scholarly journals ECX: An R Package for Studying Sensitivity of Antimicrobial Substances Using Spiral Plating Technology

2016 ◽  
Vol 17 (3) ◽  
pp. 188-194 ◽  
Author(s):  
Gabriel Andrés Torres-Londoño ◽  
Mary Hausbeck ◽  
Jianjun Hao
Keyword(s):  
R Package ◽  
Large Datasets ◽  
Graphical Interface ◽  
Commercial Software ◽  
Web Based ◽  
Plating Technique ◽  
Antimicrobial Substances ◽  
Sensitivity Studies ◽  
R Packages ◽  
User Friendly

Spiral plating technique is reliable, repeatable, and more efficient than dilution plating methods in studying the efficacy of antimicrobial products. In this method, the concentration of chemicals can be varied at different positions on agar plates, but its calculation requires using a commercial software. To establish a user-friendly and cost-free platform, the R package ECX was developed to calculate chemical concentrations in spiral plating technique. Mathematical models were established for calculating dispensed volume on agar plates using variables (molecular weight and agar height) that affect diffusion. In addition to the R packages, the web-based Shiny extensions ECX, multi, and ppm were developed to provide a graphical interface for calculating individual concentrations, multiple concentrations, and stock concentrations, respectively. No significant differences were observed (P > 0.05) when ECX was compared with the commercial software. The ability to import and process large datasets makes the ECX package a better option for spiral plating technique studies. Furthermore, the multiplatform nature of the ECX package overcomes limitations presented in other software. Therefore, these ECX characteristics can increase the use of the spiral plating technique for sensitivity studies. Accepted for publication 21 June 2016.

scholarly journals Introducing bdclean: a user friendly biodiversity data cleaning pipeline

2018 ◽  
Vol 2 ◽  
pp. e25564
Author(s):  
Tomer Gueta ◽  
Vijay Barve ◽  
Thiloshon Nagarajah ◽  
Ashwin Agrawal ◽  
Yohay Carmel
Keyword(s):  
Data Cleaning ◽  
Control Process ◽  
R Package ◽  
Modular Approach ◽  
Data Validation ◽  
Biodiversity Data ◽  
Quality Control Process ◽  
Cleaning Procedures ◽  
R Packages ◽  
User Friendly

A new R package for biodiversity data cleaning, 'bdclean', was initiated in the Google Summer of Code (GSoC) 2017 and is available on github. Several R packages have great data validation and cleaning functions, but 'bdclean' provides features to manage a complete pipeline for biodiversity data cleaning; from data quality explorations, to cleaning procedures and reporting. Users are able go through the quality control process in a very structured, intuitive, and effective way. A modular approach to data cleaning functionality should make this package extensible for many biodiversity data cleaning needs. Under GSoC 2018, 'bdclean' will go through a comprehensive upgrade. New features will be highlighted in the demonstration.

scholarly journals LIONESS Lab: a free web-based platform for conducting interactive experiments online

2020 ◽  
Vol 6 (1) ◽  
pp. 95-111 ◽  
Author(s):  
Marcus Giamattei ◽  
Kyanoush Seyed Yahosseini ◽  
Simon Gächter ◽  
Lucas Molleman
Keyword(s):  
Waiting Time ◽  
Graphical Interface ◽  
Online Experiments ◽  
Web Based ◽  
Methodological Challenges ◽  
Online Experimentation ◽  
Key Features ◽  
User Friendly ◽  
Time Form

Abstract LIONESS Lab is a free web-based platform for interactive online experiments. An intuitive, user-friendly graphical interface enables researchers to develop, test, and share experiments online, with minimal need for programming experience. LIONESS Lab provides solutions for the methodological challenges of interactive online experimentation, including ways to reduce waiting time, form groups on-the-fly, and deal with participant dropout. We highlight key features of the software, and show how it meets the challenges of conducting interactive experiments online.

scholarly journals Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Martina McMenamin ◽  
Michael J. Grayling ◽  
Anna Berglind ◽  
James M. S. Wason
Keyword(s):  
Sample Size ◽  
Lupus Erythematosus ◽  
Risk Difference ◽  
The Novel ◽  
Size Estimation ◽  
Web Based ◽  
Point Estimates ◽  
Novel Approach ◽  
R Packages ◽  
User Friendly

Abstract Background Composite responder endpoints feature frequently in rheumatology due to the multifaceted nature of many of these conditions. Current analysis methods used to analyse these endpoints discard much of the data used to classify patients as responders and are therefore highly inefficient, resulting in low power. We highlight a novel augmented methodology that uses more of the information available to improve the precision of reported treatment effects. Since these methods are more challenging to implement, we developed free, user-friendly software available in a web-based interface and as R packages. The software consists of two programs: one that supports the analysis of responder endpoints; the second that facilitates sample size estimation. We demonstrate the use of the software to conduct the analysis with both the augmented and standard analysis method using the MUSE study, a phase IIb trial in patients with systemic lupus erythematosus. Results The software outputs similar point estimates with smaller confidence intervals for the odds ratio, risk ratio and risk difference estimators using the augmented approach. The sample size required in each arm for a future trial using the novel approach based on the MUSE data is 50 versus 135 for the standard method, translating to a reduction in required sample size of approximately 63%. Conclusions We encourage trialists to use the software demonstrated to implement the augmented methodology in future studies to improve efficiency.

Web Based User-Friendly Graphical Interface to Control Robots with ROS Environment

2021 ◽  
Author(s):  
Dinodi Divyanjana Rajapaksha ◽  
Mohamed Nafeel Mohamed Nuhuman ◽  
Sewmini Dananji Gunawardhana ◽  
Atchuthan Sivalingam ◽  
Mohamed Nimran Mohamed Hassan ◽  
...  
Keyword(s):  
Graphical Interface ◽  
Web Based ◽  
User Friendly

scholarly journals Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy

2019 ◽  
Vol 5 ◽  
Author(s):  
Giulio Genova ◽  
Mattia Rossi ◽  
Georg Niedrist ◽  
Stefano Della Chiesa
Keyword(s):  
Time Series ◽  
Open Data ◽  
R Package ◽  
Private Companies ◽  
Web Based ◽  
South Tyrol ◽  
Wide Range ◽  
Shiny App ◽  
On Line ◽  
User Friendly

Meteo Browser South Tyrol is a user-friendly web-based application that helps to visualize and download the hydro-meteorological time series freely available in South Tyrol, Italy. It is designed for a wide range of users, from common citizens to students as well as researchers, private companies and the public administration. Meteo Browser South Tyrol is a Shiny App inside an R package and can be used on a local machine or accessed on-line. Drop down menus allow the user to select hydro-meteorological station and measurements. A simple map shows where the monitoring stations are, the latest measurements available, and lets the user subset the selected stations geographically by drawing a polygon.

scholarly journals webMCP-counter: a web interface for transcriptomics-based quantification of immune and stromal cells in heterogeneous human or murine samples

2020 ◽  
Author(s):  
Maxime Meylan ◽  
Etienne Becht ◽  
Catherine Sautès-Fridman ◽  
Aurélien de Reyniès ◽  
Wolf H. Fridman ◽  
...  
Keyword(s):  
Stromal Cells ◽  
R Package ◽  
Web Interface ◽  
Transcriptomic Data ◽  
R Programming Language ◽  
Link Type ◽  
Precise Estimation ◽  
R Packages ◽  
R Programming ◽  
User Friendly

AbstractSummaryWe previously reported MCP-counter and mMCP-counter, methods that allow precise estimation of the immune and stromal composition of human and murine samples from bulk transcriptomic data, but they were only distributed as R packages. Here, we report webMCP-counter, a user-friendly web interface to allow all users to use these methods, regardless of their proficiency in the R programming language.Availability and ImplementationFreely available from http://134.157.229.105:3838/webMCP/. Website developed with the R package shiny. Source code available from GitHub: https://github.com/FPetitprez/webMCP-counter.

scholarly journals A cloud-based toolbox for the versatile environmental annotation of biodiversity data

PLoS Biology ◽  
2021 ◽  
Vol 19 (11) ◽  
pp. e3001460
Author(s):  
Richard Li ◽  
Ajay Ranipeta ◽  
John Wilshire ◽  
Jeremy Malczyk ◽  
Michelle Duong ◽  
...  
Keyword(s):  
R Package ◽  
Scale Dependence ◽  
Temporal Scale ◽  
Environmental Data ◽  
Task Management ◽  
Biodiversity Data ◽  
Web Based ◽  
Continental Scale ◽  
User Friendly ◽  
Vast Range

A vast range of research applications in biodiversity sciences requires integrating primary species, genetic, or ecosystem data with other environmental data. This integration requires a consideration of the spatial and temporal scale appropriate for the data and processes in question. But a versatile and scale flexible environmental annotation of biodiversity data remains constrained by technical hurdles. Existing tools have streamlined the intersection of occurrence records with gridded environmental data but have remained limited in their ability to address a range of spatial and temporal grains, especially for large datasets. We present the Spatiotemporal Observation Annotation Tool (STOAT), a cloud-based toolbox for flexible biodiversity–environment annotations. STOAT is optimized for large biodiversity datasets and allows user-specified spatial and temporal resolution and buffering in support of environmental characterizations that account for the uncertainty and scale of data and of relevant processes. The tool offers these services for a growing set of near global, remotely sensed, or modeled environmental data, including Landsat, MODIS, EarthEnv, and CHELSA. STOAT includes a user-friendly, web-based dashboard that provides tools for annotation task management and result visualization, linked to Map of Life, and a dedicated R package (rstoat) for programmatic access. We demonstrate STOAT functionality with several examples that illustrate phenological variation and spatial and temporal scale dependence of environmental characteristics of birds at a continental scale. We expect STOAT to facilitate broader exploration and assessment of the scale dependence of observations and processes in ecology.

STATISTICAL CALCULATOR

2018 ◽  
Author(s):  
Prana Ugiana Gio ◽  
Rezzy Eko Caraka
Keyword(s):  
R Package ◽  
Process Data ◽  
Web Based ◽  
Application Program ◽  
R Programming Language ◽  
Statistical Application ◽  
R Shiny ◽  
R Packages ◽  
R Programming ◽  
Mozilla Firefox

STATCAL is an free statistical application program that is designed using R programming language, in RStudio. STATCAL uses various R packages to perform graphical and statistical analysis. STATCAL is a web-based statistical application program. It means that STATCAL uses a browser, such as Google Chrome, Mozilla Firefox, etc, as a place or media to process data. R shiny package is a main package in STATCAL. R shiny package is an R package that can be used to create an interactive web-based application. STATCAL is created by Prana Ugiana Gio and Rezzy Eko Caraka on 2017.

scholarly journals Rtpca: an R package for differential thermal proximity coaggregation analysis

2020 ◽  
Author(s):  
Nils Kurzawa ◽  
André Mateus ◽  
Mikhail M Savitski
Keyword(s):  
Protein Interactions ◽  
Predictive Performance ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Protein Protein Interactions ◽  
Proteome Profiling ◽  
R Packages ◽  
User Friendly

Abstract Summary Rtpca is an R package implementing methods for inferring protein–protein interactions (PPIs) based on thermal proteome profiling experiments of a single condition or in a differential setting via an approach called thermal proximity coaggregation. It offers user-friendly tools to explore datasets for their PPI predictive performance and easily integrates with available R packages. Availability and implementation Rtpca is available from Bioconductor (https://bioconductor.org/packages/Rtpca). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

2021 ◽  
Author(s):  
Shuyu Zheng ◽  
Jehad Aldahdooh ◽  
Tolou Shadbahr ◽  
Yinyin Wang ◽  
Dalal Aldahdooh ◽  
...  
Keyword(s):  
Drug Combination ◽  
Drug Combinations ◽  
Data Repository ◽  
Graphical Interface ◽  
Network Modelling ◽  
Web Based ◽  
Manual Curation ◽  
Cancer Sample ◽  
Sensitivity Data ◽  
User Friendly

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

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