User-friendly Software Research Articles

Imaging Single Particle Profiler to Study Nanoscale Bioparticles Using Conventional Confocal Microscopy.

Single particle profiling (SPP) is a unique methodology to study nanoscale bioparticles such as liposomes, lipid nanoparticles, extracellular vesicles, and lipoproteins in a single particle and high throughput manner. The initial version requires the single photon counting modules for data acquisition, which limits its adoptability. Here, we present imaging-based SPP (iSPP) that can be performed by imaging a spot over time in the common imaging mode with confocal detectors. We also provide user-friendly software with a graphical user interface to analyze such data and give quantitative insights on the content and properties of nanoscale bioparticles. We use iSPP to decipher lipid-protein interactions, membrane modifications by drugs, and the heterogeneity of extracellular vesicles isolated from cell lines and human urine. This easily applicable modality of the single particle profiler will facilitate nanoscale bioparticle research in laboratories with access to any confocal microscope.

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. A total of 334 normal and 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) were included, signal processing theory was employed, and model interpretability was rigorously evaluated. Leveraging SHapley Additive exPlanations (SHAP)-based global feature ranking and partial dependency analysis (PDA) estimated decision boundary cut-offs on machine learning (ML) models, a novel algorithm was developed to implement an XAI tool. Using the selected features, ML models produce an AUC of 0.96 (95% CI: 0.95–0.98), 0.98 (95% CI: 0.96–1.00) and 1.00 (95% CI: 1.00–1.00) respectively on differentiating early, moderate and advanced glaucoma patients. Overall, machine outperformed clinicians in the early stage and overall glaucoma diagnosis with 10.4 –11.2% higher accuracy. The developed user-friendly XAI software tool shows potential as a valuable tool for eye care practitioners, offering transparent and interpretable insights to improve decision-making.

Sicurpest: A Prototype of a User-Friendly Tool for Preventive Risk Assessment of Pesticide Use in Agriculture

In recent years, there has been a significant increase in attention towards the use of pesticides, as evidenced by the introduction of regulations aimed at ensuring their safe and environmentally responsible application. Although this stricter legislative framework offers potential benefits, the challenges faced by farmers, particularly those in small-scale operations, in complying with occupational health and safety (OHS) requirements are considerable. To address this issue, a research project was promoted by the Italian Workers’ Compensation Authority (INAIL) aimed at developing a user-friendly software tool to support farmers in planning pesticide applications in safe conditions. This study summarizes the results of the research, which consisted of three main phases: the definition of the main parameters that characterize the farmers’ exposure based on the analysis of the literature; the development of a risk assessment model that integrates these determinants with data provided by producers for each authorized pesticide; and the development of software tool (called SICURPEST) for a preliminary risk assessment when using pesticides. This tool serves as a preliminary risk assessment method, offering a simplified model for practical use by farmers. Its initial verification was conducted through a case study and the results represent a step towards promoting safer pesticide practices, providing a solid basis for further validation.

Protocol for quantifying muscle fiber size, number, and central nucleation of mouse skeletal muscle cross-sections using Myotally software.

Here, we present a protocol for using Myotally, a user-friendly software for fast, automated quantification of muscle fiber size, number, and central nucleation from immunofluorescent stains of mouse skeletal muscle cross-sections. We describe steps for installing the software, preparing compatible images, finding the file path, and selecting key parameters like image quality and size limits. We also detail optional features, such as measuring mean fluorescence. By automating these traditionally labor-intensive processes, Myotally improves research efficiency and data consistency.

DroughtStats: A comprehensive software for drought monitoring and analysis

The significance of drought monitoring and prediction systems has grown substantially due to the escalating impacts of climate change. However, existing tools for drought analysis face several limitations, including restricted functionality to single-variable indices, reliance on predefined probability distributions, lack of flexibility in choosing distributions, and the need for advanced programming expertise. These constraints hinder comprehensive and accurate drought assessments. This study introduces DroughtStats, a novel, user-friendly software designed to overcome these challenges and enhance drought analysis capabilities. DroughtStats integrates advanced statistical tools to analyze hydrometeorological data, compute both single-variable and multivariable drought indices using empirical and parametric methods, and evaluate drought characteristics with improved accuracy. Notably, it supports a broader range of probability distributions, performs copula-based analyses, and estimates potential evapotranspiration using multiple methods, including Penman–Monteith. Additionally, DroughtStats can analyze the relationship between different datasets using techniques like copula-based Kendall’s tau. By addressing the limitations of existing tools, DroughtStats provides a more flexible and comprehensive approach to drought monitoring. Its versatility and global applicability are demonstrated through a case study in Turkey’s Çoruh River Basin (CRB), where drought indices based on precipitation and streamflow are calculated to characterize drought conditions. The results show that DroughtStats can successfully identify and characterize drought events at various time scales, providing valuable insights into drought severity, frequency, and recovery, and offering a reliable tool for ongoing drought monitoring and management.

Rapid and Robust Monitoring of Phytophthora Infectivity Through Detached Leaf Assays with Automated Image Analysis.

The detached leaf assay is a valuable method for studying plant-pathogen interactions, enabling the assessment of pathogenicity, plant resistance, and treatment effects. In this protocol, we outline how to set up a Phytophthora detached leaf assay and use non-expert machine learning tools to increase the reliability and throughput of the image analysis. Utilizing ilastik for pixel classification and Python scripts for segmentation, manual correction, and temporal linking, the pipeline provides objective and quantitative data over time. The protocol covers assay setup and image segmentation and outlines key considerations, providing a comprehensive guide for setting up and analyzing detached leaf assays. The very minimal material requirements and user-friendly software make this protocol accessible for all Phytophthora researchers.

Design and Development of Decision Support System Software for Digital Record Creation and Reporting in Transfer Centers

Within the scope of the logistics sector, records kept manually or via Excel for situations such as vehicles, transported goods, cargo and barcodes that do not comply with the procedure in transfer centers cause data loss and reporting difficulties and reduce operational efficiency. This is one of the biggest problems facing the industry. This brief paper discusses the design and development of software that provides digital record creation, storage and reporting in order to prevent this. The digital minutes system developed within the scope of the project automates the functions of vectorizing and classifying minute texts by using text mining and machine learning algorithms. The artificial intelligence-supported classification model was evaluated with accuracy, F1 Score, Sensitivity and Sensitivity metrics, and the system aims to provide a user-friendly decision support software by making fast and accurate classification. The digital record system improves cost and risk management by providing early detection of operational errors, instant reporting and retrospective analysis.

Design and Implementation of a Rechargeable Active/Passive RFID Tag System with Integrated Software Interface for Construction Site Equipment Tracking

Radio Frequency Identification (RFID) technology has undergone continuous development since its origin in the 1940s, finding applications across various fields. This research aimed to design and implement a rechargeable active/passive RFID Tag system operating at 433MHz, with a specific focus on its application in construction site equipment tracking. The objectives were to develop an RFID Tag capable of switching between active and passive operation modes, wirelessly recharging the battery system, and demonstrating its effectiveness in managing construction equipment. The designed Tag operates in both active and passive modes, ensuring operation even after battery depletion. Data exchange uses backscattering modulation, allowing transmission without relying on an external power supply. The Tag was implemented using an ESP8266-01 encoder, envelope detection circuit for mode switching, signal amplification unit, impedance-matched antenna, and a rechargeable power supply unit. Testing evaluated data transmission, reception, signal reliability, and distance coverage in both modes. Results showed effective performance up to 50 meters in active mode and 30 meters in passive mode. The system's application in construction site equipment tracking demonstrated significant potential for improving asset utilization, enhancing security, and streamlining inventory management. Additionally, a software interface was developed and tested, showcasing the system's ability to present real-time tracking data in both tabular and graphical formats, further enhancing its practical applicability. This research contributes to advancing RFID technology by introducing a versatile, rechargeable Tag system with practical applications in challenging environments like construction sites, complemented by user-friendly software solutions for effective asset management

Genomic analysis made easy (Game V1): An automated software for plant genome assembly and annotation from illumina sequencing

The recent development and affordable accessibility of the next-generation high-throughput sequencing technology and artificial intelligence have propelled more researchers to get involved in genomics and to the threshold of a new beginning in understanding, utilizing, and conserving the biodiversity. However, one of the biggest challenges for the analysis of high-throughput sequencing reads is the whole genome assembly and annotation. Availability of user-friendly free software that manages all types of sequenced DNA to be used in a local environment is lacking. Hence, the Genomic Analysis Made Easy (GAME v1) software has been developed using Python to provide a user-friendly, fast, free, and automated GUI-based solution for plant genome assembly and annotation. The software performs on a Linux-based operating environment with a minimum of 16 GB RAM and 100 GB disc space, fully automated from the installation to execution, thus requiring minimal bioinformatics expertise for the execution. The GAME v1 generates detailed quality reports of the raw reads, GenomeScope heterozygosity report, QUAST contigs and scaffolds results, BUSCO summary plot, COG functional annotation chart, GO chart, NCBI and UniProt annotations, KEGG pathway distribution graph, and RepeatMasker plots. The nuclear genome of Chenopodium pallidicaule retrieved from NCBI was assembled and annotated successfully using GAME v1, revealing preliminary genome size estimate of around 419.54 Mb based on GenomeScope analysis prior to assembly. The final assembly, as assessed by QUAST, unveiled a total length of 285.85 Mb (0.29 Gb) containing 23,806 genes. This automated solution will facilitate plant genomics research by revealing the underlying insights of draft nuclear genomes. The software is available at https://arraygen.com/game Bangladesh J. Plant Taxon. 31(2): 225-238, 2024 (December)

Development and Performance Evaluation of an Instrumented Hybrid Onion Storage Facility

Onion (Allium cepa L.) is one of the oldest bulb crops, known to mankind and consumed worldwide. It is valued for its distinct pungent flavour and is an essential ingredient for the cuisine of many regions. This study aimed to develop and evaluate the performance of a refrigerated onion storage facility. The designed facility was simple, efficient, and cost-effective, with a focus on user-friendly operation and packaging of onions in zippered bags. The experiment involved slicing and dicing onions of equal size, which were then placed in zip bags and monitored over a week. The mass of the samples was measured daily to observe the changes. Additionally, laboratory analysis was conducted to assess the proximate composition, phytochemical contents, and heavy metal concentration of the onions. The results showed that the storage facility maintained a cooling rate of 0oC and experienced fluctuations in temperature due to external weather conditions. The relative humidity ranged from 18% to 19%, indicating the coldness of the storage facility. The analysis of the onion samples revealed high moisture content, with values ranging from 80% to 90%. Other compositional parameters such as crude protein, CHO, crude fibre, fat content, and ash content were relatively low. In terms of phytochemical analysis, flavonoid content was found to be relatively high compared to saponin, alkaloid, and tannin contents. Flavonoid content was highest in the raw state of the onion. Heavy metal analysis showed significant differences in the concentrations of iron (Fe), manganese (Mn), and zinc (Zn), while copper (Cu) did not exhibit a significant effect. The developed refrigerated onion storage facility demonstrated promising performance. The proximate composition analysis indicated that before and after storage of the onions, the moisture content and nutrients of onions was maintained. The phytochemical analysis revealed a significant presence of flavonoids, while heavy metal analysis showed varying concentrations of Fe, Mn, Zn, and Cu.

Comparative Analysis of Symmetry Parameters in the E2 Inner Core of the Pyruvate Dehydrogenase Complex.

Recent advances in cryo-electron microscopy (cryo-EM) have facilitated the high-resolution structural determination of macromolecular complexes in their native states, providing valuable insights into their dynamic behaviors. However, insufficient understanding or experience with the cryo-EM image processing parameters can result in the loss of biological meaning. In this paper, we investigate the dihydrolipoyl acetyltransferase (E2) inner core complex of the pyruvate dehydrogenase complex (PDC) and reconstruct the 3D maps using five different symmetry parameters. The results demonstrate that the reconstructions yield structurally identical 3D models even at a near-atomic structure. This finding underscores a crucial message for researchers engaging in single-particle analysis (SPA) with relatively user-friendly and convenient image processing software. This approach helps reduce the risk of missing critical biological details, such as the dynamic properties of macromolecules.

ChemNTP: Advanced Prediction of Neurotoxicity Targets for Environmental Chemicals Using a Siamese Neural Network.

Environmental chemicals can enter the human body through various exposure pathways, potentially leading to neurotoxic effects that pose significant health risks. Many such chemicals have been identified as neurotoxic, but the molecular mechanisms underlying their toxicity, including specific binding targets, remain unclear. To address this, we developed ChemNTP, a predictive model for identifying neurotoxicity targets of environmental chemicals. ChemNTP integrates a comprehensive representation of chemical structures and biological targets, improving upon traditional methods that are limited to single targets and mechanisms. By leveraging these structural representations, ChemNTP enables rapid screening across 199 potential neurotoxic targets or key molecular initiating events (MIEs). The model demonstrates robust predictive performance, achieving an area under the receiver operating characteristic curve (AUCROC) of 0.923 on the test set. Additionally, ChemNTP's attention mechanism highlights critical residues in binding targets and key functional groups or atoms in molecules, offering insights into the structural basis of interactions. Experimental validation through in vitro enzyme activity assays and molecular docking confirmed the binding of eight polybrominated diphenyl ethers (PBDEs) to acetylcholinesterase (AChE). We also provide a user-friendly software interface to facilitate the rapid identification of neurotoxicity targets for emerging environmental pollutants, with potential applications in studying MIEs for more types of toxicity.

Comprehensive Smart Mess Management Web Platform

The Mess Management System is an innovative software solution designed to automate the manual process of managing mess-related activities such, inventory management, and billing. The system is developed using PHP as the programming language and MySQL as the database management system. The primary objective of this system is to reduce the workload on mess staff, increase efficiency, and reduce errors in the mess management process. This research paper focuses on the design and implementation of the Mess Management System and its impact on college hostel mess management. This is a cost- effective, efficient, and user-friendly software solution that automates the manual mess management process. The system can be customized to cater to the specific needs of different institutions and can significantly reduce the workload on mess staff. The system's scalability, flexibility, and ease of use make it a promising solution for college hostel mess management. However, further research can be carried out to address the system's limitations, such as the need for regular maintenance and the requirement of technical expertise.

Implementing Steel Design in Educational Software: The Linear Elements Structure Model Program

The Linear Elements Structure Model, or LESM for short, is a user-friendly, free-to-use, and open-source educational software for the structural analysis of models composed of linear elements. Aimed at aiding students, teachers, engineers, and architects to understand the behavior of structures better and improve their workflow, its intuitive and straightforward GUI (Graphical User Interface) allows a simple yet effective approach to modeling, running calculations of linear-elastic static and dynamic effects, and visualizing the internal stresses and strains in two-dimensional and three-dimensional trusses and frames. This paper expands upon the capabilities of LESM by detailing the implementation of steel design for bidimensional frames in compliance with the Brazilian steel structure design code, NBR8800:2008, within the current version of the software, building upon previous efforts to enhance LESM's functionality through object-oriented programming in the MATLAB environment. With the feature of steel design initially conceived as an extension for a prior version of the software, the LESM program has since been updated and reworked with more straightforward and quicker user interaction, a more optimized source code, and the addition of new features. By comparing past and current versions of the software and examining the source code of the steel design extension, it was possible to successfully integrate the steel design feature into the latest iteration of LESM, broadening the program’s scope of use, both for the academic and industrial sectors in the field of analysis and design.

An Ecologist-Friendly R Workflow for Expediting Species-Level Classification of Camera Trap Images.

Camera trapping has become increasingly common in ecological studies, but is hindered by analyzing large datasets. Recently, artificial intelligence (deep learning models in particular) has emerged as a promising solution. However, applying deep learning for images processing is complex and often requires programming skills in Python, reducing its accessibility. Some authors addressed this issue with user-friendly software, and a further progress was the transposition of deep learning to R, a statistical language frequently used by ecologists, enhancing flexibility and customization of deep learning models without advanced computer expertise. We aimed to develop a user-friendly workflow based on R scripts to streamline the entire process, from selecting to classifying camera trap images. Our workflow integrates the MegaDetector object detector for labelling images and custom training of the state-of-the-art YOLOv8 model, together with potential for offline image augmentation to manage imbalanced datasets. Inference results are stored in a database compatible with Timelapse for quality checking of model predictions. We tested our workflow on images collected within a project targeting medium and large mammals of Central Italy, and obtained an overall precision of 0.962, a recall of 0.945, and a mean average precision of 0.913 for a training set of only 1000 pictures per species. Furthermore, the custom model achieved 91.8% of correct species-level classifications on a set of unclassified images, reaching 97.1% for those classified with > 90% confidence. YOLO, a fast and light deep learning architecture, enables application of the workflow even on resource-limited machines, and integration with image augmentation makes it useful even during early stages of data collection. All R scripts and pretrained models are available to enable adaptation of the workflow to other contexts, plus further development.

Optimal Representative Strain selector-a comprehensive pipeline for selecting next-generation reference strains of bacterial species.

Although it is common practice to use historically established 'reference strains' or 'type strains' for laboratory experiments, this approach often overlooks how effectively these strains represent the full ecological, genetic and functional diversity of the species within a specific ecological niche. In this context, this study proposes the Optimal Representative Strain (ORS) selector tool (https://zenodo.org/doi/10.5281/zenodo.13772191), an innovative bioinformatic pipeline capable of evaluating how a strain represents its whole species from a genetic and functional perspective, in addition to considering its ecological distribution in a particular ecological niche. Based on publicly available genomes, the strain that best fits all these three microbiological aspects is designated as anoptimal representative strain. Moreover, a user-friendly software called Local Alternative Optimal Representative Strain selector was developed to allow researchers to screen their local library of bacterial strains for an optimal available alternative based on the reference optimal representative strain. Five different bacterial species, i.e. Lacticaseibacillus paracasei, Lactobacillus delbrueckii, Streptococcus thermophilus, Bacteroides thetaiotaomicronand Lactococcus lactis, were tested in three different environments to evaluate the performance of the bioinformatic pipeline in selecting optimal representative strains.

Re-identification of anonymised MRI head images with publicly available software: investigation of the current risk to patient privacy

Facial recognition software (FRS) has historically been perceived as lacking the capability to identify individuals from cross-sectional medical images. Utilising such data for identification purposes was considered infeasible due to the substantial computational power and specialised technical expertise it would require. However, recent advancements in accessible artificial intelligence-based (AI-based) software and open-source tools have made these applications widely available and easy to use, raising new privacy concerns. This proof-of-concept was designed as a cross-sectional study and included participants with a verified online presence. Standard magnetic resonance imaging (MRI) head scans were performed on these participants, from which three-dimensional rendering (3DR) images were created using free and publicly available software. These images were used for face searches by free and publicly available FRS. Different head orientations and hairstyles were applied to the 3DR images to assess whether non-facial features influenced the FRS results. All results were obtained between the 10th of February 2024 and the 1st of March2024. Face searches of 3DR images in a database containing over 800 million images from the World Wide Web (WWW) yielded correct matches for 50% of the participants in less than 10min. The user-friendly software required minimal computational knowledge or resources, making this process broadly accessible. Modifying elements such as hairstyles or the orientation of the 3DR to better resemble actual photographs of the participants improved FRS matches. Current existing FRS can swiftly and accurately identify individuals from MRI head scans. This poses a significant privacy risk for participants in enrolled clinical trials and highlights the urgent need for improved data protection measures and increased sensitivity to ensure participant confidentiality. There was no funding source for this study.

Benchmarking recent computational tools for DNA-binding protein identification.

Identification of DNA-binding proteins (DBPs) is a crucial task in genome annotation, as it aids in understanding gene regulation, DNA replication, transcriptional control, and various cellular processes. In this paper, we conduct an unbiased benchmarking of 11 state-of-the-art computational tools as well as traditional tools such as ScanProsite, BLAST, and HMMER for identifying DBPs. We highlight the data leakage issue in conventional datasets leading to inflated performance. We introduce new evaluation datasets to support further development. Through a comprehensive evaluation pipeline, we identify potential limitations in models, feature extraction techniques, and training methods, and recommend solutions regarding these issues. We show that combining the predictions of the two best computational tools with BLAST-based prediction significantly enhances DBP identification capability. We provide this consensus method as user-friendly software. The datasets and software are available at https://github.com/Rafeed-bot/DNA_BP_Benchmarking.

Application of Mathematical Methods in the Analysis of Martial Arts Movements: A Case Study of the 720-Degree Aerial Lotus Kick Combined with the Horse Stance

This study investigates the application of mathematical methods in the analysis of the 720-degree aerial lotus kick combined with the horse stance, a complex technique in martial arts. Through kinematic and dynamic analyses, as well as numerical simulations, the biomechanical principles and optimization strategies for this movement are examined. The research findings highlight the critical roles of takeoff velocity, rotational velocity, and landing technique in the successful execution of the technique. The study also emphasizes the importance of progressive and systematic training programs, appropriate protective measures, and regular monitoring and assessment for safe and effective practice. The integration of applied mathematics in martial arts movement analysis is shown to provide valuable insights into technique optimization and injury prevention, offering a quantitative foundation for advanced training methodologies. However, the limitations of mathematical modeling, such as simplification and data quality, are acknowledged. Future research directions are proposed, including the extension of the mathematical framework to other complex techniques, the integration of advanced technologies, and the development of user-friendly software tools. The study concludes that the application of mathematical methods in martial arts biomechanics has the potential to drive innovation, inform evidence-based practice, and shape the future of martial arts research and training.

LIDeepDet: Deepfake Detection via Image Decomposition and Advanced Lighting Information Analysis

The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection.