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Kabier M, Gambacorta N, Trisciuzzi D, Kumar S, Nicolotti O, Mathew B. MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations. J Comput Chem 2024. [PMID: 38703357 DOI: 10.1002/jcc.27390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/12/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024]
Abstract
Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.
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Affiliation(s)
- Muzammil Kabier
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Nicola Gambacorta
- Division of Medical Genetics, IRCSS Foundation-Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia), Foggia, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
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Monney J, Dallaire SE, Stoutah L, Fanda L, Mégevand P. Voxeloc: a time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes. J Neurosci Methods 2024:110154. [PMID: 38697518 DOI: 10.1016/j.jneumeth.2024.110154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/26/2024] [Accepted: 04/27/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND Thanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient's brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired. NEW METHOD Voxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally. RESULTS With a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format. COMPARISON WITH EXISTING METHODS localizing full patients' iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better. CONCLUSIONS Voxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratize neuroscience research using iEEG.
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Affiliation(s)
- Jonathan Monney
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Shannon E Dallaire
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Dalhousie University, Halifax, Canada
| | - Lydia Stoutah
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Université Paris-Saclay, Paris, France
| | - Lora Fanda
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pierre Mégevand
- Clinical Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Basic Neuroscience department, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Neurology division, Geneva University Hospitals, Geneva, Switzerland.
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3
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Boudjehem R, Kulow A, Pérez J, Gautier E, Ould-chikh S, Pairis S, Hazemann JL, da Silva JC. ProSPyX: software for post-processing images of X-ray ptychography with spectral capabilities. J Synchrotron Radiat 2024; 31:399-408. [PMID: 38335147 PMCID: PMC10914158 DOI: 10.1107/s160057752400016x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/05/2024] [Indexed: 02/12/2024]
Abstract
X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron.
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Affiliation(s)
- Redhouane Boudjehem
- Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, 25 Avenue des Martyrs, BP 166, 38042 Grenoble, France
| | - Anico Kulow
- Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, 25 Avenue des Martyrs, BP 166, 38042 Grenoble, France
| | | | - Eric Gautier
- SPINTEC, Université Grenoble Alpes, CEA, CNRS, 17 rue des Martyrs, 38054 Grenoble, France
| | - Samy Ould-chikh
- King Abdullah University of Science and Technology, KAUST Catalysis Center, Advanced Functional Materials, Thuwal 23955, Saudi Arabia
| | - Sébastien Pairis
- Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, 25 Avenue des Martyrs, BP 166, 38042 Grenoble, France
| | - Jean-Louis Hazemann
- Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, 25 Avenue des Martyrs, BP 166, 38042 Grenoble, France
| | - Julio César da Silva
- Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, 25 Avenue des Martyrs, BP 166, 38042 Grenoble, France
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4
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Yu F, Liu K, Zhou H, Li M, Kong H, Zhang K, Wang X, Wang W, Xu Q, Pan Q, Wang Z, Wang Q. Finback: a web-based data collection system at SSRF biological macromolecular crystallography beamlines. J Synchrotron Radiat 2024; 31:378-384. [PMID: 38241124 PMCID: PMC10914168 DOI: 10.1107/s1600577523010615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/12/2023] [Indexed: 01/21/2024]
Abstract
An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with a user-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.
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Affiliation(s)
- Feng Yu
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Ke Liu
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Huan Zhou
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Minjun Li
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Huating Kong
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Kunhao Zhang
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Xingya Wang
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Weiwei Wang
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Qin Xu
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Qiangyan Pan
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Zhijun Wang
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
| | - Qisheng Wang
- The Division of Life Science, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 239 Zhengheng Road, Pudong, Shanghai 201204, People’s Republic of China
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Ogurtsov A, Alves G, Rubio A, Joyce B, Andersson B, Karlsson R, Moore ER, Yu YK. MiCId GUI: The Graphical User Interface for MiCId, a Fast Microorganism Classification and Identification Workflow with Accurate Statistics and High Recall. J Comput Biol 2024; 31:175-178. [PMID: 38301204 PMCID: PMC10874827 DOI: 10.1089/cmb.2023.0149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024] Open
Abstract
Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment. Another paramount factor to consider is the necessity of accurate statistical significance to properly control the proportion of false discoveries among the identified microorganisms, and antimicrobial-resistant proteins, and to provide robust biomass estimation. Recently, we have developed Microorganism Classification and Identification (MiCId) workflow that assigns accurate statistical significance to identified microorganisms, antimicrobial-resistant proteins, and biomass estimation. MiCId's workflow is also computationally efficient, taking about 6-17 minutes to process a tandem mass-spectrometry (MS/MS) experiment using computer resources that are available in most laptop and desktop computers, making it a portable workflow. To make data analysis accessible to a broader range of users, beyond users familiar with the Linux environment, we have developed a graphical user interface (GUI) for MiCId's workflow. The GUI brings to users all the functionality of MiCId's workflow in a friendly interface along with tools for data analysis, visualization, and to export results.
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Affiliation(s)
- Aleksey Ogurtsov
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Gelio Alves
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Alex Rubio
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Brendan Joyce
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Björn Andersson
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Roger Karlsson
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Nanoxis Consulting AB, Gothenburg, Sweden
| | - Edward R.B. Moore
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Culture Collection University of Gothenburg, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Yi-Kuo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
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6
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González-Rodríguez N, Areán-Ulloa E, Fernández-Leiro R. A web-based dashboard for RELION metadata visualization. Acta Crystallogr D Struct Biol 2024; 80:93-100. [PMID: 38265874 PMCID: PMC10836394 DOI: 10.1107/s2059798323010902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/20/2023] [Indexed: 01/26/2024] Open
Abstract
Cryo-electron microscopy (cryo-EM) has witnessed radical progress in the past decade, driven by developments in hardware and software. While current software packages include processing pipelines that simplify the image-processing workflow, they do not prioritize the in-depth analysis of crucial metadata, limiting troubleshooting for challenging data sets. The widely used RELION software package lacks a graphical native representation of the underlying metadata. Here, two web-based tools are introduced: relion_live.py, which offers real-time feedback on data collection, aiding swift decision-making during data acquisition, and relion_analyse.py, a graphical interface to represent RELION projects by plotting essential metadata including interactive data filtration and analysis. A useful script for estimating ice thickness and data quality during movie pre-processing is also presented. These tools empower researchers to analyse data efficiently and allow informed decisions during data collection and processing.
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Affiliation(s)
- Nayim González-Rodríguez
- Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain
| | - Emma Areán-Ulloa
- Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rafael Fernández-Leiro
- Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain
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7
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Aires-de-Sousa J. GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit. Mol Inform 2024; 43:e202300190. [PMID: 37885368 DOI: 10.1002/minf.202300190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 10/28/2023]
Abstract
GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol.
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Affiliation(s)
- Joao Aires-de-Sousa
- LAQV and REQUIMTE, Chemistry Department, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal
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8
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Singaraju JP, Kadiresan A, Bhoi RK, Gomez AH, Ma Z, Yang H. Organalysis: Multifunctional Image Preprocessing and Analysis Software for Cardiac Organoid Studies. Tissue Eng Part C Methods 2023; 29:572-582. [PMID: 37672553 PMCID: PMC10714253 DOI: 10.1089/ten.tec.2023.0150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023] Open
Abstract
Due to a growing need in visualizing human pluripotent stem cell-derived organoids from recent advancements in the field, an efficient bulk-processing application is necessary to provide preprocessing and image analysis services. In this study, we developed Organalysis, a high-accuracy, multifunctional, and accessible application that meets these needs by providing the functionality of image manipulation and enhancement, organoid area and intensity calculation, fractal analysis, noise removal, and feature importance computation. The image manipulation feature includes brightness and contrast adjustment. The area and intensity calculation computes six values for each image: organoid area, total image area, percentage of the image covered by organoid, the total intensity of organoid, the total intensity of organoid-by-organoid area, and total intensity of organoid by total image area. The fractal analysis function computes the fractal dimension value for each image. The noise removal function removes superfluous marks from the input images, such as bubbles and other unwanted noise. The feature importance function trains a lasso-regularized linear regression machine learning algorithm to identify cardiac growth factors that are the strongest determinants for cell differentiation. The batch processing of this application further builds on existing services like ImageJ to provide a more convenient way to process multiple images. Collectively, the versatility and preciseness of Organalysis demonstrate novelty, since no other current imaging software combines the capability of batch processing and the breadth of feature analysis. Therefore, Organalysis provides unique functions in cardiac organoid research and proves to be invaluable in regenerative medicine.
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Affiliation(s)
- Jathin Pranav Singaraju
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
- Texas Academy of Mathematics and Science, University of North Texas, Denton, Texas, USA
| | - Adheesh Kadiresan
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
- Texas Academy of Mathematics and Science, University of North Texas, Denton, Texas, USA
| | - Rahul Kumar Bhoi
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
| | - Angello Huerta Gomez
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
| | - Zhen Ma
- Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York, USA
- BioInspired Institute for Material and Living Systems, Syracuse University, Syracuse, New York, USA
| | - Huaxiao Yang
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
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9
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Abbas YM, Khan MI. Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive Analysis, and Empirical Verification. Materials (Basel) 2023; 16:7178. [PMID: 38005107 PMCID: PMC10673118 DOI: 10.3390/ma16227178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 11/05/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
In recent years, the field of construction engineering has experienced a significant paradigm shift, embracing the integration of machine learning (ML) methodologies, with a particular emphasis on forecasting the characteristics of steel-fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, persistent challenges remain-their opacity, lack of transparency, and real-world relevance for practitioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) boosting algorithm, crafting a comprehensive approach. Grounded in a meticulously curated database drawn from 43 seminal publications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of partial dependence plots (PDPs), revealing intricate relationships between input parameters and consequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against independent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thoroughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.
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Affiliation(s)
| | - Mohammad Iqbal Khan
- Department of Civil Engineering, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia;
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Wang Y, Sarfraz I, Pervaiz N, Hong R, Koga Y, Akavoor V, Cao X, Alabdullatif S, Zaib SA, Wang Z, Jansen F, Yajima M, Johnson WE, Campbell JD. Interactive analysis of single-cell data using flexible workflows with SCTK2. Patterns (N Y) 2023; 4:100814. [PMID: 37602214 PMCID: PMC10436054 DOI: 10.1016/j.patter.2023.100814] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 03/27/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.
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Affiliation(s)
- Yichen Wang
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Irzam Sarfraz
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Nida Pervaiz
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Rui Hong
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Yusuke Koga
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Vidya Akavoor
- Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA
| | - Xinyun Cao
- Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA
| | - Salam Alabdullatif
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Syed Ali Zaib
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Zhe Wang
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Frederick Jansen
- Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA
| | - Masanao Yajima
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - W. Evan Johnson
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Joshua D. Campbell
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
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11
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Svalina A, Pibernik J, Dolić J, Mandić L. Assessing the Design of Interactive Radial Data Visualizations for Mobile Devices. J Imaging 2023; 9:jimaging9050100. [PMID: 37233319 DOI: 10.3390/jimaging9050100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
The growing use of mobile devices in daily life has led to an increased demand for the display of large amounts of data. In response, radial visualizations have emerged as a popular type of visualization in mobile applications due to their visual appeal. However, previous research has highlighted issues with these visualizations, namely misinterpretation due to their column length and angles. This study aims to provide guidelines for designing interactive visualizations on mobile devices and new evaluation methods based on the results of an empirical study. The perception of four types of circular visualizations on mobile devices was assessed through user interaction. All four types of circular visualizations were found to be suitable for use within mobile activity tracking applications, with no statistically significant difference in responses by type of visualization or interaction. However, distinguishing characteristics of each visualization type were revealed depending on the category that is in focus (memorability, readability, understanding, enjoyment, and engagement). The research outcomes provide guidelines for designing interactive radial visualizations on mobile devices, enhance the user experience, and introduce new evaluation methods. The study's results have significant implications for the design of visualizations on mobile devices, particularly in activity tracking applications.
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Affiliation(s)
- Ana Svalina
- Faculty of Graphic Arts, University of Zagreb, 10000 Zagreb, Croatia
| | - Jesenka Pibernik
- Faculty of Graphic Arts, University of Zagreb, 10000 Zagreb, Croatia
| | - Jurica Dolić
- Faculty of Graphic Arts, University of Zagreb, 10000 Zagreb, Croatia
| | - Lidija Mandić
- Faculty of Graphic Arts, University of Zagreb, 10000 Zagreb, Croatia
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12
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Díaz-Martínez V, Orozco-Sandoval J, Manian V, Dhatt BK, Walia H. A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures. Sensors (Basel) 2023; 23:s23094370. [PMID: 37177572 PMCID: PMC10181662 DOI: 10.3390/s23094370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.
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Affiliation(s)
| | | | - Vidya Manian
- University of Puerto Rico, Mayagüez, PR 00681, USA
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13
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Gomes TEP, Cadete MS, Ferreira JAF, Febra R, Silva J, Noversa T, Pontes AJ, Neto V. Development of an Open-Source Injection Mold Monitoring System. Sensors (Basel) 2023; 23:3569. [PMID: 37050629 PMCID: PMC10098985 DOI: 10.3390/s23073569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/28/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors' knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns.
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Affiliation(s)
- Tiago E. P. Gomes
- TEMA—Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal; (T.E.P.G.)
| | - Mylene S. Cadete
- TEMA—Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal; (T.E.P.G.)
| | - Jorge A. F. Ferreira
- TEMA—Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal; (T.E.P.G.)
| | - Renato Febra
- Geco—Gabinete Técnico e Controlo de Moldes em Fabricação Lda, 2405-032 Maceira, Portugal
| | - João Silva
- CeNTI—Centro de Nanotecnologia e Materiais Técnicos, Funcionais e Inteligentes, 4760-034 Vila Nova de Famalicão, Portugal
| | - Tiago Noversa
- IPC—Institute for Polymers and Composites, Universidade do Minho, 4800-058 Guimarães, Portugal
| | - António J. Pontes
- IPC—Institute for Polymers and Composites, Universidade do Minho, 4800-058 Guimarães, Portugal
| | - Victor Neto
- TEMA—Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal; (T.E.P.G.)
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14
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Freitas A, Santos D, Lima R, Santos CG, Meiguins B. Pactolo Bar: An Approach to Mitigate the Midas Touch Problem in Non-Conventional Interaction. Sensors (Basel) 2023; 23:2110. [PMID: 36850707 PMCID: PMC9960067 DOI: 10.3390/s23042110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
New ways of interacting with computers is driving research, which is motivated mainly by the different types of user profiles. Referred to as non-conventional interactions, these are found with the use of hands, voice, head, mouth, and feet, etc. and these interactions occur in scenarios where the use of mouse and keyboard would be difficult. A constant challenge in the adoption of new forms of interaction, based on the movement of pointers and the selection of interface components, is the Midas Touch (MT) problem, defined as the involuntary action of selection by the user when interacting with the computer system, causing unwanted actions and harming the user experience during the usage process. Thus, this article aims to mitigate the TM problem in interaction with web pages using a solution centered on the Head Tracking (HT) technique. For this purpose, a component in the form of a Bar was developed and inserted on the left side of the web page, called the Pactolo Bar (PB), in order to enable or disable the clicking event during the interaction process. As a way of analyzing the effectiveness of PB in relation to TM, two stages of tests were carried out based on the collaboration of voluntary participants. The first step aims to find the data that would lead to the best configuration of the BP, while the second step aims to carry out a comparative analysis between the PB solution and the eViacam software, whose use is also focused on the HT technique. The results obtained from the use of PB were considered promising, since the analysis of quantitative data points to a significant prevention of involuntary clicks in the iteration interface and the analysis of qualitative data showed the development of a better user experience due to the ease of use, which can be noticed in elements such as the PB size, the triggering mechanism, and its positioning in the graphical interface. This study benefits in the context of the user experience, because, when using non-conventional interactions, basic items such as aspects of the graphic elements, and interaction events raise new studies that seek to mitigate the problem of the Midas Touch.
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15
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Hernández A, Endesfelder D, Einbeck J, Puig P, Benadjaoud MA, Higueras M, Ainsbury E, Gruel G, Oestreicher U, Barrios L, Barquinero JF. Biodose Tools: an R shiny application for biological dosimetry. Int J Radiat Biol 2023; 99:1378-1390. [PMID: 36731491 DOI: 10.1080/09553002.2023.2176564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023]
Abstract
INTRODUCTION In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used to deal with the statistical procedures needed for biological dosimetry, and in general those who made several calculations for different biomarkers were based on closed source software. Here we present a new open source program, Biodose Tools, that has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry). MATERIALS AND METHODS The application has been developed using the R programming language and the shiny package as a framework to create a user-friendly online solution. Since no unique method exists for the different mathematical processes, several meetings and periodic correspondence were held in order to reach a consensus on the solutions to be implemented. RESULTS The current version 3.6.1 supports dose-effect fitting for dicentric and translocation assay. For dose estimation Biodose Tools implements those methods indicated in international guidelines and a specific method to assess heterogeneous exposures. The app can include information on the irradiation conditions to generate the calibration curve. Also, in the dose estimate, information about the accident can be included as well as the explanation of the results obtained. Because the app allows generating a report in various formats, it allows traceability of each biological dosimetry study carried out. The app has been used globally in different exercises and training, which has made it possible to find errors and improve the app itself. There are some features that still need consensus, such as curve fitting and dose estimation using micronucleus analysis. It is also planned to include a package dedicated to interlaboratory comparisons and the incorporation of Bayesian methods for dose estimation. CONCLUSION Biodose Tools provides an open-source solution for biological dosimetry laboratories. The consensus reached helps to harmonize the way in which uncertainties are calculated. In addition, because each laboratory can download and customize the app's source code, it offers a platform to integrate new features.
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Affiliation(s)
- Alfredo Hernández
- Department of Animal Biology, Plant Biology and Ecology (BABVE), Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - David Endesfelder
- Department of Effects and Risks of Ionising and Non-Ionising Radiation, Federal Office for Radiation Protection, Neuherberg, Germany
| | - Jochen Einbeck
- Department of Mathematical Sciences, and Durham Research Methods Centre, Durham University, Durham, UK
| | - Pedro Puig
- Department of Mathematics, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Centre de Recerca Matemàtica, Bellaterra, Spain
| | - Mohamed Amine Benadjaoud
- Radiobiology and Regenerative Medicine Research Service (SERAMED), Institut de Radioprotection et de Sûreté Nucléaire, Fontenay-aux-Roses, France
| | - Manuel Higueras
- Scientific Computation & Technological Innovation Center (SCoTIC), Universidad de La Rioja, Logroño, Spain
| | | | - Gaëtan Gruel
- Radiobiology of Accidental Exposure Laboratory (LRAcc), Institut de Radioprotection et de Sûreté Nucléaire, Fontenay-aux-Roses, France
| | - Ursula Oestreicher
- Department of Effects and Risks of Ionising and Non-Ionising Radiation, Federal Office for Radiation Protection, Neuherberg, Germany
| | - Leonardo Barrios
- Department of Cell Biology, Physiology and Immunology (BCFI), Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Joan Francesc Barquinero
- Department of Animal Biology, Plant Biology and Ecology (BABVE), Universitat Autònoma de Barcelona, Bellaterra, Spain
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16
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Abstract
The structure of a protein defines its function and integrity and correlates with the protein folding stability (PFS). Quantifying PFS allows researchers to assess differential stability of proteins in different disease or ligand binding states, providing insight into protein efficacy and potentially serving as a metric of protein quality. There are a number of mass spectrometry (MS)-based methods to assess PFS, such as Thermal Protein Profiling (TPP), Stability of Proteins from Rates of Oxidation (SPROX), and Iodination Protein Stability Assay (IPSA). Despite the critical value that PFS studies add to the understanding of mechanisms of disease and treatment development, proteomics research is still primarily dominated by concentration-based studies. We found that a major reason for the lack of PFS studies is the lack of a user-friendly data processing tool. Here we present the first user-friendly software, CHalf, with a graphical user interface for calculating PFS. Besides calculating site-specific PFS of a given protein from chemical denature folding stability assays, CHalf is also compatible with thermal denature folding stability assays. CHalf also includes a set of data visualization tools to help identify changes in PFS across protein sequences and in between different treatment conditions. We expect the introduction of CHalf to lower the barrier of entry for researchers to investigate PFS, promoting the usage of PFS in studies. In the long run, we expect this increase in PFS research to accelerate our understanding of the pathogenesis and pathophysiology of disease.
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Affiliation(s)
- Chad D. Hyer
- Department
of Chemistry and Biochemistry, Brigham Young
University, Provo, Utah84602, United States
| | - Hsien-Jung L. Lin
- Department
of Chemistry and Biochemistry, Brigham Young
University, Provo, Utah84602, United States
| | - Connor T. Haderlie
- Department
of Chemistry and Biochemistry, Brigham Young
University, Provo, Utah84602, United States
| | - Monica Berg
- Department
of Chemistry and Biochemistry, Brigham Young
University, Provo, Utah84602, United States
| | - John C. Price
- Department
of Chemistry and Biochemistry, Brigham Young
University, Provo, Utah84602, United States
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17
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Kohler D, Kaza M, Pasi C, Huang T, Staniak M, Mohandas D, Sabido E, Choi M, Vitek O. MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments. J Proteome Res 2023; 22:551-556. [PMID: 36622173 DOI: 10.1021/acs.jproteome.2c00603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).
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Affiliation(s)
- Devon Kohler
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Maanasa Kaza
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Cristina Pasi
- Universitat Oberta de Catalunya, Barcelona 08018, Spain
| | - Ting Huang
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | | | | | - Eduard Sabido
- Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona 08003, Spain.,Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Meena Choi
- MPL, Genentech, South San Francisco, California 94080, United States
| | - Olga Vitek
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
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18
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Lechuga Y, Kandel G, Miguel JA, Martinez M. Development of an Automated Design Tool for FEM-Based Characterization of Solid and Hollow Microneedles. Micromachines (Basel) 2023; 14:133. [PMID: 36677194 PMCID: PMC9861112 DOI: 10.3390/mi14010133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Microneedle design for biomedical applications, such as transdermal drug delivery, vaccination and transdermal biosensing, has lately become a rapidly growing research field. In this sense, finite element analysis has been extendedly used by microneedle designers to determine the most suitable structural parameters for their prototypes, and also to predict their mechanical response and efficiency during the insertion process. Although many proposals include computer-aided tools to build geometrical models for mechanical analysis, there is a lack of software utilities intended to automate the design process encompassing geometrical modeling, simulation setup and postprocessing of results. This work proposes a novel MATLAB-based design tool for microneedle arrays that permits personalized selection of the basic characteristics of a mechanical model. The tool automatically exports the selected options to an ANSYS batch file, including instructions to run a static and a linear buckling analysis. Later, the subsequent simulation results can be retrieved for on-screen display and potential postprocessing. In addition, this work reviews recent proposals (2018-2022) about finite element model characterization of microneedles to establish the minimum set of features that any tool intended for automating a design process should provide.
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Affiliation(s)
- Yolanda Lechuga
- Group of Microelectronics Engineering, Department of Electronics Technology, Systems Engineering and Automation, Universidad de Cantabria, 39005 Santander, Spain
| | - Gregoire Kandel
- Group of Microelectronics Engineering, Department of Electronics Technology, Systems Engineering and Automation, Universidad de Cantabria, 39005 Santander, Spain
- ENSEIRB-MATMECA, Bordeaux INP, CEDEX, 33402 Talence, France
| | - Jose Angel Miguel
- Group of Microelectronics Engineering, Department of Electronics Technology, Systems Engineering and Automation, Universidad de Cantabria, 39005 Santander, Spain
| | - Mar Martinez
- Group of Microelectronics Engineering, Department of Electronics Technology, Systems Engineering and Automation, Universidad de Cantabria, 39005 Santander, Spain
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19
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Jin H, Wagner MW, Ertl-Wagner B, Khalvati F. An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology. Can Assoc Radiol J 2022:8465371221144264. [PMID: 36475925 DOI: 10.1177/08465371221144264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Deep learning techniques using convolutional neural networks (CNNs) have been successfully developed for various medical image analysis tasks. However, the skills to understand and develop deep learning models are not usually taught during radiology training, which constitutes a barrier for radiologists looking to integrate machine learning (ML) into their research or clinical practice. In this work, we developed and evaluated an educational graphical user interface (GUI) to construct CNNs for teaching deep learning concepts to radiology trainees. The GUI was developed in Python using the PyQt and PyTorch frameworks. The functionality of the GUI was demonstrated through a binary classification task on a dataset of MR images of the brain. The usability of the GUI was assessed through 45-min user testing sessions with 5 neuroradiologists and neuroradiology fellows, assessing mean task completion times, the System Usability Scale (SUS), and a qualitative questionnaire as metrics. Task completion times were compared against a ML expert who performed the same tasks. After a 20-min introduction to CNNs and a walkthrough of the GUI, users were able to perform all assigned tasks successfully. There was no significant difference in task completion time compared to a ML expert. The educational GUI achieved a score of 82.5 on the SUS, suggesting that the system is highly usable. Users indicated that the GUI seems useful as an educational tool to teach ML topics to radiology trainees. An educational GUI allows interactive teaching in ML that can be incorporated into radiology training.
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Affiliation(s)
- Haiyue Jin
- Division of Engineering Science, University of Toronto, Toronto, ON, Canada
| | - Matthias W Wagner
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Birgit Ertl-Wagner
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Farzad Khalvati
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada,Department of Computer Science, University of Toronto, Toronto, ON, Canada
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20
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Chellal AA, Lima J, Gonçalves J, Fernandes FP, Pacheco F, Monteiro F, Brito T, Soares S. Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System. Sensors (Basel) 2022; 22:9532. [PMID: 36502234 PMCID: PMC9740827 DOI: 10.3390/s22239532] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/20/2022] [Accepted: 11/26/2022] [Indexed: 06/12/2023]
Abstract
Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost.
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Affiliation(s)
- Arezki Abderrahim Chellal
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Engineering Department, School of Sciences and Technology, UTAD, 5000-801 Vila Real, Portugal
| | - José Lima
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- INESC TEC—INESC Technology and Science, 4200-465 Porto, Portugal
| | - José Gonçalves
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- INESC TEC—INESC Technology and Science, 4200-465 Porto, Portugal
| | - Florbela P. Fernandes
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Fátima Pacheco
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Fernando Monteiro
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Thadeu Brito
- Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
- INESC TEC—INESC Technology and Science, 4200-465 Porto, Portugal
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Salviano Soares
- Engineering Department, School of Sciences and Technology, UTAD, 5000-801 Vila Real, Portugal
- IEETA—Institute of Electronics and Informatics Engineering of Aveiro, 3810-193 Aveiro, Portugal
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21
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Laios A, De Freitas DLD, Saalmink G, Tan YS, Johnson R, Zubayraeva A, Munot S, Hutson R, Thangavelu A, Broadhead T, Nugent D, Kalampokis E, de Lima KMG, Theophilou G, De Jong D. Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score. Curr Oncol 2022; 29:9088-104. [PMID: 36547125 DOI: 10.3390/curroncol29120711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/11/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70-98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3-5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS.
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22
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Manu D, Tshakwanda PM, Lin Y, Jiang W, Yang L. Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices. J Imaging 2022; 8. [PMID: 36547477 DOI: 10.3390/jimaging8120312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/10/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022] Open
Abstract
Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it's use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.
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Sun J, Cao W, Yamanaka T. JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis. Front Plant Sci 2022; 13:964058. [PMID: 36275541 PMCID: PMC9583140 DOI: 10.3389/fpls.2022.964058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U2-Net; and (4) leaf segmentation with U2-Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.
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Ollenschläger M, Küderle A, Mehringer W, Seifer AK, Winkler J, Gaßner H, Kluge F, Eskofier BM. MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data. Sensors (Basel) 2022; 22:5849. [PMID: 35957406 PMCID: PMC9371110 DOI: 10.3390/s22155849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/17/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.
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Affiliation(s)
- Malte Ollenschläger
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Wolfgang Mehringer
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Ann-Kristin Seifer
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
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25
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Clement P, Petr J, Dijsselhof MBJ, Padrela B, Pasternak M, Dolui S, Jarutyte L, Pinter N, Hernandez-Garcia L, Jahn A, Kuijer JPA, Barkhof F, Mutsaerts HJMM, Keil VC. A Beginner's Guide to Arterial Spin Labeling (ASL) Image Processing. Front Radiol 2022; 2:929533. [PMID: 37492666 PMCID: PMC10365107 DOI: 10.3389/fradi.2022.929533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/23/2022] [Indexed: 07/27/2023]
Abstract
Arterial spin labeling (ASL) is a non-invasive and cost-effective MRI technique for brain perfusion measurements. While it has developed into a robust technique for scientific and clinical use, its image processing can still be daunting. The 2019 Ann Arbor ISMRM ASL working group established that education is one of the main areas that can accelerate the use of ASL in research and clinical practice. Specifically, the post-acquisition processing of ASL images and their preparation for region-of-interest or voxel-wise statistical analyses is a topic that has not yet received much educational attention. This educational review is aimed at those with an interest in ASL image processing and analysis. We provide summaries of all typical ASL processing steps on both single-subject and group levels. The readers are assumed to have a basic understanding of cerebral perfusion (patho) physiology; a basic level of programming or image analysis is not required. Starting with an introduction of the physiology and MRI technique behind ASL, and how they interact with the image processing, we present an overview of processing pipelines and explain the specific ASL processing steps. Example video and image illustrations of ASL studies of different cases, as well as model calculations, help the reader develop an understanding of which processing steps to check for their own analyses. Some of the educational content can be extrapolated to the processing of other MRI data. We anticipate that this educational review will help accelerate the application of ASL MRI for clinical brain research.
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Affiliation(s)
- Patricia Clement
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Jan Petr
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Mathijs B. J. Dijsselhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Beatriz Padrela
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Maurice Pasternak
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, OT, Canada
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Lina Jarutyte
- School of Psychological Science, University of Bristol, England, United Kingdom
| | - Nandor Pinter
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Dent Neurologic Institute, Buffalo, Amherst, NY, United States
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, United States
| | - Luis Hernandez-Garcia
- fMRI Laboratory, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Andrew Jahn
- fMRI Laboratory, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Joost P. A. Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
- Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, United Kingdom
| | - Henk J. M. M. Mutsaerts
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
- Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, United Kingdom
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, Netherlands
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26
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Palansooriya K, Li J, Dissanayake PD, Suvarna M, Li L, Yuan X, Sarkar B, Tsang DCW, Rinklebe J, Wang X, Ok YS. Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning. Environ Sci Technol 2022; 56:4187-4198. [PMID: 35289167 PMCID: PMC8988308 DOI: 10.1021/acs.est.1c08302] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 02/07/2022] [Accepted: 02/23/2022] [Indexed: 05/19/2023]
Abstract
Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.
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Affiliation(s)
- Kumuduni
N. Palansooriya
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South
Korea
| | - Jie Li
- Department
of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Pavani D. Dissanayake
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South
Korea
- Soils and
Plant Nutrition Division, Coconut Research
Institute, Lunuwila 61150, Sri Lanka
| | - Manu Suvarna
- Department
of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Lanyu Li
- Department
of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Xiangzhou Yuan
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South
Korea
| | - Binoy Sarkar
- Lancaster
Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Daniel C. W. Tsang
- Department
of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Jörg Rinklebe
- School
of Architecture and Civil Engineering, Institute of Foundation Engineering,
Water and Waste Management, Laboratory of Soil and Groundwater Management, University of Wuppertal, Pauluskirchstraße 7, 42285 Wuppertal, Germany
- Department
of Environment, Energy and Geoinformatics, Sejong University, 98
Gunja-Dong, Gwangjin-Gu, Seoul 05006, Republic of Korea
| | - Xiaonan Wang
- Department
of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yong Sik Ok
- Korea
Biochar Research Center, APRU Sustainable Waste Management Program
& Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South
Korea
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Zhang Y, Fang Q. BlenderPhotonics: an integrated open-source software environment for three-dimensional meshing and photon simulations in complex tissues. J Biomed Opt 2022; 27:083014. [PMID: 35429155 PMCID: PMC9010662 DOI: 10.1117/1.jbo.27.8.083014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE Rapid advances in biophotonics techniques require quantitative, model-based computational approaches to obtain functional and structural information from increasingly complex and multiscaled anatomies. The lack of efficient tools to accurately model tissue structures and subsequently perform quantitative multiphysics modeling greatly impedes the clinical translation of these modalities. AIM Although the mesh-based Monte Carlo (MMC) method expands our capabilities in simulating complex tissues using tetrahedral meshes, the generation of such domains often requires specialized meshing tools, such as Iso2Mesh. Creating a simplified and intuitive interface for tissue anatomical modeling and optical simulations is essential toward making these advanced modeling techniques broadly accessible to the user community. APPROACH We responded to the above challenge by combining the powerful, open-source three-dimensional (3D) modeling software, Blender, with state-of-the-art 3D mesh generation and MC simulation tools, utilizing the interactive graphical user interface in Blender as the front-end to allow users to create complex tissue mesh models and subsequently launch MMC light simulations. RESULTS Here, we present a tutorial to our Python-based Blender add-on-BlenderPhotonics-to interface with Iso2Mesh and MMC, which allows users to create, configure and refine complex simulation domains and run hardware-accelerated 3D light simulations with only a few clicks. We provide a comprehensive introduction to this tool and walk readers through five examples, ranging from simple shapes to sophisticated realistic tissue models. CONCLUSIONS BlenderPhotonics is user friendly and open source, and it leverages the vastly rich ecosystem of Blender. It wraps advanced modeling capabilities within an easy-to-use and interactive interface. The latest software can be downloaded at http://mcx.space/bp.
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Affiliation(s)
- Yuxuan Zhang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Northeastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
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Szklanny K, Wichrowski M, Wieczorkowska A. Prototyping Mobile Storytelling Applications for People with Aphasia. Sensors (Basel) 2021; 22:s22010014. [PMID: 35009557 PMCID: PMC8747090 DOI: 10.3390/s22010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/06/2021] [Accepted: 12/15/2021] [Indexed: 05/03/2023]
Abstract
Aphasia is a partial or total loss of the ability to articulate ideas or comprehend spoken language, resulting from brain damage, in a person whose language skills were previously normal. Our goal was to find out how a storytelling app can help people with aphasia to communicate and share daily experiences. For this purpose, the Aphasia Create app was created for tablets, along with Aphastory for the Google Glass device. These applications facilitate social participation and enhance quality of life by using visual storytelling forms composed of photos, drawings, icons, etc., that can be saved and shared. We performed usability tests (supervised by a neuropsychologist) on six participants with aphasia who were able to communicate. Our work contributes (1) evidence that the functions implemented in the Aphasia Create tablet app suit the needs of target users, but older people are often not familiar with tactile devices, (2) reports that the Google Glass device may be problematic for persons with right-hand paresis, and (3) a characterization of the design guidelines for apps for aphasics. Both applications can be used to work with people with aphasia, and can be further developed. Aphasic centers, in which the apps were presented, expressed interest in using them to work with patients. The Aphasia Create app won the Enactus Poland National Competition in 2015.
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Saffariha M, Jahani A, Jahani R. A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats. Plant Direct 2021; 5:e363. [PMID: 34849453 PMCID: PMC8611508 DOI: 10.1002/pld3.363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/30/2021] [Accepted: 11/04/2021] [Indexed: 05/27/2023]
Abstract
Hyperforin, a major bioactive constituent of Hypericum concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in Hypericum perforatum based on different ecological and phenological conditions. We employed artificial intelligence modeling techniques including multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) to examine the factors critical in predicting hyperforin content. We found that the MLP model (R 2 = .9) is the most suitable and precise model compared with RBF (R 2 = .81) and SVM (R 2 = .74) in predicting hyperforin in H. perforatum based on ecological conditions, plant growth, and soil features. Moreover, phenological stages, organic carbon, altitude, and total N are detected in sensitivity analysis as the main factors that have a considerable impact on hyperforin content. We also report that the developed graphical user interface would be adaptable for key stakeholders including producers, manufacturers, analytical laboratory managers, and pharmacognosists.
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Affiliation(s)
| | - Ali Jahani
- Assessment and Environment Risks DepartmentResearch Center of Environment and Sustainable DevelopmentTehranIran
| | - Reza Jahani
- Department of Pharmacology and Toxicology, School of PharmacyShahid Beheshti University of Medical SciencesTehranIran
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Fisher S, Oscarsson M, De Nolf W, Cotte M, Meyer J. Daiquiri: a web-based user interface framework for beamline control and data acquisition. J Synchrotron Radiat 2021; 28:1996-2002. [PMID: 34738955 PMCID: PMC8570207 DOI: 10.1107/s1600577521009851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
Daiquiri is a web-based user interface (UI) framework for control system monitoring and data acquisition designed for synchrotron beamlines. It provides simple, intuitive and responsive interfaces to control and monitor hardware, launch acquisition sequences and manage associated metadata. Daiquiri concerns itself only with the UI layer; it does not provide a scan engine or controls system but can be easily integrated with existing systems.
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Affiliation(s)
- Stuart Fisher
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38043 Grenoble, France
| | - Marcus Oscarsson
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38043 Grenoble, France
| | - Wout De Nolf
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38043 Grenoble, France
| | - Marine Cotte
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38043 Grenoble, France
- LAMS, CNRS UMR 8220, Sorbonne Universités, Univ Paris 06, 4 Place Jussieu, 75005 Paris, France
| | - Jens Meyer
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38043 Grenoble, France
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Spreizer S, Senk J, Rotter S, Diesmann M, Weyers B. NEST Desktop, an Educational Application for Neuroscience. eNeuro 2021; 8:ENEURO.0274-21.2021. [PMID: 34764188 PMCID: PMC8638679 DOI: 10.1523/eneuro.0274-21.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/20/2021] [Accepted: 09/19/2021] [Indexed: 11/21/2022] Open
Abstract
Simulation software for spiking neuronal network models matured in the past decades regarding performance and flexibility. But the entry barrier remains high for students and early career scientists in computational neuroscience since these simulators typically require programming skills and a complex installation. Here, we describe an installation-free Graphical User Interface (GUI) running in the web browser, which is distinct from the simulation engine running anywhere, on the student's laptop or on a supercomputer. This architecture provides robustness against technological changes in the software stack and simplifies deployment for self-education and for teachers. Our new open-source tool, NEST Desktop, comprises graphical elements for creating and configuring network models, running simulations, and visualizing and analyzing the results. NEST Desktop allows students to explore important concepts in computational neuroscience without the need to learn a simulator control language before. Our experiences so far highlight that NEST Desktop helps advancing both quality and intensity of teaching in computational neuroscience in regular university courses. We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities.
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Affiliation(s)
- Sebastian Spreizer
- Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance (JARA)-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
- Department of Computer Science, University of Trier, 54296 Trier, Germany
| | - Johanna Senk
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance (JARA)-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
| | - Stefan Rotter
- Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance (JARA)-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52074 Aachen, Germany
- Department of Physics, Faculty 1, Rheinisch-Westfälische Technische Hochschule Aachen University, 52074 Aachen, Germany
| | - Benjamin Weyers
- Department of Computer Science, University of Trier, 54296 Trier, Germany
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Yildiz H. IRTGUI: An R Package for Unidimensional Item Response Theory Analysis With a Graphical User Interface. Appl Psychol Meas 2021; 45:551-552. [PMID: 34866712 PMCID: PMC8640354 DOI: 10.1177/01466216211040532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In the last decade, many R packages were published to perform item response theory (IRT) analysis. Some researchers and practitioners have difficulty in using these functional tools because of their insufficient coding skills. The IRTGUI package provides these researchers a user-friendly GUI where they can perform unidimensional IRT analysis without coding skills. Using the IRTGUI package, person and item parameters, model and item fit indices can be obtained. Dimensionality and local independence assumptions can be tested. With the IRTGUI package, users can generate dichotomous data sets with customizable conditions. Also, Wright Maps, item characteristics and information curves can be graphically displayed. All outputs can be easily downloaded by users.
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Zhou G, Noto T, Sharma A, Yang Q, González Otárula KA, Tate M, Templer JW, Lane G, Zelano C. HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking. eNeuro 2021; 8:ENEURO. [PMID: 34544760 DOI: 10.1523/ENEURO.0509-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 08/09/2021] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
Epilepsy affects 3.4 million people in the United States, and, despite the availability of numerous antiepileptic drugs, 36% of patients have uncontrollable seizures, which severely impact quality of life. High-frequency oscillations (HFOs) are a potential biomarker of epileptogenic tissue that could be useful in surgical planning. As a result, research into the efficacy of HFOs as a clinical tool has increased over the last 2 decades. However, detection and identification of these transient rhythms in intracranial electroencephalographic recordings remain time-consuming and challenging. Although automated detection algorithms have been developed, their results are widely inconsistent, reducing reliability. Thus, manual marking of HFOs remains the gold standard, and manual review of automated results is required. However, manual marking and review are time consuming and can still produce variable results because of their subjective nature and the limitations in functionality of existing open-source software. Our goal was to develop a new software with broad application that improves on existing open-source HFO detection applications in usability, speed, and accuracy. Here, we present HFOApp: a free, open-source, easy-to-use MATLAB-based graphical user interface for HFO marking. This toolbox offers a high degree of intuitive and ergonomic usability and integrates interactive automation-assist options with manual marking, significantly reducing the time needed for review and manual marking of recordings, while increasing inter-rater reliability. The toolbox also features simultaneous multichannel detection and marking. HFOApp was designed as an easy-to-use toolbox for clinicians and researchers to quickly and accurately mark, quantify, and characterize HFOs within electrophysiological datasets.
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Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani SA, Davies GW, Mughal N, Moore LSP. Clinical Utility and Functionality of an Artificial Intelligence-Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Form Res 2021; 5:e27992. [PMID: 34115603 PMCID: PMC8320734 DOI: 10.2196/27992] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/19/2021] [Accepted: 05/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow. Objective Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting. Methods Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study. Results All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of “excellent.” The clinicians found the app intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern was related to the use of the app in isolation rather than in conjunction with other clinical parameters. However, most clinicians speculated that the app could positively reinforce or validate their clinical decision-making. Conclusions Translating artificial intelligence technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web-based app designed to predict the outcomes of patients with COVID-19 from an ANN.
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Affiliation(s)
- Ahmed Abdulaal
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Aatish Patel
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Ahmed Al-Hindawi
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Esmita Charani
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom
| | - Saleh A Alqahtani
- Johns Hopkins University, Baltimore, MD, United States.,King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Gary W Davies
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Nabeela Mughal
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.,North West London Pathology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Luke Stephen Prockter Moore
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.,North West London Pathology, Imperial College Healthcare NHS Trust, London, United Kingdom
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Kräter M, Abuhattum S, Soteriou D, Jacobi A, Krüger T, Guck J, Herbig M. AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. Adv Sci (Weinh) 2021; 8:e2003743. [PMID: 34105281 PMCID: PMC8188199 DOI: 10.1002/advs.202003743] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/08/2021] [Indexed: 05/13/2023]
Abstract
Artificial intelligence (AI)-based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy-to-use, adaptable, and open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming is presented. AID provides a variety of NN-architectures, allowing to apply trained models on new data, obtain performance metrics, and export final models to different formats. AID is benchmarked on large image datasets (CIFAR-10 and Fashion-MNIST). Furthermore, models are trained to distinguish areas of differentiated stem cells in images of cell culture. A conventional blood cell count and a blood count obtained using an NN are compared, trained on >1.2 million images, and demonstrated how AID can be used for label-free classification of B- and T-cells. All models are generated by non-programmers on generic computers, allowing for an interdisciplinary use.
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Affiliation(s)
- Martin Kräter
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Shada Abuhattum
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Despina Soteriou
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Angela Jacobi
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
- Department of Internal Medicine IUniversity Hospital Carl Gustav CarusTU DresdenDresden01307Germany
| | - Thomas Krüger
- Department of Internal Medicine IUniversity Hospital Carl Gustav CarusTU DresdenDresden01307Germany
- German Cancer Consortium (DKTK)Partner Site DresdenGerman Cancer Research Center (DKFZ)Heidelberg69120Germany
- Center for Regenerative Therapies (CRTD)TU DresdenDresden01307Germany
| | - Jochen Guck
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Maik Herbig
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
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Song D, Chen Y, Li J, Wang H, Ning T, Wang S. A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition. J Biophotonics 2021; 14:e202000456. [PMID: 33547854 DOI: 10.1002/jbio.202000456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/20/2021] [Accepted: 02/04/2021] [Indexed: 05/08/2023]
Abstract
It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user-friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky-Golay smoothing, area-under-curve normalization, mean-centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares-discriminant analysis, support vector machine (SVM), and PCA-SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open-source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.
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Affiliation(s)
- Dongliang Song
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Yishen Chen
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Jie Li
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Haifeng Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Tian Ning
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Shuang Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
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Groba B, Nieto-Riveiro L, Canosa N, Concheiro-Moscoso P, Miranda-Duro MDC, Pereira J. Stakeholder Perspectives to Support Graphical User Interface Design for Children with Autism Spectrum Disorder: A Qualitative Study. Int J Environ Res Public Health 2021; 18:4631. [PMID: 33925424 PMCID: PMC8123795 DOI: 10.3390/ijerph18094631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/19/2021] [Accepted: 04/23/2021] [Indexed: 11/17/2022]
Abstract
The development of digital supports for people with autism has increased considerably in recent years. Technology designers and developers have interpreted the needs and learning styles of people with autism in different ways. As a result, there are generic, non-specific or heterogeneous guidelines for the design and development of technology for people with autism. This study aims to identify and describe the recommended elements to support graphical user interface design for children with Autism Spectrum Disorder (ASD), considering the stakeholders' perspective, engaged in a computer application development. A qualitative, longitudinal, multicentre study was carried out. A sample of 39 participants belonging to four groups of stakeholders participated: children with autism, family members, professionals with experience in the intervention with children with autism, and professionals with expertise in the design and development of assistive technology. The techniques used to formalise the collection of information from participants were semi-structured interviews and observation. MAXQDA 2020 software (Verbi Software, Berlin, Germany) was used to analyse the data. The result is a guide with suggestions to support an interface design that emerges from the stakeholder perspectives. This study provides useful information to offer alternatives for children with ASD and facilitate the understanding of daily life.
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Affiliation(s)
| | - Laura Nieto-Riveiro
- CITIC, Research Group TALIONIS, Faculty of Health Sciences, Universidade da Coruña, 15071 A Coruña, Spain; (B.G.); (N.C.); (P.C.-M.); (M.d.C.M.-D.); (J.P.)
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Park J, Park J, Shin D, Choi Y. A BCI Based Alerting System for Attention Recovery of UAV Operators. Sensors (Basel) 2021; 21:2447. [PMID: 33918116 PMCID: PMC8037861 DOI: 10.3390/s21072447] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 12/11/2022]
Abstract
As unmanned aerial vehicles have become popular, the number of accidents caused by an operator's inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator's attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator's electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.
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Affiliation(s)
- Jonghyuk Park
- Department of Industrial Engineering and Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (J.P.); (J.P.)
- ai.m Inc., Gangnamdae-ro, Gangnam-gu, Seoul 06241, Korea
| | - Jonghun Park
- Department of Industrial Engineering and Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (J.P.); (J.P.)
| | - Dongmin Shin
- Department of Industrial and Management Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Korea;
| | - Yerim Choi
- ai.m Inc., Gangnamdae-ro, Gangnam-gu, Seoul 06241, Korea
- Department of Data Science, Seoul Women’s University, Hwarang-ro, Nowon-gu, Seoul 01797, Korea
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Laport F, Iglesia D, Dapena A, Castro PM, Vazquez-Araujo FJ. Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces. Sensors (Basel) 2021; 21:2220. [PMID: 33810122 PMCID: PMC8004835 DOI: 10.3390/s21062220] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/03/2022]
Abstract
Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user's intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms-one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.
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Affiliation(s)
- Francisco Laport
- CITIC Research Center, University of A Coruña, Campus de Elviña, 15071 A Coruña, Spain; (D.I.); (A.D.); (P.M.C.); (F.J.V.-A.)
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Hwang GB, Cho KN, Han CY, Oh HW, Yoon YH, Lee SE. Lossless Decompression Accelerator for Embedded Processor with GUI. Micromachines (Basel) 2021; 12:mi12020145. [PMID: 33572563 PMCID: PMC7911039 DOI: 10.3390/mi12020145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 01/16/2021] [Accepted: 01/28/2021] [Indexed: 02/06/2023]
Abstract
The development of the mobile industry brings about the demand for high-performance embedded systems in order to meet the requirement of user-centered application. Because of the limitation of memory resource, employing compressed data is efficient for an embedded system. However, the workload for data decompression causes an extreme bottleneck to the embedded processor. One of the ways to alleviate the bottleneck is to integrate a hardware accelerator along with the processor, constructing a system-on-chip (SoC) for the embedded system. In this paper, we propose a lossless decompression accelerator for an embedded processor, which supports LZ77 decompression and static Huffman decoding for an inflate algorithm. The accelerator is implemented on a field programmable gate array (FPGA) to verify the functional suitability and fabricated in a Samsung 65 nm complementary metal oxide semiconductor (CMOS) process. The performance of the accelerator is evaluated by the Canterbury corpus benchmark and achieved throughput up to 20.7 MB/s at 50 MHz system clock frequency.
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Peternel L, Schøn DT, Fang C. Binary and Hybrid Work-Condition Maps for Interactive Exploration of Ergonomic Human Arm Postures. Front Neurorobot 2021; 14:590241. [PMID: 33488376 PMCID: PMC7819876 DOI: 10.3389/fnbot.2020.590241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/09/2020] [Indexed: 11/18/2022] Open
Abstract
Ergonomics of human workers is one of the key elements in design and evaluation of production processes. Human ergonomics have a major impact on productivity as well as chronic health risks incurred by inappropriate working postures and conditions. In this paper we propose a novel method for estimating and communicating the ergonomic work condition called Binary Work-Condition Map, which provides a visualized feedback about work conditions of different configurations of an arm. The map is of binary nature and is derived by imposing the desired thresholds on considered ergonomic and safety related criteria. Therefore, the suggested arm postures in the map guarantee that all considered criteria are satisfied. This eliminates the ambiguity compared to state-of-the-art maps that uses continuous scales derived from weighted sum of multiple ergonomics criteria. In addition, to combine the advantages of both the binary map and the continuous map, we additionally propose a Hybrid Work-Condition Map that rules out unsuitable workspace with the binary map approach and renders the suitable workspace with the continuous map approach. The proposed approach was tested in simulation for various tasks and conditions. In addition, we conducted subjective evaluation experiments to compare the proposed methods with the state-of-the art method regarding the usability. The results indicated that the binary map is simpler to use, while the hybrid map is a good tradeoff between the binary and the continuous map. In selecting the map, strong points of each map should be considered with respect to the requirements of a specific application and task.
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Affiliation(s)
- Luka Peternel
- Delft Haptics Lab, Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
| | - Daniel Tofte Schøn
- SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Cheng Fang
- SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
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Nieves P, Arapan S, Kądzielawa AP, Legut D. MAELASviewer: An Online Tool to Visualize Magnetostriction. Sensors (Basel) 2020; 20:s20226436. [PMID: 33187158 PMCID: PMC7697634 DOI: 10.3390/s20226436] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 10/20/2020] [Accepted: 11/09/2020] [Indexed: 11/16/2022]
Abstract
The design of new materials for technological applications is increasingly being assisted by online computational tools that facilitate the study of their properties. In this work, based on modern web application frameworks, the online app MAELASviewer has been developed to visualize and analyze magnetostriction via a user-friendly interactive graphical interface. The features and technical details of this new tool are described in detail. Among other applications, it could potentially be used for the design of magnetostrictive materials for sensors and actuators.
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Simonne DH, Martini A, Signorile M, Piovano A, Braglia L, Torelli P, Borfecchia E, Ricchiardi G. THORONDOR: a software for fast treatment and analysis of low-energy XAS data. J Synchrotron Radiat 2020; 27:1741-1752. [PMID: 33147203 DOI: 10.1107/s1600577520011388] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 08/19/2020] [Indexed: 06/11/2023]
Abstract
THORONDOR is a data treatment software with a graphical user interface (GUI) accessible via the browser-based Jupyter notebook framework. It aims to provide an interactive and user-friendly tool for the analysis of NEXAFS spectra collected during in situ experiments. The program allows on-the-fly representation and quick correction of large datasets from single or multiple experiments. In particular, it provides the possibility to align in energy several spectral profiles on the basis of user-defined references. Various techniques to calculate background subtraction and signal normalization have been made available. In this context, an innovation of this GUI involves the usage of a slider-based approach that provides the ability to instantly manipulate and visualize processed data for the user. Finally, the program is characterized by an advanced fitting toolbox based on the lmfit package. It offers a large selection of fitting routines as well as different peak distributions and empirical ionization potential step edges, which can be used for the fit of the NEXAFS rising-edge peaks. Statistical parameters describing the goodness of a fit such as χ2 or the R-factor together with the parameter uncertainty distributions and the related correlations can be extracted for each chosen model.
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Affiliation(s)
- David Horst Simonne
- Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy
| | - Andrea Martini
- Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy
| | - Matteo Signorile
- Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy
| | - Alessandro Piovano
- Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy
| | - Luca Braglia
- CNR-IOM, TASC Laboratory, SS 14 km 163.5, Trieste 34149, Italy
| | - Piero Torelli
- CNR-IOM, TASC Laboratory, SS 14 km 163.5, Trieste 34149, Italy
| | - Elisa Borfecchia
- Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy
| | - Gabriele Ricchiardi
- Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy
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Molton F. Simultispin: A versatile graphical user interface for the simulation of solid-state continuous wave EPR spectra. Magn Reson Chem 2020; 58:718-726. [PMID: 32173891 DOI: 10.1002/mrc.5019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/31/2020] [Accepted: 03/09/2020] [Indexed: 06/10/2023]
Abstract
Solid-state continuous wave (cw) electronic paramagnetic resonance (EPR) spectroscopy is particularly suitable for metal complex analysis. Extracting magnetic parameters by simulation is often necessary to describe the electronic structure of the studied molecular compounds that can have various electronic spin states and characterized by different parameters like g-values, hyperfine coupling or zero field splitting values. Easyspin toolbox on MATLAB is a powerful tool, but for the user, it requires spending time with coding and could discourage nonexperts. Facing this context, we have developed a graphical user interface called Simultispin, dedicated to solid-state cw-EPR spectra simulation. Some examples of experimental spectra of metal complexes (mixture of low spin and high spin FeIII complexes, dynamic disorder of a CuII complex, photogeneration of a MnIII complex), highlighting specific solid-state functions, are described and analyzed based on simulations performed with Simultispin. We hope that its ergonomy and the ease to set up a complete set of parameters to get reliable simulations could help a large EPR community to improve the efficiency of their interpretations.
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Affiliation(s)
- Florian Molton
- Department of Molecular Chemistry, Grenoble Alpes University, Grenoble, France
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Peng Y, Zhang X, Li Y, Su Q, Wang S, Liu F, Yu C, Liang M. MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data. Front Neurosci 2020; 14:545. [PMID: 32742251 PMCID: PMC7364177 DOI: 10.3389/fnins.2020.00545] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/04/2020] [Indexed: 12/03/2022] Open
Abstract
With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUI-based toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.
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Affiliation(s)
- Yanmin Peng
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Xi Zhang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Yifan Li
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Qian Su
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Sijia Wang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Feng Liu
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.,Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunshui Yu
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.,Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
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Raggam P, Bauernfeind G, Wriessnegger SC. NICA: A Novel Toolbox for Near-Infrared Spectroscopy Calculations and Analyses. Front Neuroinform 2020; 14:26. [PMID: 32523524 PMCID: PMC7261925 DOI: 10.3389/fninf.2020.00026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/29/2020] [Indexed: 11/13/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) measures the functional activity of the cerebral cortex. The concentration changes of oxygenated (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) can be detected and associated with activation of the cortex in the investigated area (neurovascular coupling). Recorded signals of hemodynamic responses may contain influences from physiological signals (systemic influences, physiological artifacts) which do not originate from the cerebral cortex activity. The physiological artifacts contain the blood pressure (BP), respiratory patterns, and the pulsation of the heart. In order to perform a comprehensive analysis of recorded fNIRS data, a proper correction of these physiological artifacts is necessary. This article introduces NICA – a novel toolbox for near-infrared spectroscopy calculations and analyses based on MATLAB. With NICA it is possible to process and visualize fNIRS data, including different signal processing methods for physiological artifact correction. The artifact correction methods used in this toolbox are common average reference (CAR), independent component analysis (ICA), and transfer function (TF) models. A practical example provides results from a study, where NICA was used for analyzing the measurement data, in order to demonstrate the signal processing steps and the physiological artifact correction. The toolbox was developed for fNIRS data recorded with the NIRScout 1624 measurement device and the corresponding recording software NIRStar.
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Affiliation(s)
- Philipp Raggam
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | | | - Selina C Wriessnegger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
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47
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Abstract
BACKGROUND/AIMS Dose-escalation studies are essential in the early stages of developing novel treatments, when the aim is to find a safe dose for administration in humans. Despite their great importance, many dose-escalation studies use study designs based on heuristic algorithms with well-documented drawbacks. Bayesian decision procedures provide a design alternative that is conceptually simple and methodologically sound, but very rarely used in practice, at least in part due to their perceived statistical complexity. There are currently very few easily accessible software implementations that would facilitate their application. METHODS We have created MoDEsT, a free and easy-to-use web application for designing and conducting single-agent dose-escalation studies with a binary toxicity endpoint, where the objective is to estimate the maximum tolerated dose. MoDEsT uses a well-established Bayesian decision procedure based on logistic regression. The software has a user-friendly point-and-click interface, makes changes visible in real time, and automatically generates a range of graphs, tables, and reports. It is aimed at clinicians as well as statisticians with limited expertise in model-based dose-escalation designs, and does not require any statistical programming skills to evaluate the operating characteristics of, or implement, the Bayesian dose-escalation design. RESULTS MoDEsT comes in two parts: a 'Design' module to explore design options and simulate their operating characteristics, and a 'Conduct' module to guide the dose-finding process throughout the study. We illustrate the practical use of both modules with data from a real phase I study in terminal cancer. CONCLUSION Enabling both methodologists and clinicians to understand and apply model-based study designs with ease is a key factor towards their routine use in early-phase studies. We hope that MoDEsT will enable incorporation of Bayesian decision procedures for dose escalation at the earliest stage of clinical trial design, thus increasing their use in early-phase trials.
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Affiliation(s)
- Philip Pallmann
- Centre for Trials Research, College of Biomedical & Life Sciences, Cardiff University, Cardiff, UK
| | - Fang Wan
- Department of Mathematics & Statistics, Lancaster University, Lancaster, UK
| | - Adrian P Mander
- Centre for Trials Research, College of Biomedical & Life Sciences, Cardiff University, Cardiff, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Graham M Wheeler
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK & UCL Cancer Trials Centre, University College London, London, UK
| | - Christina Yap
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Sally Clive
- Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Lisa V Hampson
- Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - Thomas Jaki
- Department of Mathematics & Statistics, Lancaster University, Lancaster, UK
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48
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Thompson S, Dowrick T, Xiao G, Ramalhinho J, Robu M, Ahmad M, Taylor D, Clarkson MJ. SnappySonic: An Ultrasound Acquisition Replay Simulator. J Open Res Softw 2020; 8:8. [PMID: 32395246 PMCID: PMC7212065 DOI: 10.5334/jors.289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
SnappySonic provides an ultrasound acquisition replay simulator designed for public engagement and training. It provides a simple interface to allow users to experience ultrasound acquisition without the need for specialist hardware or acoustically compatible phantoms. The software is implemented in Python, built on top of a set of open source Python modules targeted at surgical innovation. The library has high potential for reuse, most obviously for those who want to simulate ultrasound acquisition, but it could also be used as a user interface for displaying high dimensional images or video data.
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Affiliation(s)
- Stephen Thompson
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Thomas Dowrick
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Goufang Xiao
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - João Ramalhinho
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Maria Robu
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Mian Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Dan Taylor
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Matthew J Clarkson
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
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49
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Spurney RJ, Van den Broeck L, Clark NM, Fisher AP, de Luis Balaguer MA, Sozzani R. tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks. Plant J 2020; 101:716-730. [PMID: 31571287 DOI: 10.1111/tpj.14558] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/20/2019] [Accepted: 09/17/2019] [Indexed: 06/10/2023]
Abstract
Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.
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Affiliation(s)
- Ryan J Spurney
- Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC, 27695, USA
| | - Lisa Van den Broeck
- Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA
| | - Natalie M Clark
- Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA
- Biomathematics Graduate Program, North Carolina State University, Raleigh, NC, 27695, USA
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, 50010, USA
| | - Adam P Fisher
- Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA
| | - Maria A de Luis Balaguer
- Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA
- Elo Life Systems, Durham, NC, 27709, USA
| | - Rosangela Sozzani
- Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA
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50
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Zhang XZ, Feng N, Ma AJ, Li BQ. Aligning retention time shifts in HPLC three-dimensional spectra by icoshift approach combined with data arrangement methods and the release of a graphical user interface. J Sep Sci 2019; 43:552-560. [PMID: 31670445 DOI: 10.1002/jssc.201900791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 10/28/2019] [Accepted: 10/28/2019] [Indexed: 11/07/2022]
Abstract
High-performance liquid chromatography coupled with photodiode array detection has been extensively applied in many fields and the peaks among the analyzed samples can be shifted due to the variations of instrumental and experimental conditions. In multivariate analysis, retention time alignment is an important pretreatment step. Hence, the shifted peaks in high-performance liquid chromatography coupled with photodiode array detection three-dimensional spectra should be aligned for further analysis. Being motivated by this purpose, the interval correlated shifting method combined with the proposed data arrangement methods are recommended and employed on high-performance liquid chromatography coupled with photodiode array detection data as a demonstration. We validate the alignment performance of the proposed method through comparison the consistency of the retention time before and after alignment. The obtained results demonstrated that the proposed method is capable of successful aligning the employed data. Additionally, the interval correlated shifting method combined with the data arrangement modes is implemented in an easy-to-use graphical user interface environment and so can be operated easily by users not familiar with programming languages.
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Affiliation(s)
- Xiang-Zhi Zhang
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen, P.R. China
| | - Na Feng
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen, P.R. China
| | - Ai-Jun Ma
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen, P.R. China
| | - Bao Qiong Li
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen, P.R. China
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