Graphical Interface Research Articles

Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models.

Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One-at-a-time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed. Global sensitivity analysis (GSA) methods provide more comprehensive assessments by accounting for changes in all uncertain or variable parameters, though at a higher computational cost. This tutorial article presents a software package for conducting both OAT and GSA of PBPK models built in the Open Systems Pharmacology (OSP) Suite. The tool is accessible through either an R script or a graphical user interface, and the outputs consist of sensitivity metrics of pharmacokinetic (PK) parameters, such as Cmax and AUC, evaluated with respect to model input parameters. Results are formatted according to regulatory standards. The OAT analysis methods comprise two-way local sensitivity analyses and probabilistic uncertainty analyses, whereas the GSA methods include the Morris, Sobol, and EFAST methods. These analyses can be conducted on single PBPK models or pairs of models for the evaluation of the sensitivity of PK parameter ratiosin drug-drug interaction studies. The practical application of the package is demonstrated through three illustrative case studies.

AlGrow: a graphical interface for easy, fast and accurate area and growth analysis of heterogeneously colored targets.

AlGrow software provides a graphical interface to define target color volumes as hulls in color space and applies them to image segmentation and growth rate analysis across a multiplexed image series.

Expanded Functionality and Portability for the Colvars Library.

Colvars is an open-source C++ library that provides a modular toolkit for collective-variable-based molecular simulations. It allows practitioners to easily create and implement descriptors that best fit a process of interest and to apply a wide range of biasing algorithms in collective variable space. This paper reviews several features and improvements to Colvars that were added since its original introduction. Special attention is given to contributions that significantly expanded the capabilities of this software or its distribution with major MD simulation packages. Collective variables can now be optimized either manually or by machine-learning methods, and the space of descriptors can be explored interactively using the graphical interface included in VMD. Beyond the spatial coordinates of individual molecules, Colvars can now apply biasing forces to mesoscale structures and alchemical degrees of freedom and perform simulations guided by experimental data within ensemble averages or probability distributions. It also features advanced computational schemes to boost the accuracy, robustness, and general applicability of simulation methods, including extended-system and multiple-walker adaptive biasing force, boundary conditions for metadynamics, replica exchange with biasing potentials, and adiabatic bias molecular dynamics. The library is made available directly within the main distributions of the academic software GROMACS, LAMMPS, NAMD, Tinker-HP, and VMD. The robustness of the software and the reliability of the results are ensured through the use of continuous integration with a test suite within the source repository.

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring

Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.

A parameterized modeling method for analyzing welding residual stress in orthotropic steel deck

This paper presents a new parameterized method for establishing OSD models and analyzing WRS, and two plugins are developed based on the graphical user interface (GUI) of Abaqus. This parameterized method addresses the extremely high cost of manual modeling of OSD in Abaqus. In the two presented plugins, the OSD dimensions and welds size, the material properties, the welding heating source, and the mesh properties are parameterized, and the OSD model can be created automatically by the plugins without users’ pre-processing operations in Abaqus, which significantly facilitates the OSD modeling and the WRS analysis. The plugins developed in this study can provide the WRS analysis in the OSD with any dimensions and welding technologies, and the applicability of the plugins is verified by the consistency between the simulated WRS analysis results by plugins and those reported in the literature.

Tuning the structural and mechanical properties of SiC-Li and SiC-Na alloys for aerospace application: an ab initio study

Aluminum (Al) and its alloys are popular in the aerospace industry due to their high strength-to-weight ratio, corrosion resistance, and ductility. However, these properties (extreme ductility and malleability) can compromise corrosion resistance, making them susceptible to dents and scratches. Silicon Carbide (SiC) is a promising alternative to Al and it alloys due to its higher Youngs modulus and excellent wear resistance, although it has the drawbacks of brittleness and higher density. This study investigated the structural and mechanical properties of SiC alloyed with lithium (SiC-Li) or sodium (SiC-Na) using ab initio calculations with the aim of tuning the structural and mechanical properties of SiC. Modeling was done using Burai software, which offers a friendly graphical user interface for Quantum ESPRESSO, thus facilitating the creation of input files, visualization of crystal structures and analysis of results. The results from this study showed that the addition of Li and Na lowered the density as well as the mechanical properties of SiC but still being favorably better than those of Al and its alloys, suggesting that the modeled alloys could potentially replace the traditional Al and its alloys in the aerospace industry. Further experimental studies are needed to validate these findings and to explore the possibility of simultaneous alloying of SiC with both Li and Na for enhanced performance.

Reference Sequence Browser: An R application with a user-friendly GUI to rapidly query sequence databases.

Land managers, researchers, and regulators increasingly utilize environmental DNA (eDNA) techniques to monitor species richness, presence, and absence. In order to properly develop a biological assay for eDNA metabarcoding or quantitative PCR, scientists must be able to find not only reference sequences (previously identified sequences in a genomics database) that match their target taxa but also reference sequences that match non-target taxa. Determining which taxa have publicly available sequences in a time-efficient and accurate manner currently requires computational skills to search, manipulate, and parse multiple unconnected DNA sequence databases. Our team iteratively designed a Graphic User Interface (GUI) Shiny application called the Reference Sequence Browser (RSB) that provides users efficient and intuitive access to multiple genetic databases regardless of computer programming expertise. The application returns the number of publicly accessible barcode markers per organism in the NCBI Nucleotide, BOLD, or CALeDNA CRUX Metabarcoding Reference Databases. Depending on the database, we offer various search filters such as min and max sequence length or country of origin. Users can then download the FASTA/GenBank files from the RSB web tool, view statistics about the data, and explore results to determine details about the availability or absence of reference sequences.

129Xe Image Processing Pipeline: An open-source, graphical user interface application for the analysis of hyperpolarized 129Xe MRI.

Hyperpolarized 129Xe MRI presents opportunities to assess regional pulmonary microstructure and function. Ongoing advancements in hardware, sequences, and image processing have helped it become increasingly adopted for both research and clinical use. As the number of applications and users increase, standardization becomes crucial. To that end, this study developed an executable, open-source 129Xe image processing pipeline (XIPline) to provide a user-friendly, graphical user interface-based analysis pipeline to analyze and visualize 129Xe MR data, including scanner calibration, ventilation, diffusion-weighted, and gas exchange images. The customizable XIPline is designed in MATLAB to analyze data from all three major scanner platforms. Calibration data is processed to calculate optimal flip angle and determine129Xe frequency offset. Data processing includes loading, reconstructing, registering, segmenting, and post-processing images. Ventilation analysis incorporates three common algorithms to calculate ventilation defect percentage and novel techniques to assess defect distribution and ventilation texture. Diffusion analysis features ADC mapping, modified linear binning to account for ADC age-dependence, and common diffusion morphometry methods. Gas exchange processing uses a generalized linear binning for data acquired using 1-point Dixon imaging. The XIPline workflow is demonstrated using analysis from representative calibration, ventilation, diffusion, and gas exchange data. The application will reduce redundant effort when implementing new techniques across research sites by providing an open-source framework for developers. In its current form, it offers a robust and adaptable platform for 129Xe MRI analysis to ensure methodological consistency, transparency, and support for collaborative research across multiple sites and MRI manufacturers.

Educational Software for Stress Analysis of Open Thin-Walled Structures

Thin-walled structures are commonly employed in aerospace and automotive structures because of their high strength to weight ratio. Design and hence the stress analysis of thin-walled structures is tedious and time-consuming. An educational software which can aid students in stress analysis of thin-walled open sections will enhance the teaching-learning process. With the present available software, the number of sections that can be analysed is limited, hence in this work, a generalized section has been developed through which many new complex sections can be generated and analysed for stresses. The objective of the present work is to develop educational software that will provide a solution for the section properties like centroid, moment of inertia and shear centre, and the stresses on the section for a given loading. The software enables students to select different cross-sections which may be subjected to bending, shear or torsional loads and evaluate the stresses on it. Results obtained through this software have been validated against literature. The software has been developed using MATLAB with graphical user interface (GUI). The software is expected to be a useful tool for effective teaching learning process of courses on thin-walled structures, aircraft structures and advanced mechanics of materials.

An Automated Computational Fluid Dynamics Workflow for Simulating the Internal Flow of Race Car Radiators

In this article, we present a software tool developed in Python, named T-WorkFlow. It has been devised to meet some of the design needs of Tatuus Racing S.p.a., a leading company in the design and production of racing cars for the FIA Formula 3 Regional and Formula 4 categories. The software leverages the open-source tools OpenFOAM and FreeCAD to fully automate the fluid dynamics simulation process within car radiators. The goal of T-WorkFlow is to provide designers with precise and easily interpretable results that facilitate the identification of the geometry, ensuring optimal flow distribution in the radiator channels. T-WorkFlow requires the radiator’s geometry files in .stp and .stl formats, along with additional user inputs provided through a graphical interface. For mesh generation, the software leverages the OpenFOAM tools blockMesh and snappyHexMesh. To ensure uniform mesh quality across different configurations, and thus, comparable numerical results, various pre-processing operations on the specific geometry files are needed. After generating the mesh, T-WorkFlow automatically defines a control surface for each radiator channel to monitor the volumetric flow rate distribution. This is achieved by combining the OpenFOAM command topoSet with specific geometric information directly obtained from the radiator’s CAD through FreeCAD. During the simulation, the software provides various outputs that automate the main post-processing operations, enabling quick and easy identification of the configuration that ensures the desired performance.

A GUI Based Application for Breast Cancer Diagnosis from Histopathology Images Using a Sequential Convolutional Neural Network Model

Breast cancer is the most common type of cancer among women globally. Automated breast cancer diagnosis improves healthcare by saving time and providing valuable assistance to pathologists. The focus of this research is the development of a deep learning framework named BCnet (Breast Cancer net), which aims to automatically diagnose breast cancer using histopathological images at various levels of magnification. We train the model from scratch using the BreakHis dataset, incorporating augmentation techniques. BCnet's performance is subsequently evaluated against current architectures that utilize the transfer learning approach. Furthermore, a Graphical User Interface (GUI) application was specifically designed for pathologists to use. BCnet was 95% accurate, while VGG-16, VGG-19, Xception, InceptionResnetV2, and Resnet152V2 were 78%, 79%, 77%, 79%, and 84% accurate, respectively. BCnet hence functions as an intelligent assistance system for pathologists and a potent tool for timely and precise identification, with the capacity to significantly influence patient outcomes.

A comparative ensemble approach to bedload prediction using metaheuristic machine learning

An adequate bedload prediction is a challenging task in hydraulic engineering because of the complex sediment transport processes and corresponding environmental factors. The current study introduces a novel comparative ensemble approach using metaheuristic machine learning (ML) models to enhance the accuracy of bedload prediction using data from laboratory flume experiments. To uncover key insights in bedload transport prediction, several models are employed, such as K-Nearest Neighbours (KNN), Extra Trees Regressor (ETR), Linear Regression (LR), Random Forest (RF), Bagging Regressor (BR), and XGBoost (XGB). The coefficient of determination (R2) and root mean square error (RMSE) values vary between various models; however, XGB showed R2 = 0.99 and RMSE = 0.11. The sensitivity analysis emphasizes the crucial role of the Shields parameter in bedload prediction, while the SHAP analysis highlights the substantial influence of the XGB model in enhancing predictive accuracy. The REC curves show that BR, XGB, and RF, outperformed KNN and LR models. Furthermore, a graphical user interface has also been developed to facilitate user interaction with the predictive models, allowing for easier visualization, analysis, and interpretation of bedload transport predictions. Additionally, k-fold cross-validation was performed to assess the performance, consistency and robustness of the ML models. Thus, it can be concluded from the current study that the utilization of ML algorithms can improve accuracy, providing valuable insights for hydraulic engineers and highlighting the importance of ML models in civil engineering practices, particularly in bedload transport prediction.

DYNAMO-HIA: A health impact assessment tool fit for quantifying tobacco control policy

Abstract DYNAMO-HIA is a generic, publicly-available and easy to use software tool for health impact assessments of changes in health risk-factors such as obesity, air pollution, processed meat consumption, physical activity, alcohol taxation, or smoking. The tool uses a graphic use interface, does not require programming skills, and has comparatively modest data requirements. Health outcomes that can be investigated are mortality, life expectancy, healthy-life expectancy, disability and prevalence of various chronic disease such as chronic obstructive pulmonary disease (COPD), cancer, or coronary heart diseases. DYNAMO-HIA can in principle be used for any tobacco control policy or ordinance-e.g. price increases or pictorial health warning labels-that target changes in smoking prevalence or smoking initiation/quitting rates. Past applications of DYNAMO-HIA for tobacco control policy included country studies for Belgium, Mongolia, or Korea and several comparative country studies. In this presentation we will give a general overview into the use of DYNAMO-HIA and show-case a recently published study. This study quantified the reduction in COPD burden that would occur in Italy, England and Sweden over 40 years if tobacco prices were increased by 5%, 10% and 20% over current local prices.

Development of an Open-Source Robotic NDT Solution for Automated Composite Repair Testing

Abstract Non-destructive testing (NDT) plays an important role in aircraft maintenance and repair processes, ensuring the structural integrity necessary for safe operation. The paper presents the design and evaluation of an animated, low-cost robotic NDT system tailored for inspecting composite bonding agents. The system integrates commercially available components, including a three-degree-of-freedom robotic arm and a Raspberry Pi 4B, managed by custom Python software with a user-friendly graphical interface. Mechanical Impedance Analysis (MIA) and Eddy Current Testing (ET) methods were employed to assess the system’s performance on representative test specimens. Results indicate that the system delivers reliable and accurate measurements comparable to commercial tools like the MAUS V, while offering simplicity and modularity. Limitations such as scanning speed and handling of complex geometries are acknowledged, with potential solutions proposed for future enhancement. The system provides an affordable and customizable alternative for NDT automation in the aerospace industry.

Impact of adjacent thoracic structures in negative left atrial remodelling in atrial fibrillation

Abstract Background Previous work has suggested that compression from external structures can promote negative atrial remodelling in atrial fibrillation (AF) [1]. This is mechanistically plausible, with many recognised risk factors in developing AF (age, hypertension, obesity, obstructive sleep apnoea, etc.) related through activation of the autonomic nervous system with an associated increase in atrial pressure and chronic elevation in wall stress [2,3]. Such negative remodelling contributes to the existence of arrhythmogenic atrial substrate [4]. This is highly significant when we consider catheter ablation of extrapulmonary vein targets. Purpose This study aims to investigate the relationship between abnormal conduction patterns indicative of potential AF drivers and their proximity to structures adjacent to the left atrium (LA). Methods Eight patients with paroxysmal or persistent AF, scheduled for an elective catheter ablation procedure using a charge density mapping (CDM) system [5] underwent pre-procedural cardiac MRI. 3D surface mesh reconstructions of individuals’ LA and adjacent structures (ascending aorta [AoA], descending aorta [AoD], and spine) were produced from 2D MRI slices using a proprietary graphical interface tool developed in MATLAB (Figure 1A & B) [6]. MRI derived LA geometries were registered with their CDM counterparts by the Iterative Closest Point algorithm (Figure 1C); this enabled integration of MRI LA geometries into the CDM system. Subsequently, non-contact intrachamber voltage measurements from individuals’ procedures were used to derive cardiac activation directly onto MRI LA geometries. Abnormal conduction patterns representing possible AF drivers (focal firing, rotational propagation, and pivoting propagation) [5] were quantified as occurrences per second at each vertex of MRI LA geometries (Figure 1D). These conduction patterns could then be visualised and assessed in the context of adjacent structures (Figure 1E), with the Euclidean distance between each vertex of the MRI LA geometries and the closest point to their adjacent structures being calculated. Results Mean age was 65±9 years; 4 patients (50%) were male; AF was paroxysmal in 3 (37%) and persistent in 5 (63%); median time since AF diagnosis was 1 (1-3.8) years; ablation type was de novo in 5 (63%) and retreatment in 3 (38%). Frequencies of pivoting and rotational propagation were notably higher in regions of the LA closer to the AoA, and lower near the AoD and spine (Figure 2A & B). Conversely, focal firing showed a less prominent trend, being more frequent in LA regions nearer the AoD and distant from the AoA (Figure 2C). Conclusions Compression from the ascending aorta may promote anterior LA remodelling in AF, leading to increased pivoting and rotational AF drivers. Clinically, this suggests a potential target for ablation therapy with an anterior seatbelt line connecting the right superior pulmonary vein to the mitral annulus.

Evaluation of root canal filling length on periapical radiograph using artificial intelligence.

This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques. 1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long. Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings. Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.

Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.

Bladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is crucial. Herein, endoscopic images are meticulously evaluated by experts, and sometimes even by different disciplines, to identify tissue types. It is believed that the time spent by experts can be utilized for patient treatment with the creation of a computer-aided decision support system. For this purpose, in this study, it is evaluated that the performances of three models proposed using the bladder tissue dataset. The first model is a convolutional neural network (CNN)-based deep learning (DL) network, and the second is a model named hybrid cnn-machine learning (ML) or DL + ML, which involves classifying deep features obtained from a CNN-based network with ML. The last one, and the one that achieved the best performance metrics, is a vision transformer (ViT) architecture. Furthermore, a graphical user interface (GUI) is provided for an accessible decision support system. As a result, accuracy and F1 score values for DL, DL + ML, and ViT models are 0.9086-0.8971-0.9257 and 0.8884-0.8496-0.8931, respectively.

Cell-nanoparticle stickiness and dose delivery in a multi-model in silico platform: DosiGUI

BackgroundIt is well-known that nanoparticles sediment, diffuse and aggregate when dispersed in a fluid. Once they approach a cell monolayer, depending on the affinity or “stickiness” between cells and nanoparticles, they may adsorb instantaneously, settle slowly – in a time- and concentration-dependent manner - or even encounter steric hindrance and rebound. Therefore, the dose perceived by cells in culture may not necessarily be that initially administered. Methods for quantifying delivered dose are difficult to implement, as they require precise characterization of nanoparticles and exposure scenarios, as well as complex mathematical operations to handle the equations governing the system dynamics. Here we present a pipeline and a graphical user interface, DosiGUI, for application to the accurate nano-dosimetry of engineered nanoparticles on cell monolayers, which also includes methods for determining the parameters characterising nanoparticle-cell stickiness.ResultsWe evaluated the stickiness for 3 industrial nanoparticles (TiO2 – NM-105, CeO2 – NM-212 and BaSO4 – NM-220) administered to 3 cell lines (HepG2, A549 and Caco-2) and subsequently estimated corresponding delivered doses. Our results confirm that stickiness is a function of both nanoparticle and cell type, with the stickiest combination being BaSO4 and Caco-2 cells. The results also underline that accurate estimations of the delivered dose cannot prescind from a rigorous evaluation of the affinity between the cell type and nanoparticle under investigation.ConclusionAccurate nanoparticle dose estimation in vitro is crucial for in vivo extrapolation, allowing for their safe use in medical and other applications. This study provides a computational platform – DosiGUI – for more reliable dose-response characterization. It also highlights the importance of cell-nanoparticle stickiness for better risk assessment of engineered nanomaterials.

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Detection, recognition and transmission of snoring signals by ESP32

This study focuses on the monitoring, transmission, recognition and detection of snoring signals and their relationship with obstructive sleep apnea. To achieve this purpose, the ESP32 microcontroller and a MEMS technology microphone were used to capture and measure characteristic parameters of snoring signals, such as their intensity, frequency and duration. In addition, the WiFi radio interface was used to send the signals to a server where the information was processed, the snoring was detected, linked to a chatbot in Nodred to show the user in a graphical interface his diagnosis of the snoring level. This comprehensive approach allows real-time, wireless monitoring of snoring, leading to a less invasive diagnosis of obstructive sleep apnea.