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Khan I, Shahaab A. A Peer-To-Peer Publication Model on Blockchain. FRONTIERS IN BLOCKCHAIN 2021. [DOI: 10.3389/fbloc.2021.615726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In the past few decades, there has been a sharp rise of research irreproducibility and retraction, to a point that now is deemed as a crisis. Addressing this crisis, we present a peer-to-peer (P2P) publication model that utilizes blockchain and smart contract technologies. Focusing primarily on researchers and reviewers, the conceptual P2P publication model addresses the sociocultural and incentivization aspects of the irreproducibility crisis. In the P2P publication model, instead of a complete publication, a preapproved experimental design will be published on an incremental basis (unit-by-unit) and authorship will be shared with reviewers. The concept of the P2P publication model was inspired by the transformational journey the music publishing industry has undertaken as it traverses through vinyl age (complete albums) to the Spotify age (single-by-single), where there is a growing inclination among artists toward building an incremental album, taking account of feedback from fans and utilizing automated revenue collection and sharing systems. The ability to publish incrementally through the P2P publication model will relieve researchers from the burden of publishing complete and “good results” while simultaneously incentivizing reviewers to undertake rigorous review work to gain authorship credit in the research. The proposed P2P publication model aims to transform the century-old publication model and incentivization structure in alignment with open access publication ethos of the 21st century.
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The "MYOCYTER" - Convert cellular and cardiac contractions into numbers with ImageJ. Sci Rep 2019; 9:15112. [PMID: 31641278 PMCID: PMC6805901 DOI: 10.1038/s41598-019-51676-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 10/02/2019] [Indexed: 12/28/2022] Open
Abstract
Measurement and quantification of cardiomyocyte or cardiac contractions as important (patho) physiologic parameters require highly specialized and expensive setups of fully integrated hard- and software that may be very difficult to use and may also depend on highly sophisticated methods of further data evaluation. With MYOCYTER (MC) we present a complete and highly customizable open-source macro for ImageJ, enabling fast, reliable user-friendly large scale analysis extracting an extensive amount of parameters from (even multiple) video recorded contracting cells or whole hearts, gained from a very competitive experimental setup. The extracted parameters enable extensive further (statistical) analysis to identify and quantify the effects of pathologic changes or drugs. Using videos following known mathematical functions, we were able to demonstrate the accuracy of MYOCYTER’s data extraction, also successfully applied the software to both cellular and animal models, introducing innovations like dynamic thresholding, automatic multi-cell recognition, “masked” evaluation and change of applied parameters even after evaluation.
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Plant AL, Becker CA, Hanisch RJ, Boisvert RF, Possolo AM, Elliott JT. How measurement science can improve confidence in research results. PLoS Biol 2018; 16:e2004299. [PMID: 29684013 PMCID: PMC5933802 DOI: 10.1371/journal.pbio.2004299] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 05/03/2018] [Indexed: 11/18/2022] Open
Abstract
The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.
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Affiliation(s)
- Anne L. Plant
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
- * E-mail:
| | - Chandler A. Becker
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - Robert J. Hanisch
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - Ronald F. Boisvert
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - Antonio M. Possolo
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - John T. Elliott
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
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Grune T, Kehm R, Höhn A, Jung T. "Cyt/Nuc," a Customizable and Documenting ImageJ Macro for Evaluation of Protein Distributions Between Cytosol and Nucleus. Biotechnol J 2018; 13:e1700652. [PMID: 29319229 DOI: 10.1002/biot.201700652] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/28/2017] [Indexed: 12/12/2022]
Abstract
Large amounts of data from multi-channel, high resolution, fluorescence microscopic images require tools that provide easy, customizable, and reproducible high-throughput analysis. The freeware "ImageJ" has become one of the standard tools for scientific image analysis. Since ImageJ offers recording of "macros," even a complex multi-step process can be easily applied fully automated to large numbers of images, saving both time and reducing human subjective evaluation. In this work, we present "Cyt/Nuc," an ImageJ macro, able to recognize and to compare the nuclear and cytosolic areas of tissue samples, in order to investigate distributions of immunostained proteins between both compartments, while it documents in detail the whole process of evaluation and pattern recognition. As practical example, the redistribution of the 20S proteasome, the main intracellular protease in mammalian cells, is investigated in NZO-mouse liver after feeding the animals different diets. A significant shift in proteasomal distribution between cytosol and nucleus in response to metabolic stress was revealed using "Cyt/Nuc" via automatized quantification of thousands of nuclei within minutes. "Cyt/Nuc" is easy to use and highly customizable, matches the precision of careful manual evaluation and bears the potential for quick detection of any shift in intracellular protein distribution.
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Affiliation(s)
- Tilman Grune
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.,German Center for Diabetes Research (DZD), 85764 Muenchen-Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), 10117 Berlin, Germany.,NutriAct - Competence Cluster Nutrition Research Berlin-Potsdam, 14558 Nuthetal, Germany
| | - Richard Kehm
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.,German Center for Diabetes Research (DZD), 85764 Muenchen-Neuherberg, Germany
| | - Annika Höhn
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.,German Center for Diabetes Research (DZD), 85764 Muenchen-Neuherberg, Germany
| | - Tobias Jung
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.,German Center for Cardiovascular Research (DZHK), 10117 Berlin, Germany
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Schmutzer T, Bolger ME, Rudd S, Chen J, Gundlach H, Arend D, Oppermann M, Weise S, Lange M, Spannagl M, Usadel B, Mayer KFX, Scholz U. Bioinformatics in the plant genomic and phenomic domain: The German contribution to resources, services and perspectives. J Biotechnol 2017; 261:37-45. [PMID: 28698099 DOI: 10.1016/j.jbiotec.2017.07.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 06/30/2017] [Accepted: 07/04/2017] [Indexed: 10/19/2022]
Abstract
Plant genetic resources are a substantial opportunity for plant breeding, preservation and maintenance of biological diversity. As part of the German Network for Bioinformatics Infrastructure (de.NBI) the German Crop BioGreenformatics Network (GCBN) focuses mainly on crop plants and provides both data and software infrastructure which are tailored to the needs of the plant research community. Our mission and key objectives include: (1) provision of transparent access to germplasm seeds, (2) the delivery of improved workflows for plant gene annotation, and (3) implementation of bioinformatics services that link genotypes and phenotypes. This review introduces the GCBN's spectrum of web-services and integrated data resources that address common research problems in the plant genomics community.
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Affiliation(s)
- Thomas Schmutzer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Marie E Bolger
- Forschungszentrum Jülich (FZJ), Institute of Bio- and Geosciences (IBG-2) Plant Sciences, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
| | - Stephen Rudd
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Jinbo Chen
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Heidrun Gundlach
- Helmholtz Zentrum München (HMGU), Plant Genome and Systems Biology (PGSB), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Markus Oppermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Stephan Weise
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Manuel Spannagl
- Helmholtz Zentrum München (HMGU), Plant Genome and Systems Biology (PGSB), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Björn Usadel
- Forschungszentrum Jülich (FZJ), Institute of Bio- and Geosciences (IBG-2) Plant Sciences, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
| | - Klaus F X Mayer
- Helmholtz Zentrum München (HMGU), Plant Genome and Systems Biology (PGSB), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354 Freising, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Seeland, Germany.
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Bajcsy P, Cardone A, Chalfoun J, Halter M, Juba D, Kociolek M, Majurski M, Peskin A, Simon C, Simon M, Vandecreme A, Brady M. Survey statistics of automated segmentations applied to optical imaging of mammalian cells. BMC Bioinformatics 2015; 16:330. [PMID: 26472075 PMCID: PMC4608288 DOI: 10.1186/s12859-015-0762-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 10/07/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. METHODS We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. RESULTS The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. CONCLUSIONS The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.
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Affiliation(s)
- Peter Bajcsy
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Antonio Cardone
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Joe Chalfoun
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Michael Halter
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Derek Juba
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | | | - Michael Majurski
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Adele Peskin
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Carl Simon
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Mylene Simon
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Antoine Vandecreme
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Mary Brady
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
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