scholarly journals Characterization of an open access medical news platform readership during the COVID-19 pandemic (Preprint)

10.2196/26666 ◽  
2020 ◽  
Author(s):  
Alex K. Chan ◽  
Constance Wu ◽  
Andrew Cheung ◽  
Marc D. Succi
Keyword(s):  
Open Access ◽  
Medical News

scholarly journals Perfil dos autores brasileiros com publicações científicas em periódicos de acesso aberto

2020 ◽  
pp. 150-154
Author(s):  
: Patrícia Mascarenhas Dias ◽  
Thiago Magela Rodrigues Dias ◽  
Gray Farias Moita
Keyword(s):  
Open Access ◽  
Geographic Distribution ◽  
Scientific Research ◽  
Scientific Communication ◽  
Scientific Development ◽  
Current State ◽  
Open Access Journals ◽  
Data Source

In the current state of scientific development, identifying how the results of scientific and technological investigations are being published allows us to understand how scientific communication has been used to disseminate the studies carried out and the results achieved. In this scenario, the publication of articles in open access journals appears as an important and interesting mechanism for the dissemination of scientific research, since it facilitates and enables access to them, considering that there are no barriers, especially financial ones, to access the contents of this type of publication. Thus, this work aims to present a characterization of the group of Brazilian researchers who have published articles in open access journals. To this end, the Lattes Platform curricula is used as a data source to initially identify researchers with publications of articles in open access journals and, subsequently, draw a portrait of the profile of these researchers, such as areas of expertise, levels of training and geographic distribution.

scholarly journals NeuroPM toolbox: integrating Molecular, Neuroimaging and Clinical data for Characterizing Neuropathological Progression and Individual Therapeutic Needs

2020 ◽  
Author(s):  
Yasser Iturria-Medina ◽  
Felix Carbonell ◽  
Atoussa Assadi ◽  
Quadri Adewale ◽  
Ahmed F. Khan ◽  
...  
Keyword(s):  
Open Access ◽  
High Performance ◽  
Large Scale ◽  
Synergistic Interactions ◽  
Academic Researchers ◽  
Cross Platform ◽  
Therapeutic Needs ◽  
User Friendly ◽  
Performance Computing

There is a critical need for a better multiscale and multifactorial understanding of neurological disorders, covering from genes to neuroimaging to clinical factors and treatments effects. Here we present NeuroPM-box, a cross-platform, user-friendly and open-access software for characterizing multiscale and multifactorial brain pathological mechanisms and identifying individual therapeutic needs. The implemented methods have been extensively tested and validated in the neurodegenerative context, but there is not restriction in the kind of disorders that can be analyzed. By using advanced analytic modeling of molecular, neuroimaging and/or cognitive/behavioral data, this framework allows multiple applications, including characterization of: (i) the series of sequential states (e.g. transcriptomic, imaging or clinical alterations) covering decades of disease progression, (ii) intra-brain spreading of pathological factors (e.g. amyloid and tau misfolded proteins), (iii) synergistic interactions between multiple brain biological factors (e.g. direct tau effects on vascular and structural properties), and (iv) biologically-defined patients stratification based on therapeutic needs (i.e. optimum treatments for each patient). All models outputs are biologically interpretable. A 4D-viewer allows visualization of spatiotemporal brain (dis)organization. Originally implemented in MATLAB, NeuroPM-box is compiled as standalone application for Windows, Linux and Mac environments: neuropm-lab.com/software. In a regular workstation, it can analyze over 150 subjects per day, reducing the need for using clusters or High-Performance Computing (HPC) for large-scale datasets. This open-access tool for academic researchers may significantly contribute to a better understanding of complex brain processes and to accelerating the implementation of Precision Medicine (PM) in neurology.

scholarly journals Planomonospora: a Metabolomics Perspective on an Underexplored Actinobacteria Genus

2020 ◽  
Author(s):  
Mitja M. Zdouc ◽  
Marianna Iorio ◽  
Sonia I. Maffioli ◽  
Max Crüsemann ◽  
Stefano Donadio ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Open Access ◽  
Secondary Metabolites ◽  
Gene Cluster ◽  
Chemical Diversity ◽  
Peptide Analysis ◽  
Track Record ◽  
Metabolomic Study

ABSTRACTDespite an excellent track record, microbial drug discovery suffers from high rates of re-discovery. Better workflows for the rapid investigation of complex extracts are needed to increase throughput and allow early prioritization of samples. In addition, systematic characterization of poorly explored strains is seldomly performed. Here, we report a metabolomic study of 72 isolates belonging to the rare actinomycete genus Planomonospora, using a workflow of open access tools to investigate its secondary metabolites. The results reveal a correlation of chemical diversity and strain phylogeny, with classes of metabolites exclusive to certain phylogroups. We were able to identify previously reported Planomonospora metabolites, including the ureylene-containing oligopeptide antipain, the thiopeptide siomycin including new congeners and the ribosomally synthesized peptides sphaericin and lantibiotic 97518. In addition, we found that Planomonospora strains can produce the siderophore desferrioxamine or a salinichelin-like peptide. Analysis of the genomes of three newly sequenced strains led to the detection of 47 gene cluster families, of which several were connected to products found by LC-MS/MS profiling. This study demonstrates the value of metabolomic studies to investigate poorly explored taxa and provides a first picture of the biosynthetic capabilities of the genus Planomonospora.

scholarly journals ProPheno 1.0: An online dataset for accelerating the complete characterization of the human protein-phenotype landscape in biomedical literature

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Indika Kahanda
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Predictive Models ◽  
Complete Characterization ◽  
Biomedical Literature ◽  
Human Protein ◽  
Pubmed Central ◽  
Mining Tool ◽  
Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed Central Open Access. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

scholarly journals Characterization of an open access medical news platform readership during the COVID-19 pandemic (Preprint)

2020 ◽  
Author(s):  
Alex K. Chan ◽  
Constance Wu ◽  
Andrew Cheung ◽  
Marc D. Succi
Keyword(s):  
Open Access ◽  
Survey Data ◽  
Medical Literature ◽  
Journal Article ◽  
Likert Scale ◽  
News Sources ◽  
Medical Study ◽  
Medical News ◽  
Primary Means ◽  
Strongly Agree

BACKGROUND There now exists many alternatives to direct journal access, such as podcasts, blogs, and news sites for physicians and the general public to stay up-to-date with medical literature. Currently however, there is a scarcity of literature that investigates these readership characteristics of open access medical news sites and how they may have shifted with coronavirus disease 19 (COVID-19). OBJECTIVE The current study aimed to employ readership and survey data to characterize open access medical news readership trends in relation to COVID-19 in addition to overall readership trends regarding pandemic related information delivery. METHODS Anonymous aggregate readership data was obtained from 2 Minute Medicine® (www.2minutemedicine.com), an open-access, physician-run medical news organization that has published over 8000 original physician-written text and visual summaries of new medical research since 2013. In this retrospective observational study, the average article views, actions (defined as the sum of views, shares, and outbound link clicks), read times, and bounce rate (probability to leave a page in <30s) were compared between COVID-19 articles published between January 1 to May 31, 2020 (N = 40) to non-COVID-19 articles (N = 145) published in the same time period. A voluntary survey was also sent to subscribed 2 Minute Medicine readers to further characterize readership demographics and preferences scored by Likert Scale. RESULTS COVID-19 articles had significantly more median views than non-COVID-19 articles (296 vs. 110, U = 748.5, P < 0.001). There were no differences in average read times or bounce rate. Non-COVID-19 had more median actions than COVID-19 articles (2.9 vs. 2.5, U = 2070.5, P < 0.05). On a Likert scale of 1 (Strongly Disagree) to 5 (Strongly Agree), survey data revealed that 66% (78/119) of readers Agreed or Strongly Agreed that they preferred staying up to date with emerging literature surrounding COVID-19 using sources such as 2 Minute Medicine versus direct journal access. A greater proportion of survey takers also indicated open access news sources to be one of their primary means of staying informed (71.7%) than direct journal article access (50.8%). A lesser proportion of readers indicated reading one or less full length medical study following introduction to 2 Minute Medicine compared to prior (16.9% vs. 31.8%, P < 0.05). CONCLUSIONS There is a significantly increased readership in one open-access medical literature platform during the pandemic, reinforcing that open-access physician-written sources of medical news represent an important alternative to direct journal access for readers to stay up to date with medical literature.

scholarly journals EpiGraph: an open-source platform to quantify epithelial organization

2019 ◽  
Author(s):  
Pablo Vicente-Munuera ◽  
Pedro Gómez-Gálvez ◽  
Robert J Tetley ◽  
Cristina Forja ◽  
Antonio Tagua ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Access ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Distribution Analysis ◽  
Supplementary Data ◽  
Programming Skills ◽  
User Friendly ◽  
Degree Of Order

Abstract Summary Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required. Availability and implementation EpiGraph is available at https://imagej.net/EpiGraph and the code is accessible (https://github.com/ComplexOrganizationOfLivingMatter/Epigraph) under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparison among Methods and Statistical Software Packages to Analyze Germplasm Genetic Diversity by Means of Codominant Markers

J ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 197-215 ◽  
Author(s):  
Mario Pagnotta
Keyword(s):  
Genetic Diversity ◽  
Molecular Markers ◽  
Open Access ◽  
Basic Principles ◽  
Statistical Software ◽  
Germplasm Collections ◽  
Software Packages ◽  
Dominant Markers ◽  
Selection Of

Co-dominant markers’ data are often analysed as if they were dominant markers, an over-simplification that may be misleading. Addressing this, the present paper aims to provide a practical guide to the analysis of co-dominant data and selection of suitable software. An overview is provided of the computational methods and basic principles necessary for statistical analyses of co-dominant molecular markers to determine genetic diversity and molecular characterization of germplasm collections. The Hardy–Weinberg principle is at the base of statistical methods to determine genetic distance, genetic diversity, and its distribution among and within populations. Six statistical software packages named GenAlEx, GDA, Power Marker, Cervus, Arlequin, and Structure are compared and contrasted. The different software packages were selected based on: (i) The ability to analyze co-dominant data, (ii) open access software, (iii) ease of downloading, and (iv) ease of running using a Microsoft Window interface. The software packages are compared analyzing the same dataset. Differences among parameters are discussed together with the comments on some of the software outputs.

Is Open Access to Research Data a Strategic Priority of Czech Universities?

2018 ◽  
Vol 7 (2) ◽  
pp. 39-45
Author(s):  
Jakub Novotný
Keyword(s):  
Open Access ◽  
Open Science ◽  
Research Data ◽  
Science Concept ◽  
Modus Operandi ◽  
Strategic Plans ◽  
The Empirical Analysis ◽  
Current Science ◽  
Science And Research

Abstract Open access to research data is one of the key themes of current science development concepts and relevant R & D strategies at least in Europe. A systemic change in the modus operandi of science and research should lead to so-called Open Science. The presented paper questions the extent to which the Open Science concept is reflected in the strategies of Czech universities. The paper first describes basic idea of Open Access to Research Data including principles of „FAIR data” as one of the key assumption of it. After a brief characterization of the Czech university sector, the results of the empirical analysis of the inclusion of the Open Access to Research Data concept in the current strategic plans of the Czech universities are presented. The conclusion of the paper is then an evaluation of the results, which reveal an underestimation of the Open Science concept in the current strategic plans of the Czech universities.

scholarly journals EpiGraph: an open-source platform to quantify epithelial organization

2017 ◽  
Author(s):  
Pablo Vicente-Munuera ◽  
Pedro Gómez-Gálvez ◽  
Robert J. Tetley ◽  
Cristina Forja ◽  
Antonio Tagua ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Access ◽  
Analysis Tool ◽  
Distribution Analysis ◽  
Form Complex ◽  
Programming Skills ◽  
User Friendly ◽  
Degree Of Order ◽  
Different Tissues

SUMMARYDuring development, cells must coordinate their differentiation with their growth and organization to form complex multicellular structures such as tissues and organs. Healthy tissues must maintain these structures during homeostasis. Epithelia are packed ensembles of cells from which the different tissues of the organism will originate during embryogenesis. A large barrier to the analysis of the morphogenetic changes in epithelia is the lack of simple tools that enable the quantification of cell arrangements. Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform FIJI. This makes EpiGraph very user friendly, with no programming skills required.

scholarly journals ProPheno 1.0: An online dataset for accelerating the complete characterization of the human protein-phenotype landscape in biomedical literature

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Indika Kahanda
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Predictive Models ◽  
Complete Characterization ◽  
Biomedical Literature ◽  
Human Protein ◽  
Pubmed Central ◽  
Mining Tool ◽  
Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed Central Open Access. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

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