu, Joseph Hellerstein, Stuart C. Sealfon, and Herbert M. Sauro. "Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology." PLOS Computational Biology 14, no. 6 (June 15, 2018): e1006220. http://dx.doi.org/10.1371/journal.pcbi.1006220.
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Nelson, Andrew R. J., and Stuart W. Prescott. "refnx: neutron and X-ray reflectometry analysis in Python." Journal of Applied Crystallography 52, no. 1 (February 1, 2019): 193–200. http://dx.doi.org/10.1107/s1600576718017296.
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refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log-likelihood and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple data sets and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least-squares (global and gradient-based optimizers) and a Bayesian approach using a Markov-chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses.
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Gomez, Noe A. "Using e‐notebooks to explore biochemistry with an agricultural lens." Biochemistry and Molecular Biology Education 48, no. 6 (November 2020): 667–69. http://dx.doi.org/10.1002/bmb.21467.
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BOOMER, SARAH M., DANIEL P. LODGE, and BRYAN E. DUTTON. "Bacterial Diversity Studies Using the 16S rRNA Gene Provide a Powerful Research-Based Curriculum for Molecular Biology Laboratory." Microbiology Education 3, no. 1 (May 2002): 18–25. http://dx.doi.org/10.1128/me.3.1.18-25.2002.
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We have developed a ten-week curriculum for molecular biology that uses 16S ribosomal RNA genes to characterize and compare novel bacteria from hot spring communities in Yellowstone National Park. The 16S rRNA approach bypasses selective culture-based methods. Our molecular biology course offered the opportunity for students to learn broadly applicable methods while contributing to a long-term research project. Specifically, students isolated and characterized clones that contained novel 16S rRNA inserts using restriction enzyme, DNA sequencing, and computer-based phylogenetic methods. In both classes, students retrieved novel bacterial 16S rRNA genes, several of which were most similar to Green Nonsulfur bacterial isolates. During class, we evaluated student performance and mastery of skills and concepts using quizzes, formal lab notebooks, and a broad project assignment. For this report, we also assessed student performance alongside data quality and discussed the significance, our goal being to improve both research and teaching methods.
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DiBartolomeis, Susan M., and James P. Moné. "Apoptosis: A Four-Week Laboratory Investigation for Advanced Molecular and Cellular Biology Students." Cell Biology Education 2, no. 4 (December 2003): 275–95. http://dx.doi.org/10.1187/cbe.03-06-0027.
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Over the past decade, apoptosis has emerged as an important field of study central to ongoing research in many diverse fields, from developmental biology to cancer research. Apoptosis proceeds by a highly coordinated series of events that includes enzyme activation, DNA fragmentation, and alterations in plasma membrane permeability. The detection of each of these phenotypic changes is accessible to advanced undergraduate cell and molecular biology students. We describe a 4-week laboratory sequence that integrates cell culture, fluorescence microscopy, DNA isolation and analysis, and western blotting (immunoblotting) to follow apoptosis in cultured human cells. Students working in teams chemically induce apoptosis, and harvest, process, and analyze cells, using their data to determine the order of events during apoptosis. We, as instructors, expose the students to an environment closely simulating what they would encounter in an active cell or molecular biology research laboratory by having students coordinate and perform multiple tasks simultaneously and by having them experience experimental design using current literature, data interpretation, and analysis to answer a single question. Students are assessed by examination of laboratory notebooks for completeness of experimental protocols and analysis of results and for completion of an assignment that includes questions pertaining to data interpretation and apoptosis.
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Ghannoum, Salim, Waldir Leoncio Netto, Damiano Fantini, Benjamin Ragan-Kelley, Amirabbas Parizadeh, Emma Jonasson, Anders Ståhlberg, Hesso Farhan, and Alvaro Köhn-Luque. "DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics." International Journal of Molecular Sciences 22, no. 3 (January 30, 2021): 1399. http://dx.doi.org/10.3390/ijms22031399.
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The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.
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Williams, Eleanor, Josh Moore, Simon W. Li, Gabriella Rustici, Aleksandra Tarkowska, Anatole Chessel, Simone Leo, et al. "Image Data Resource: a bioimage data integration and publication platform." Nature Methods 14, no. 8 (June 19, 2017): 775–81. http://dx.doi.org/10.1038/nmeth.4326.
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Abstract Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR). IDR links data from several imaging modalities, including high-content screening, multi-dimensional microscopy and digital pathology, with public genetic or chemical databases and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable reanalysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open-source platform for publishing imaging data. Thus IDR provides an online resource and a software infrastructure that promotes and extends publication and reanalysis of scientific image data.
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Lucas, Alice M., Pearl V. Ryder, Bin Li, Beth A. Cimini, Kevin W. Eliceiri, and Anne E. Carpenter. "Open-source deep-learning software for bioimage segmentation." Molecular Biology of the Cell 32, no. 9 (April 19, 2021): 823–29. http://dx.doi.org/10.1091/mbc.e20-10-0660.
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Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.
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Eldredge, Niles. "Darwin's Other Books: “Red” and “Transmutation” Notebooks, “Sketch,” “Essay,” and Natural Selection." PLoS Biology 3, no. 11 (November 15, 2005): e382. http://dx.doi.org/10.1371/journal.pbio.0030382.
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Rule, Adam, Amanda Birmingham, Cristal Zuniga, Ilkay Altintas, Shih-Cheng Huang, Rob Knight, Niema Moshiri, et al. "Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks." PLOS Computational Biology 15, no. 7 (July 25, 2019): e1007007. http://dx.doi.org/10.1371/journal.pcbi.1007007.
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KABAT, ALAN R., and RICHARD E. PETIT. "Glenn Robert Webb (1918–1999), his molluscan taxa,and his journal Gastropodia (1952–1994)." Zootaxa 1589, no. 1 (September 19, 2007): 1–21. http://dx.doi.org/10.11646/zootaxa.1589.1.1.
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Glenn Robert Webb (1918–1999) was a prolific author on the reproductive biology of the terrestrial pulmonates (Mollusca) of the United States. Webb published almost 110 papers, notes and abstracts, many containing detailed descriptions of the functional morphology and reproduction of these gastropods, which remain of value in resolving their phylogeny. Webb described two family-level names (both still considered valid), ten genus-level names (at least seven remain valid), and ten species-level names, all for terrestrial pulmonates. Webb also edited and published the journal Gastropodia, which appeared in thirteen issues, from 1952–1994, and contained extensive original research on the terrestrial pulmonates. Several other malacologists, notably Leslie Hubricht, also published articles and described new species in Gastropodia. This paper provides a biography of Webb, a complete bibliography of his publications, and a list of his taxa, along with a list of other authors’ publications and new taxa in Gastropodia. Webb’s research collection, including some type specimens, and his notebooks, are now housed in the Field Museum of Natural History, Chicago.
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Pillay, Ché S. "Analyzing biological models and data sets using Jupyter notebooks as an alternate to laboratory‐based exercises during COVID‐19." Biochemistry and Molecular Biology Education 48, no. 5 (September 2020): 532–34. http://dx.doi.org/10.1002/bmb.21443.
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Hewera, Michael, Daniel Hänggi, Björn Gerlach, and Ulf Dietrich Kahlert. "eLabFTW as an Open Science tool to improve the quality and translation of preclinical research." F1000Research 10 (April 16, 2021): 292. http://dx.doi.org/10.12688/f1000research.52157.1.
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Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.
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Hewera, Michael, Daniel Hänggi, Björn Gerlach, and Ulf Dietrich Kahlert. "eLabFTW as an Open Science tool to improve the quality and translation of preclinical research." F1000Research 10 (August 2, 2021): 292. http://dx.doi.org/10.12688/f1000research.52157.3.
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Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-science labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.
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Hewera, Michael, Daniel Hänggi, Björn Gerlach, and Ulf Dietrich Kahlert. "eLabFTW as an Open Science tool to improve the quality and translation of preclinical research." F1000Research 10 (May 27, 2021): 292. http://dx.doi.org/10.12688/f1000research.52157.2.
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Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-sceience labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.
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Richards, Richard A. "A review ofCharles Darwin's Notebooks from the Voyage of the Beagle, by Gordon Chancellor and John van Wyhe." Evolution & Development 12, no. 1 (January 2010): 106–7. http://dx.doi.org/10.1111/j.1525-142x.2009.00395.x.
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Keller, André A. "Reaction-diffusion systems in natural sciences and new technology transfer." Journal of the Mechanical Behaviour of Materials 21, no. 3-4 (December 1, 2012): 123–46. http://dx.doi.org/10.1515/jmbm-2012-0024.
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AbstractDiffusion mechanisms in natural sciences and innovation management involve partial differential equations (PDEs). This is due to their spatio-temporal dimensions. Functional semi-discretized PDEs (with lattice spatial structures or time delays) may be even more adapted to real world problems. In the modeling process, PDEs can also formalize behaviors, such as the logistic growth of populations with migration, and the adopters’ dynamics of new products in innovation models. In biology, these events are related to variations in the environment, population densities and overcrowding, migration and spreading of humans, animals, plants and other cells and organisms. In chemical reactions, molecules of different species interact locally and diffuse. In the management of new technologies, the diffusion processes of innovations in the marketplace (e.g., the mobile phone) are a major subject. These innovation diffusion models refer mainly to epidemic models. This contribution introduces that modeling process by using PDEs and reviews the essential features of the dynamics and control in biological, chemical and new technology transfer. This paper is essentially user-oriented with basic nonlinear evolution equations, delay PDEs, several analytical and numerical methods for solving, different solutions, and with the use of mathematical packages, notebooks and codes. The computations are carried out by using the software Wolfram Mathematica®7, and C++ codes.
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Eaton, Deren A. R., and Isaac Overcast. "ipyrad: Interactive assembly and analysis of RADseq datasets." Bioinformatics 36, no. 8 (January 6, 2020): 2592–94. http://dx.doi.org/10.1093/bioinformatics/btz966.
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Abstract Summary ipyrad is a free and open source tool for assembling and analyzing restriction site-associated DNA sequence datasets using de novo and/or reference-based approaches. It is designed to be massively scalable to hundreds of taxa and thousands of samples, and can be efficiently parallelized on high performance computing clusters. It is available both as a command line interface and as a Python package with an application programming interface, the latter of which can be used interactively to write complex, reproducible scripts and implement a suite of downstream analysis tools. Availability and implementation ipyrad is a free and open source program written in Python. Source code is available from the GitHub repository (https://github.com/dereneaton/ipyrad/), and Linux and MacOS installs are distributed through the conda package manager. Complete documentation, including numerous tutorials, and Jupyter notebooks demonstrating example assemblies and applications of downstream analysis tools are available online: https://ipyrad.readthedocs.io/.
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McKenzie, Patrick F., and Deren A. R. Eaton. "ipcoal: an interactive Python package for simulating and analyzing genealogies and sequences on a species tree or network." Bioinformatics 36, no. 14 (May 12, 2020): 4193–96. http://dx.doi.org/10.1093/bioinformatics/btaa486.
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Abstract Summary ipcoal is a free and open source Python package for simulating and analyzing genealogies and sequences. It automates the task of describing complex demographic models (e.g. with divergence times, effective population sizes, migration events) to the msprime coalescent simulator by parsing a user-supplied species tree or network. Genealogies, sequences and metadata are returned in tabular format allowing for easy downstream analyses. ipcoal includes phylogenetic inference tools to automate gene tree inference from simulated sequence data, and visualization tools for analyzing results and verifying model accuracy. The ipcoal package is a powerful tool for posterior predictive data analysis, for methods validation and for teaching coalescent methods in an interactive and visual environment. Availability and implementation Source code is available from the GitHub repository (https://github.com/pmckenz1/ipcoal/) and is distributed for packaged installation with conda. Complete documentation and interactive notebooks prepared for teaching purposes, including an empirical example, are available at https://ipcoal.readthedocs.io/. Contact p.mckenzie@columbia.edu
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Mar'i, Husnul, Ristiono Ristiono, Yosi Laila Rahmi, and Yuni Ahda. "Effect of Biology Module with Scientific Approach Equipped with a Glossary in Discovery Learning models Against Learning Competencies of Class X Students of SMAN 1 Pariaman." Jurnal Atrium Pendidikan Biologi 5, no. 1 (April 30, 2020): 40. http://dx.doi.org/10.24036/apb.v5i1.6765.
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This study is based on the problems that exist in SMAN 1 Pariaman, namely: textbooks that are used have not involved students playing an active role in learning, the learning model that is applied is still monotonous, student notebooks are incomplete and textbooks used in schools are not yet available examples -example/images according to the demands of Basic Competence to clarify the material description. Efforts can be made is the application of biological modules with a scientific approach equipped with a glossary in discovery learning models on the competencies of students in Class X of SMAN 1 Pariaman.The purpose of this study was to determine the effect of biological modules with a scientific approach equipped with a glossary in discovery learning models on the competencies of students in Class X of SMAN 1 Pariaman. This study was a quasi-experimental study with a randomized posttest control group design. The study population was tenth-grade students of SMAN 1 Pariaman 2018/2019 academic year consisting of seven classes. The research sample was taken using a purposive sampling technique, the results of which were selected Class X MIPA 2 as the experimental class and Class X MIPA 3 as the control class.The results of data analysis found that the knowledge competency data was normal and not homogeneous, competency data on attitudes, and homogeneous and normal student skills. Hypothesis test results are known that knowledge competencies (7.06>1.67), attitude competence (0.76<1.67), and skill competencies (0.925<1.67), so it can be concluded that the biological module with a scientific approach is equipped The glossary in discovery learning models has a positive influence on students 'knowledge competencies but does not have a positive influence on the competency of students' attitudes and skills at SMAN 1 Pariaman.
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Mar'i, Husnul, Ristiono Ristiono, Yosi Laila Rahmi, and Yuni Ahda. "The Effect of Biology Module with Scientific Approach Equipped with a Glossary in Discovery Learning Model Against Learning Competencies of Class X Students of SMAN 1 Pariaman." Jurnal Atrium Pendidikan Biologi 4, no. 4 (December 10, 2019): 85. http://dx.doi.org/10.24036/apb.v4i4.7179.
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This study is based on the problems that exist in SMAN 1 Pariaman, namely: textbooks that are used have not involved students playing an active role in learning, the learning model that is applied is still monotonous, student notebooks are incomplete and textbooks used in schools are not yet available example or images according to the demands of basic competencies to clarify the material description. Efforts can be made is the application of biological modules with a scientific approach equipped with a glossary in Discovery Learning models on the competencies of students in Class X of SMAN 1 Pariaman.The purpose of this study was to determine the effect of biological modules with a scientific approach equipped with a glossary in Discovery Learning models on the competencies of students in Class X of SMAN 1 Pariaman. This study was a quasi-experimental study with a randomized posttest control group design. The study population was tenth-grade students of SMAN 1 Pariaman 2018/2019 academic year consisting of seven classes. The research sample was taken using a purposive sampling technique, the results of which were selected Class X MIPA 2 as the experimental class and Class X MIPA 3 as the control class.The results of data analysis found that the knowledge competencies data was normal and not homogeneous, competencies data on attitudes skill are homogeneous and normal. Hypothesis test results are known that knowledge competencies (7,06>1,67), attitude competencies (0,76<1,67), and skill competencies (0,93<1,67), so it can be concluded that the biological module with a scientific approach is equipped a glossary in Discovery Learning models has a positive influence on students' knowledge competencies but does not have a positive influence on the competencies of students' attitudes and skills at SMAN 1 Pariaman.
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Waithe, Dominic. "Summary of two questionnaires designed to understand the research climate for Bioimage Analysts in the UK between 2016-2019." F1000Research 10 (April 6, 2021): 276. http://dx.doi.org/10.12688/f1000research.51794.1.
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Background: Bioimage analysis is an emerging field within the global research community. It is an interdisciplinary discipline which requires knowledge of biology, image analysis and biophysics. This report represents the analysis and discussion of two questionnaires run by the Image Analysis Focused Interest Group of the Royal Microscopical Society (IAFIG-RMS). The goal of this document, which represents the analysis and interpretation of these questionnaires, is to highlight the current research climate for Bioimage Analysts in the UK and discusses some of the problems and possibilities for this emerging discipline. Methods: Two questionnaires (2016 and 2019) were developed and sent to researchers in the UK using mailing lists and forums specific for microscopy and image analysis. The participants were asked a range of questions spanning different aspects of their work and funding. Respondents were collected and analysed using Jupyter notebooks. Results: The analysis of the responses from these questionnaires highlighted many interesting issues and aspects of this community. It is clear that a major issue for the community is the nature of the funding and the long-term career possibilities available. Furthermore, the issue of independence is discussed with clear evidence that researchers would like to pursue their own research with the option of dedicated time to support the research of others. Conclusions: It is our hope that this study will help catalyse funding opportunities which help support this emerging discipline and help it establish a unique identity for itself within the research community in the UK and beyond.
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Walicka-Cupryś, Katarzyna, Renata Skalska-Izdebska, Maciej Rachwał, and Aleksandra Truszczyńska. "Influence of the Weight of a School Backpack on Spinal Curvature in the Sagittal Plane of Seven-Year-Old Children." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/817913.
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The aim of the paper was to determine a correlation between the weight of a child’s backpack, their body weight, and certain features of their body posture.Material and Methods. The study group consisted of 109 children, all aged seven years. The parameters of body posture were determined using the Zebris Ultrasonic System.Results. The number of children carrying a school backpack in accordance with recommendations was 44 subjects (40.37%). Statistically significant changes were found in the total length of the spine (Z=2.223,p=0.026) and between backpack weight and changes in the following parameters: the total length of the spine (rs=-0.3999,p=0.017), the length and the angle of the lumbar lordosis (rs=-0.3352,p=0.049), the angle of the lumbar lordosis (rs=-0.5065,p=0.002), and the sacral angle (rs=-0.4279,p=0.010).Conclusions. Wearing a backpack heavier than 10% of one’s body weight can cause shallowing of the lumbar lordosis and a tendency towards a vertical position of the sacrum. Monitoring the weight of children’s school backpacks and enabling them to leave books and notebooks at school would probably be beneficial in reducing the daily burden put on children’s spines.
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Bergstrand, Lee H., Josh D. Neufeld, and Andrew C. Doxey. "Pygenprop: a Python library for programmatic exploration and comparison of organism genome properties." Bioinformatics 35, no. 23 (June 25, 2019): 5063–65. http://dx.doi.org/10.1093/bioinformatics/btz522.
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Abstract Summary A critical step in comparative genomics is the identification of differences in the presence/absence of encoded biochemical pathways among organisms. Our library, Pygenprop, facilitates these comparisons using data from the Genome Properties database. Pygenprop is written in Python and, unlike existing libraries, it is compatible with a variety of tools in the Python data science ecosystem, such as Jupyter Notebooks for interactive analyses and scikit-learn for machine learning. Pygenprop assigns YES, NO, or PARTIAL support for each property based on InterProScan annotations of open reading frames from an organism’s genome. The library contains classes for representing the Genome Properties database as a whole and methods for detecting differences in property assignments between organisms. As the Genome Properties database grows, we anticipate widespread adoption of Pygenprop for routine genome analyses and integration within third-party bioinformatics software. Availability and implementation Pygenprop is written in Python and is compatible with versions 3.6 or higher. Source code is available under Apache Licence Version 2 at https://github.com/Micromeda/pygenprop. The package can be installed from both PyPi (https://pypi.org/project/pygenprop) and Anaconda (https://anaconda.org/lbergstrand/pygenprop). Documentation is available on Read the Docs (http://pygenprop.rtfd.io/).
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Boulle, A., and J. Kieffer. "High-performance Python for crystallographic computing." Journal of Applied Crystallography 52, no. 4 (July 24, 2019): 882–97. http://dx.doi.org/10.1107/s1600576719008471.
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The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against state-of-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes.
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Burton, Ross J., Raya Ahmed, Simone M. Cuff, Sarah Baker, Andreas Artemiou, and Matthias Eberl. "CytoPy: An autonomous cytometry analysis framework." PLOS Computational Biology 17, no. 6 (June 8, 2021): e1009071. http://dx.doi.org/10.1371/journal.pcbi.1009071.
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Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.
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Shrikumar, Avanti, Eva Prakash, and Anshul Kundaje. "GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs." Bioinformatics 35, no. 14 (July 2019): i173—i182. http://dx.doi.org/10.1093/bioinformatics/btz322.
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Abstract Summary Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing interpretation methods such as deltaSVM, in-silico mutagenesis (ISM) or SHAP either do not scale well or make limiting assumptions about the model that can produce misleading results when the gkm kernel is combined with nonlinear kernels. Here, we propose GkmExplain: a computationally efficient feature attribution method for interpreting predictive sequence patterns from gkm-SVM models that has theoretical connections to the method of Integrated Gradients. Using simulated regulatory DNA sequences, we show that GkmExplain identifies predictive patterns with high accuracy while avoiding pitfalls of deltaSVM and ISM and being orders of magnitude more computationally efficient than SHAP. By applying GkmExplain and a recently developed motif discovery method called TF-MoDISco to gkm-SVM models trained on in vivo transcription factor (TF) binding data, we recover consolidated, non-redundant TF motifs. Mutation impact scores derived using GkmExplain consistently outperform deltaSVM and ISM at identifying regulatory genetic variants from gkm-SVM models of chromatin accessibility in lymphoblastoid cell-lines. Availability and implementation Code and example notebooks to reproduce results are at https://github.com/kundajelab/gkmexplain. Supplementary information Supplementary data are available at Bioinformatics online.
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Bagheri, Hamid, Andrew J. Severin, and Hridesh Rajan. "Detecting and correcting misclassified sequences in the large-scale public databases." Bioinformatics 36, no. 18 (June 24, 2020): 4699–705. http://dx.doi.org/10.1093/bioinformatics/btaa586.
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Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability and implementation Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online.
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Gao, Dasong, Paul R. Barber, Jenu V. Chacko, Md Abdul Kader Sagar, Curtis T. Rueden, Aivar R. Grislis, Mark C. Hiner, and Kevin W. Eliceiri. "FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis." PLOS ONE 15, no. 12 (December 30, 2020): e0238327. http://dx.doi.org/10.1371/journal.pone.0238327.
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In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.
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Arnstein, Larry, Stefan Sigdursson, and Bob Franza. "Ubiquitous Computing in the Biology Laboratory." JALA: Journal of the Association for Laboratory Automation 6, no. 1 (February 2001): 66–67. http://dx.doi.org/10.1016/s1535-5535-04-00119-4.
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Our objective is to eliminate the digital divide that persists between the physical and information spaces of wet-lab based enterprises by embedding computational resources into the shared laboratory environment. Our first challenge is to enable individual lab workers to contribute to a fine-grained formal representation of ongoing lab activities — to build the database by doing the work, without having to stop and write things down in a notebook or to enter information into a computer. By eliminating the redundancy of doing the work and then recording it, accuracy and completeness will be improved. And, by capturing information at a finer detail than is practical for manual entry systems, unanticipated applications can be supported.
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Dao, An Thi Minh, Huong Thi Thu Nguyen, and Long Hoang Nguyen. "Variation Overtime among Patients of the Six Methadone Maintenance Treatment Clinics in Thai Nguyen from 2011 to 2015." BioMed Research International 2018 (August 7, 2018): 1–7. http://dx.doi.org/10.1155/2018/9081968.
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Background. Methadone Maintenance Treatment (MMT) program’s success depends on the likelihood of reducing drop-out rate and keeping patients remaining in the program. There have been neither comprehensive studies about variation among patients who have been experiencing MMT for long period nor prediction of MMT period in which the risk of drop-out would be the highest in Thai Nguyen, a northern mountainous province where the MMT was established in 2011.Objectives. To analyze variation of the MMT population through indicators of drop-out and death, re-enrolment, and retention rate in the six Thai Nguyen MMT clinics.Methods. A retrospective study by reviewing daily treatment notebooks of the six MMT clinics in Thai Nguyen to identify events of drop-out, death, reenrolment among 2,567 patients registered from 12 May 2011 to 6 September 2015.Results. Cumulative hazard of drop-out over period from the first to the fourth year of MMT treatment has an increasing trend at 0.15; 0.31; 0.46; and 0.61, respectively. The cumulative probability of re-enrolment among 740 patients who have already quit the MMT program and then returned slightly increased from 0.07 to 0.16 between the first years and the fourth year in which the highest returning rate occurred within the first 2 years after drop-out. The cumulative retention rate decreased annually and stayed at 71.7% after 4 years of running the MMT.Conclusions. MMT patients and their families should be informed and consulted about the highest risk period of drop-out and also about period when drop-out patients are most likely to reenter the MMT. Counseling adherence for patients should be conducted not only at the beginning but also during the ongoing MMT and play an extremely important role in reducing drop-out of the program while special counseling should also be reenforced for the re-enrolment patients of MMT.
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Rahn, Jennifer, Dana Willner, James Deverick, Peter Kemper, and Margaret Saha. "Incorporating Computer Programming & Data Science into a Guided Inquiry-Based Undergraduate Ecology Lab." American Biology Teacher 81, no. 9 (November 2019): 649–57. http://dx.doi.org/10.1525/abt.2019.81.9.649.
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The biological sciences are becoming increasingly reliant on computer science and associated technologies to quickly and efficiently analyze and interpret complex data sets. Introducing students to data analysis techniques is a critical part of their development as well-rounded, scientifically literate citizens. As part of a collaborative effort between the Biology and Computer Science departments at William & Mary, we sought to develop laboratory exercises that would introduce basic ideas of data analysis while also exposing students to Python, a commonly used computer programming language. We accomplished this by developing exercises within the interactive Jupyter Notebook platform, an open-source application that allows Python code to be written and executed as discrete blocks in real time. Students used the developed Jupyter Notebook to analyze data collected as part of a multiweek ecology field experiment aimed at determining the effect of white-tailed deer on aspects of biological diversity. These inquiry-based laboratory exercises generated scientifically relevant data and gave students a chance to experience and participate in ongoing scientific research while demonstrating the utility of computer science in the scientific process.
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Castells-Brooke, Nathalie, Roisin Mullins, John Antoniw, and Paul Verrier. "The Molecular Biology Notebook on CD-Rom – A step toward the virtual laboratory." Journal of Biological Education 33, no. 4 (September 1999): 223–25. http://dx.doi.org/10.1080/00219266.1999.9655672.
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Johnston, Jill, Sashi Kant, Vanessa Gysbers, Dale Hancock, and Gareth Denyer. "Using an ePortfolio system as an electronic laboratory notebook in undergraduate biochemistry and molecular biology practical classes." Biochemistry and Molecular Biology Education 42, no. 1 (December 20, 2013): 50–57. http://dx.doi.org/10.1002/bmb.20754.
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Badenhorst, Melinda, Christopher J. Barry, Christiaan J. Swanepoel, Charles Theo van Staden, Julian Wissing, and Johann M. Rohwer. "Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’." Processes 7, no. 7 (July 18, 2019): 460. http://dx.doi.org/10.3390/pr7070460.
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Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.
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Hachim, M. Y., and S. Hannawi. "CO0005 C-C CHEMOKINE RECEPTOR TYPE 5 AND ITS LIGANDS CCL4, 8 AND 11 CAN LINK COVID-19, RHEUMATOID ARTHRITIS AND HYDROXYCHLOROQUINE." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 213.3–214. http://dx.doi.org/10.1136/annrheumdis-2020-eular.6801.
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Background:Coronavirus disease (COVID-19) caused by SARS-COV2 represents an unprecedented global public health concern with a particular burden on patients with chronic diseases and those on immune-modulating drugs. It is especially worrisome to patients with rheumatoid arthritis (RA) who are on immune suppression regimens[1]. On the other side, many reports showed and recommended the use of some Disease-Modifying Drugs commonly used to treat rheumatic diseases like hydroxychloroquine. However, the general understanding of COVID-19 characteristics in this population and the mechanism of action of these drugs in COVID-19 is still unknown[2].Objectives:Explore publicly available transcriptomic dataset of patients infected with SARS-COV2 compared to uninfected to identify differentially expressed genes (DEGs) related to the immune system that might be pathogenic in RA synovium. Then explore the effect of Disease-Modifying Drugs on their local expression that might give hints about their possible mechanism of action.Methods:RNAseq dataset (GSE147507) were retrieved using the Gene Expression Omnibus (GEO) and used to identify DEGs between infected and uninfected lung samples using BioJupies tools [3]. The DEGs were explored for common pathways using Metascape online tool (http://metascape.org) [10], as shown in figure (1). The chemokines genes were filtered out, and their common receptor (CR) was identified. The immune cells that express a higher level of the identified receptor were explored using DICE project tool (https://dice-database.org/). The expression of CR was searched in a microarray dataset (GSE77298) of synovial biopsies of RA and healthy controls. RNAseq dataset (GSE97165) of synovial biopsies taken from 19 early RA patients at baseline and after six months of Triple Disease-Modifying Anti-rheumatic drugs (tDMARD; methotrexate, sulfasalazine, and hydroxychloroquine) treatment.Results:84 DEGs were identified between uninfected and COVID-19 infected lung samples. These DEGs were enriched in pathways specific to (response to the virus, response to interferon, leukocyte activation, and chemotaxis). Interestingly, SARS-COV-2 infected lungs express more CCL4, CCL8, and CCL11; the three ligands shared the same receptor, which is CCR5. Top immune cells that express CCR5 were CD4 T memory T reg cells, Th17, Th1, and monocytes. CCR5 was significantly upregulated in RA compared to healthy controls synovium (p=0.04) and was dramatically downregulated after six months of tDMARD treatment (p=0.004), as shown in figure (2).Conclusion:Using publicly available transcriptomic datasets properly highlighted the possible beneficiary effect of DMARDs in patients with COVID-19, which can block CCR5 rich immune cells recruitment.References:[1]Favalli, E.G., et al.,COVID-19 infection and rheumatoid arthritis: Faraway, so close!Autoimmun Rev, 2020. 19(5): p. 102523.[2]Gianfrancesco, M.A., et al.,Rheumatic disease and COVID-19: initial data from the COVID-19 Global Rheumatology Alliance provider registries.The Lancet Rheumatology, 2020. 2(5): p. e250-e253.[3]Torre, D., A. Lachmann, and A. Ma’ayan,BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud.Cell Systems, 2018. 7(5): p. 556-561.e3.Figure 1.Flowchart of transcriptomic analysisFigure 2.(A) Top immune cells that express CCR5 (B) CCR5 expression in synovial biopsies of RA and control (C) CCR5 expression at baseline and after 6 months of tDMARD treatment.Disclosure of Interests:None declared
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Nussbeck, Sara Y., Philipp Weil, Julia Menzel, Bartlomiej Marzec, Kai Lorberg, and Blanche Schwappach. "The laboratory notebook in the 21 st century." EMBO reports 15, no. 6 (May 15, 2014): 631–34. http://dx.doi.org/10.15252/embr.201338358.
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Geneve, Robert L. "Using Interactive Multimedia to Enhance Student Access to Information on Plant Anatomy and Cell Biology." HortScience 32, no. 3 (June 1997): 432B—432. http://dx.doi.org/10.21273/hortsci.32.3.432b.
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An interactive multimedia presentation was developed using authoring software (Authorware from Macromedia) to provide information on plant anatomy and cell biology. Our current course in growth and development of horticultural crops has limited time and lab facilities available for these subjects, yet a good foundation in this area is important to understanding growth and development. This software uses a variety of techniques, including color digital images, illustrations, cartoon animation, and video, to teach aspects of cell biology and different plant cell types. In addition, a review session allows students to interactively test their knowledge of the subject. The software was placed on a Dept. of Horticulture server that provided student access to a folder for course work. Students were able to access the software from anywhere on campus via the University network. Multiple students can use the software simultaneously. The approach of using a local server provided easy access and avoided some of the delays involved with viewing large (1 mb) images found when using the World Wide Web. It took students several weeks to complete the software's modules. Then, students completed an independent plant anatomy lab using the software for reference. Students were required to create a virtual notebook of labeled digital images captured from prepared microscope slides using a microscope attached with a digital camera and linked to a computer. Students found this approach to learning to be challenging, and initial feedback has been very positive.
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Voegele, Catherine, Baptiste Bouchereau, Nivonirina Robinot, James McKay, Philippe Damiecki, and Lucile Alteyrac. "A universal open-source Electronic Laboratory Notebook." Bioinformatics 29, no. 13 (May 3, 2013): 1710–12. http://dx.doi.org/10.1093/bioinformatics/btt253.
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Yacoub, Abdulraheem, and Paulette Mehta. "Learn While You Wait: An Experience of a Novel, Interactive VHA e-Notebook Education Program for Multiple Myeloma." Blood 116, no. 21 (November 19, 2010): 4743. http://dx.doi.org/10.1182/blood.v116.21.4743.4743.
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Abstract Abstract 4743 We evaluated a novel e-notebook -based education program for patients with multiple myeloma. The program was developed by the Myeloma Initiative in the Veteran's Administration (MIVA) consortium to educate patients while they wait for clinic appointments or procedures. The goals of this study were to assess feasibility of the program in a busy clinic setting, patients’ gain in knowledge from the program, patients’ acceptance of the e-notebook format, and potential use of program in other populations and in different formats. Study subjects were patients with multiple myeloma being treated at the Central Arkansas Veterans Healthcare System McClellan Hospital. Eligibility criteria were diagnosis of multiple myeloma, ability to follow instructions in the program and complete a paper questionnaire. Study subjects underwent the program while awaiting their oncology visit and then completed a survey consisting of ten multiple-choice questions designed to measure the goals of the study. The scope of the questions were: patients’ perceived benefit of the education program in improving their understanding of their disease (three questions), patients’ primary source of medical education, patients’ preferences in regard to educational formats (three questions), and multiple myeloma knowledge (three questions). Program use did not adversely affect patient flow in our busy oncology clinic, and was well accepted by the patients. Eight patients were enrolled in the study. All patients considered their oncologist to be their primary source of information about their disease. All patients stated that the education program helped them better understand the information provided by their oncologists and better understand multiple myeloma treatment options. Five patients (63%) answered that the program motivated them to learn more about myeloma. Seven patients (88%) recommended that this program be expanded to spouses or family members, and seven patients (88%) advocated creating a video format of the program for repeat viewing. Five patients (63%) stated they plan to view the content again at a later time. Of all multiple myeloma knowledge questions, 71% were answered correctly and 50% of the patients attributed their correct answers to information they learned in the education program. We conclude that this e-notebook -based multiple myeloma education program is well accepted, can be adopted in a busy clinic and has the potential for further applications. It is an effective and motivating tool for patient education in adjunct with physician efforts. It can be a productive utilization of waiting room time. Disclosures: No relevant conflicts of interest to declare.
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Ather, Syed Hussain, Olaitan Igbagbo Awe, Thomas J. Butler, Tamiru Denka, Stephen Andrew Semick, Wanhu Tang, and Ben Busby. "SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis." F1000Research 7 (May 22, 2018): 628. http://dx.doi.org/10.12688/f1000research.14880.1.
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Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy (source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.
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Ather, Syed Hussain, Olaitan Igbagbo Awe, Thomas J. Butler, Tamiru Denka, Stephen Andrew Semick, Wanhu Tang, and Ben Busby. "SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis." F1000Research 7 (November 30, 2018): 628. http://dx.doi.org/10.12688/f1000research.14880.2.
Full textAbstract:
Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy (source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.
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Stumpf, Michael P. H. "Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds." Journal of The Royal Society Interface 17, no. 171 (October 2020): 20200419. http://dx.doi.org/10.1098/rsif.2020.0419.
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Recent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare the performance of different candidate models at describing a particular biological system quantitatively. Model selection has been applied with great success to problems where a small number—typically less than 10—of models are compared, but recent studies have started to consider thousands and even millions of candidate models. Often, however, we are left with sets of models that are compatible with the data, and then we can use ensembles of models to make predictions. These ensembles can have very desirable characteristics, but as I show here are not guaranteed to improve on individual estimators or predictors. I will show in the cases of model selection and network inference when we can trust ensembles, and when we should be cautious. The analyses suggest that the careful construction of an ensemble—choosing good predictors—is of paramount importance, more than had perhaps been realized before: merely adding different methods does not suffice. The success of ensemble network inference methods is also shown to rest on their ability to suppress false-positive results. A Jupyter notebook which allows carrying out an assessment of ensemble estimators is provided.
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Yacoub, Abdulraheem, and Paulette Mehta. "Learn While You Wait: An Experience of a Novel, Interactive VHA e-Notebook Education Program for Multiple Myeloma, Final Results." Blood 118, no. 21 (November 18, 2011): 4759. http://dx.doi.org/10.1182/blood.v118.21.4759.4759.
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Abstract Abstract 4759 We evaluated a novel e-notebook -based education program for patients with multiple myeloma. The program was developed by the Myeloma Initiative in the Veteran's Administration (MIVA) consortium to educate patients about myeloma while they wait for clinic appointments or procedures. The goals of this study were to assess the feasibility of the program in a busy clinic setting, patients ‘knowledge after the program and patients’ acceptance of the e-notebook format. Study subjects were patients with multiple myeloma being treated at the Central Arkansas Veterans Healthcare System McClellan Hospital. Eligibility criteria were diagnosis of multiple myeloma and ability to follow instructions in the program and complete a paper questionnaire. Study subjects evaluated the module while awaiting their clinic visit and then completed a survey consisting of ten multiple-choice questions designed to measure the goals of the study. The scope of the questions were: patients' primary source of medical education, patients' perceived benefit of the education program in improving their understanding of their disease (three questions), patients' preferences in regard to educational formats (three questions), and multiple myeloma knowledge (three questions). Fifteen consecutive patients were enrolled in the study. Patient flow was not adversely affected by use of the module in the waiting room and the program was well accepted by the patients. Most patients in our study (93%) considered their primary source of medical information about their disease to be their oncologist, followed by internet resources (20%). Most patients (93%) stated that the education program helped them better understand the information provided by their oncologists and better understand multiple myeloma treatment options. Twelve patients (60%) answered that the program motivated them to learn more about myeloma. Fourteen patients (93%) recommended that this program be expanded to spouses or family members, and twelve patients (80%) advocated creating a video format of the program for repeat viewing. Nine patients (60%) stated they plan to view the content again at a later time. Of all multiple myeloma knowledge questions, 76% were answered correctly and 60% of the patients who answered correctly attributed their answers to information they learned in the education program. We conclude that this e-notebook -based multiple myeloma education program is well accepted and can be adopted in a busy clinic. It is an effective and motivating tool for patient education in adjunct with physician efforts. It can be a productive utilization of waiting room time and has the potential for further applications. Disclosures: No relevant conflicts of interest to declare.
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Mar'i, Husnul, Ristiono Ristiono, Yosi Laila Rahmi, and Yuni Ahda. "Effect of Biology Module with Scientific Approach Equipped with a Glossary in discovery learning models Against Learning Competencies of Class X Students of SMAN 1 Pariaman." Jurnal Atrium Pendidikan Biologi 5, no. 4 (September 29, 2020): 31. http://dx.doi.org/10.24036/apb.v5i4.6264.
Full textAbstract:
The low learning competencies of students is caused by several problems encountered during the teaching and learning process, including the problems that the first textbook used has not involved students actively playing a role in the learning process, learning is still monotonous and the student notebook is incomplete. One of the efforts that is done to increase students’ learning competencies is to apply a biological module with a scientific approach equipped with a glossary in discovery learning models. This research used a Randomized Control Group Posttest Only Design. The population is all student of class 1 SMAN 1 Pariaman. Sampel was taken by using purposive sampling technique. The control class uses textbooks with learning models Discovery learning and experimental classes use biological modules with discovery learning models. Instrument The research instrument used was in the form of a learning competency test, an assessment of attitudes and skills in the form of an observation sheet. The t-test results revealed that knowledge competencies t count > t table (7.06> 1.7), t test results 'competency skills t count < t table ( 0,925< 1.7), and t test results' competency attitude t count < t table (0,76< 1.7). This shows that the hypothesis is accepted in the aspect of knowledge that is not followed by aspects of skills and attitudes. So, it can be concluded that the biological module with a scientific approach equipped with a glossary in the discovery learning model that has significance for learning competencies in the aspects of student knowledgeThe low learning competencies of students is caused by several problems encountered during the teaching and learning process, including the problems that the first textbook used has not involved students actively playing a role in the learning process, learning is still monotonous and the student notebook is incomplete. One of the efforts that is done to increase students’ learning competencies is to apply a biological module with a scientific approach equipped with a glossary in discovery learning models. This research used a Randomized Control Group Posttest Only Design. The population is all student of class 1 SMAN 1 Pariaman. Sampel was taken by using purposive sampling technique. The control class uses textbooks with learning models Discovery learning and experimental classes use biological modules with discovery learning models. Instrument The research instrument used was in the form of a learning competency test, an assessment of attitudes and skills in the form of an observation sheet. The t-test results revealed that knowledge competencies t count > t table (7.06> 1.7), t test results 'competency skills t count < t table ( 0,925< 1.7), and t test results' competency attitude t count < t table (0,76< 1.7). This shows that the hypothesis is accepted in the aspect of knowledge that is not followed by aspects of skills and attitudes. So, it can be concluded that the biological module with a scientific approach equipped with a glossary in the discovery learning model that has significance for learning competencies in the aspects of student knowledge.
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Wallert, Mark A., and Joseph J. Provost. "Integrating standard operating procedures and industry notebook standards to evaluate students in laboratory courses." Biochemistry and Molecular Biology Education 42, no. 1 (December 23, 2013): 41–49. http://dx.doi.org/10.1002/bmb.20752.
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