Python-Based Data Analysis Tool for a 6-DoF Industrial Robot

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): 1) King’s Health Partnership Research & Development Challenge Fund Award (funded through Medical Research Council, UK) 2) British Heart Foundation, UK Background Electroanatomic mapping data is typically stored in proprietary formats and is difficult to access and analyse. This presents a barrier to electrophysiology research. OpenEP has been developed over the past decade to address this issue, with the first public release of the software in late 2020. OpenEP provides a standard format for storing electrophysiology data as well as an extensive suite of analysis tools. However, the current implementation of OpenEP has several limitations. Firstly, it is written in Matlab and thus not accessible to researchers without a Matlab license. Secondly, it lacks a graphical interface mandating a level of programming expertise to use. Finally, it lacks interfaces for computational modelling data, limiting its use to clinical rather than simulated data. Purpose In this work we address these limitations by developing (1) a Python-based implementation of OpenEP; (2) a graphical interface for interacting with OpenEP data and (3) interfaces for computational modelling environments. Methods Following review of the OpenEP source code, we developed use-case documentation for each OpenEP function and the proposed graphical user interface. By inspection of simulation outputs from an open-source simulation environment (openCARP) we designed new interfaces for modelling data. The implementation of OpenEP-py and OpenEP-GUI is based on the standard scientific Python stack with a PyQt front-end. Results OpenEP-Py is an open-source Python implementation for manipulation of electrophysiology data. The feature set is under active development and aims to match that of the Matlab implementation of OpenEP. In addition, OpenEP-Py can read data from cardiac electrophysiology simulations. The simulated data is stored in the standard OpenEP data structure, meaning analysis tools for clinical data can be used on the simulated data. Further, clinical data can be exported into the openCARP format, streamlining the creation of patient-specific models for simulations. Finally, because the software is open-source, OpenEP provides a transparent process for analysing data and thereby improves the reproducibility of studies. OpenEP-GUI (Figure 1) is a cross-platform desktop application for the visualisation and analysis of clinical and simulated cardiac electrophysiology data. It uses OpenEP-Py for loading, visualising, analysis and exporting electroanatomic mapping data. Multiple cases can be loaded via the System Manager, facilitating qualitative and quantitative comparison between cases. OpenEP-Py and OpenEP-GUI are licensed under the GNU-GPL v3 [WS1] and available online. Conclusions OpenEP-Py and OpenEP-GUI provide a simple yet effective way to perform cardiac electrophysiology research. The software is both open-source and under active development. We welcome feedback, feature requests, and contributions from the wider electrophysiology research community.

Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl.Contact:peter@biomatters.com

BackgroundEven though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS.ResultsMEPHAS is a web-based GUI (https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files.ConclusionMEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.

In the aftermath of the London '7/7' attacks in 2005, UK government agencies required the development of a quick-running tool to predict the weapon and injury effects caused by the initiation of a person borne improvised explosive device (PBIED) within crowded metropolitan environments. This prediction tool, termed the HIP (human injury predictor) code, was intended to:--assist the security services to encourage favourable crowd distributions and densities within scenarios of 'sensitivity'; --provide guidance to security engineers concerning the most effective location for protection systems; --inform rescue services as to where, in the case of such an event, individuals with particular injuries will be located; --assist in training medical personnel concerning the scope and types of injuries that would be sustained as a consequence of a particular attack; --assist response planners in determining the types of medical specialists (burns, traumatic amputations, lungs, etc.) required and thus identify the appropriate hospitals to receive the various casualty types. This document describes the algorithms used in the development of this tool, together with the pertinent underpinning physical processes. From its rudimentary beginnings as a simple spreadsheet, the HIP code now has a graphical user interface (GUI) that allows three-dimensional visualization of results and intuitive scenario set-up. The code is underpinned by algorithms that predict the pressure and momentum outputs produced by PBIEDs within open and confined environments, as well as the trajectories of shrapnel deliberately placed within the device to increase injurious effects. Further logic has been implemented to transpose these weapon effects into forms of human injury depending on where individuals are located relative to the PBIED. Each crowd member is subdivided into representative body parts, each of which is assigned an abbreviated injury score after a particular calculation cycle. The injury levels of each affected body part are then summated and a triage state assigned for each individual crowd member based on the criteria specified within the 'injury scoring system'. To attain a comprehensive picture of a particular event, it is important that a number of simulations, using what is substantively the same scenario, are undertaken with natural variation being applied to the crowd distributions and the PBIED output. Accurate mathematical representation of such complex phenomena is challenging, particularly as the code must be quick-running to be of use to the stakeholder community. In addition to discussing the background and motivation for the algorithm and GUI development, this document also discusses the steps taken to validate the tool and the plans for further functionality implementation.

With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Eye movement analysis is an effective method for research on visual perception and cognition. However, recordings of eye movements present practical difficulties related to the cost of the recording devices and the programming of device controls for use in experiments. GazeParser is an open-source library for low-cost eye tracking and data analysis; it consists of a video-based eyetracker and libraries for data recording and analysis. The libraries are written in Python and can be used in conjunction with PsychoPy and VisionEgg experimental control libraries. Three eye movement experiments are reported on performance tests of GazeParser. These showed that the means and standard deviations for errors in sampling intervals were less than 1 ms. Spatial accuracy ranged from 0.7° to 1.2°, depending on participant. In gap/overlap tasks and antisaccade tasks, the latency and amplitude of the saccades detected by GazeParser agreed with those detected by a commercial eyetracker. These results showed that the GazeParser demonstrates adequate performance for use in psychological experiments.

This paper describes a system that concentrates on data collection to provide uniform data analysis. The result is an integrated data collection and analysis system designed for semiconductor manufacturers. It includes automatic data insertion programs using distributed databases for data storage. It has a statistical analysis package designed with a graphical user interface. The system provides data collection for all data types regardless of the data origin so that users can perform data analysis. For the examples, three devices from a single product line are tested on three different testers. Each tester executes the same tests. The test data files are inserted into a common database format so they can be analyzed.

In the field of digital AV equipment, the size of embedded software for the implementation of the graphical user interface (GUI) has increased. Also, as a remote controller is used as an input device, a GUI different from that of a computer using a mouse is required. We propose an example of applying the MVC model for GUI software design intended for remote controller operation, an efficient runtime architecture in line with this design model, and development tools. We also evaluated the improvement of software productivity when using this design model and its runtime architecture.

BackgroundAlthough high-throughput sequencing of small subunit rRNA genes has revolutionized our understanding of microbial ecosystems, these technologies generate data at depths that benefit from automated analysis. Here we present AXIOME (Automation, eXtension, and Integration Of Microbial Ecology), a highly flexible and extensible management tool for popular microbial ecology analysis packages that promotes reproducibility and customization in microbial research.FindingsAXIOME streamlines and manages analysis of small subunit (SSU) rRNA marker data in QIIME and mothur. AXIOME also implements features including the PAired-eND Assembler for Illumina sequences (PANDAseq), non-negative matrix factorization (NMF), multi-response permutation procedures (MRPP), exploring and recovering phylogenetic novelty (SSUnique) and indicator species analysis. AXIOME has a companion graphical user interface (GUI) and is designed to be easily extended to facilitate customized research workflows.ConclusionsAXIOME is an actively developed, open source project written in Vala and available from GitHub (http://neufeld.github.com/axiome) and as a Debian package. Axiometic, a GUI companion tool is also freely available (http://neufeld.github.com/axiometic). Given that data analysis has become an important bottleneck for microbial ecology studies, the development of user-friendly computational tools remains a high priority. AXIOME represents an important step in this direction by automating multi-step bioinformatic analyses and enabling the customization of procedures to suit the diverse research needs of the microbial ecology community.

Diminutive polyps among black and Latino populations undergoing screening colonoscopy: evidence supporting a resect and discard approach

Identification of proteins by MS plays an important role in proteomics. A crucial step concerns the identification of peptides from MS/MS spectra. The X!Tandem Project (http://www.thegpm.org/tandem) supplies an open-source search engine for this purpose. In this study, we present an open-source Java library called XTandem Parser that parses X!Tandem XML result files into an easily accessible and fully functional object model (http://xtandem-parser.googlecode.com). In addition, a graphical user interface is provided that functions as a usage example and an end-user visualization tool.

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided a significant impact on our understanding of biological systems. It contributes to almost all aspects of biomedical research, including studying developmental biology, disease progression, drug effects, and host-parasite relationships. However, the high-throughput data generation presents a challenge for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for gene expression and protein datasets analysis and visualization. GeneCloudOmics allows both microarray or RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides user access to 23 different data analytics and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathways enrichments, evolutionary analyses, pathological analyses and protein-protein interaction (PPI) identifications. The user performs all tasks through an intuitive graphical user interface (GUI) that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. GeneCloudOmics is a one-stop open-source tool for gene exprerssion data analysis and visualization and is freely available at http://combio-sifbi.org/GeneCloudOmics.

BackgroundAdvances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects.ResultsXplorSeq is a software package that facilitates the compilation, management and phylogenetic analysis of DNA sequences. XplorSeq was developed for, but is not limited to, high-throughput analysis of environmental rRNA gene sequences. XplorSeq integrates and extends several commonly used UNIX-based analysis tools by use of a Macintosh OS-X-based graphical user interface (GUI). Through this GUI, users may perform basic sequence import and assembly steps (base-calling, vector/primer trimming, contig assembly), perform BLAST (Basic Local Alignment and Search Tool; [1-3]) searches of NCBI and local databases, create multiple sequence alignments, build phylogenetic trees, assemble Operational Taxonomic Units, estimate biodiversity indices, and summarize data in a variety of formats. Furthermore, sequences may be annotated with user-specified meta-data, which then can be used to sort data and organize analyses and reports. A document-based architecture permits parallel analysis of sequence data from multiple clones or amplicons, with sequences and other data stored in a single file.ConclusionXplorSeq should benefit researchers who are engaged in analyses of environmental sequence data, especially those with little experience using bioinformatics software. Although XplorSeq was developed for management of rDNA sequence data, it can be applied to most any sequencing project. The application is available free of charge for non-commercial use at .

Development of public toxicogenomics software for microarray data management and analysis