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2022 ◽  
Author(s):  
Eline Van Geert ◽  
Christophe Bossens ◽  
Johan Wagemans

Do individuals prefer stimuli that are ordered or chaotic, simple or complex, or that strike the right balance of order and complexity? Earlier research mainly focused on the separate influence of order and complexity on aesthetic appreciation. When order and complexity were studied in combination, stimulus manipulations were often not parametrically controlled, only rather specific types of order (i.e., balance or symmetry) were studied, and/or the multidimensionality of order and complexity was ignored. Progress has also been limited by the lack of an easy way to create reproducible and expandible stimulus sets, including both order and complexity manipulations. The Order & Complexity Toolbox for Aesthetics (OCTA), a Python toolbox that is also available as a point-and-click Shiny application, aims to fill this gap. OCTA provides researchers with a free and easy way to create multi-element displays varying qualitatively (i.e., different types) and quantitatively (i.e., different levels) in order and complexity, based on regularity and variety along multiple element features (e.g., shape, size, color, orientation). The standard vector-based output is ideal for experiments on the web and the creation of dynamic interfaces and stimuli. OCTA will not only facilitate reproducible stimulus construction and experimental design in research on order, complexity, and aesthetics. In addition, OCTA can be a very useful tool in any type of research using visual stimuli, or even to create digital art. To illustrate OCTA’s potential, we will propose several possible applications and diverse questions that can be addressed using OCTA.


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0262145
Author(s):  
Olatunji Johnson ◽  
Claudio Fronterre ◽  
Peter J. Diggle ◽  
Benjamin Amoah ◽  
Emanuele Giorgi

User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.


2021 ◽  
Author(s):  
Hamzah Syed ◽  
Georg W Otto ◽  
Daniel Kelberman ◽  
Chiara Bacchelli ◽  
Philip L Beales

Background: Multi-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotypes, integrating information from the genome, transcriptome, epigenome, metabolome, proteome and microbiome. This integration of data is of particular use in rare disease studies where the sample sizes are often relatively small. Methods development for multi-omics studies is in its early stages due to the complexity of the different individual data types. There is a need for software to perform data simulation and power calculation for multi-omics studies to test these different methodologies and help calculate sample size before the initiation of a study. This software, in turn, will optimise the success of a study. Results: The interactive R shiny application MOPower described below simulates data based on three different omics using statistical distributions. It calculates the power to detect an association with the phenotype through analysis of n number of replicates using a variety of the latest multi-omics analysis models and packages. The simulation study confirms the efficiency of the software when handling thousands of simulations over ten different sample sizes. The average time elapsed for a power calculation run between integration models was approximately 500 seconds. Additionally, for the given study design model, power varied with the increase in the number of features affecting each method differently. For example, using MOFA had an increase in power to detect an association when the study sample size equally matched the number of features. Conclusions: MOPower addresses the need for flexible and user-friendly software that undertakes power calculations for multi-omics studies. MOPower offers users a wide variety of integration methods to test and full customisation of omics features to cover a range of study designs.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dionysios Fanidis ◽  
Panagiotis Moulos ◽  
Vassilis Aidinis

AbstractIdiopathic pulmonary fibrosis is a lethal lung fibroproliferative disease with limited therapeutic options. Differential expression profiling of affected sites has been instrumental for involved pathogenetic mechanisms dissection and therapeutic targets discovery. However, there have been limited efforts to comparatively analyse/mine the numerous related publicly available datasets, to fully exploit their potential on the validation/creation of novel research hypotheses. In this context and towards that goal, we present Fibromine, an integrated database and exploration environment comprising of consistently re-analysed, manually curated transcriptomic and proteomic pulmonary fibrosis datasets covering a wide range of experimental designs in both patients and animal models. Fibromine can be accessed via an R Shiny application (http://www.fibromine.com/Fibromine) which offers dynamic data exploration and real-time integration functionalities. Moreover, we introduce a novel benchmarking system based on transcriptomic datasets underlying characteristics, resulting to dataset accreditation aiming to aid the user on dataset selection. Cell specificity of gene expression can be visualised and/or explored in several scRNA-seq datasets, in an effort to link legacy data with this cutting-edge methodology and paving the way to their integration. Several use case examples are presented, that, importantly, can be reproduced on-the-fly by a non-specialist user, the primary target and potential user of this endeavour.


2021 ◽  
Author(s):  
Nelly Lopes de Moraes Gil ◽  
Aline Chotte de Oliveira ◽  
Gabriela Ganassin ◽  
Carolina Luca ◽  
Sandra Pelloso ◽  
...  

Background: Health decision-makers currently face the challenge of accumulating health data in time to inform evidence-based interventions to improve health outcomes. The Brazilian healthcare system is in need of daily primary care data reported in real-time to support evidence-based policy decisions. This study aims to detail the development of a solution for geospatial monitoring in public health called AUTOMAP. Its main objective is to facilitate epidemiological surveillance and promote that rapidly available data improve the provision of health services. Methods: AUTOMAP is an application that articulates concepts inherent to epidemiological surveillance, geographic information systems, and free access technologies to design a monitoring tool of health conditions. The system architecture consists of three modules: user, application, and database. They work together to collect information regarding health conditions, its processing, and dynamic viewing. AUTOMAP design uses the statistical language R, which employs literate programming through a Shiny application package to transform statistical results of health conditions into interactive maps in real-time. AUTOMAP is a web application that has two interfaces: one for loading data and another for generating dynamic epidemiological maps. Conclusion: AUTOMAP allows a variety of clinical solutions, such as risk calculators, spatial evaluation of events of interest, decision models, simulations, and epidemiological patient monitoring. The software is open-source with easy accessibility, allowing anyone to make adjustments and handle a myriad of health conditions, thus being applicable globally. AUTOMAP is a tool that will facilitate and advance data collection for evidence generation and expedite evidence-based health system improvements.


2021 ◽  
pp. 113176
Author(s):  
Bernd Jagla ◽  
Valentina Libri ◽  
Claudia Chica ◽  
Vincent Rouilly ◽  
Sebastien Mella ◽  
...  

2021 ◽  
pp. 0013189X2110513
Author(s):  
Joseph A. Taylor ◽  
Terri Pigott ◽  
Ryan Williams

Toward the goal of more rapid knowledge accumulation via better meta-analyses, this article explores statistical approaches intended to increase the precision and comparability of effect sizes from education research. The featured estimate of the proposed approach is a standardized mean difference effect size whose numerator is a mean difference that has been adjusted for baseline differences in the outcome measure, at a minimum, and whose denominator is the total variance. The article describes the utility and efficiency of covariate adjustment through baseline measures and the need to standardize effects on a total variance that accounts for variation at multiple levels. As computation of the total variance can be complex in multilevel studies, a shiny application is provided to assist with computation of the total variance and subsequent effect size. Examples are provided for how to interpret and input the required calculator inputs.


JAMIA Open ◽  
2021 ◽  
Vol 4 (4) ◽  
Author(s):  
S Scott Graham ◽  
Zoltan P Majdik ◽  
Joshua B Barbour ◽  
Justin F Rousseau

Abstract Objective To create a data visualization dashboard to advance research related to clinical trials sponsorship and monopolistic practices in the pharmaceuticals industry. Materials and Methods This R Shiny application aggregates data from ClinicialTrials.gov resulting from user’s queries by terms. Returned data are visualized through an interactive dashboard. Results The Clinical Trials Sponsorship Network Dashboard (CTSND) uses force-directed network mapping algorithms to visualize clinical trials sponsorship data. Interpretation of network visualization is further supported with data on sponsor classes, sponsorship timelines, evaluated products, and target conditions. The source code for the CTSND is available at https://github.com/sscottgraham/ConflictMetrics. Discussion Monopolistic practices have been identified as a likely contributor to high drug prices in the United States. CTSND data and visualizations support the analysis of clinical trials sponsorship networks and may aid in identifying current and emerging monopolistic practices. Conclusions CTSND data can support more robust deliberation about an understudied area of drug pricing.


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