Open Source Software Tools Research Articles

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles.

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.

Using Facebook to Recruit Urban Participants for Smartphone-Based Travel Surveys

Social media has become an integral part of everyday life for many individuals, serving as a platform to express opinions, share memories and lifestyles, follow news, and adapt to social trends and norms. The wealth of user information and analytics on these platforms has facilitated the development and sale of tailored products and services, benefiting advertisers and researchers seeking survey participants. Social media advertising has demonstrated its effectiveness in reaching hard-to-reach populations. However, transport researchers have yet to capitalise on this potential fully. This paper presents our experience using social media to recruit participants for two smartphone travel surveys conducted in Australia. We demonstrate that social media recruitment and smartphone-based travel surveys are highly effective, adaptable, and can be rapidly deployed in response to research opportunities, such as during the early phase of the COVID-19 pandemic when traditional methods may be less suitable. This approach also holds great potential for travel surveys targeting the general population. This paper shares several lessons from this experiment, including our administrative approach and detailed technical instructions to utilise open-source software tools for conducting smartphone travel surveys like ours. This approach significantly reduces study costs compared to most commercial solutions.

“Small is beautiful” – Examining reliable determination of low-abundant therapeutic antibody glycovariants

Glycans associated with biopharmaceutical drugs play crucial roles in drug safety and efficacy, and therefore, their reliable detection and quantification is essential. Our study introduces a multi-level quantification approach for glycosylation analysis in monoclonal antibodies (mAbs), focusing on minor abundant glycovariants. Mass spectrometric data is evaluated mainly employing open-source software tools. Released N-glycan and glycopeptide data form the basis for integrating information across different structural levels up to intact glycoproteins. Comprehensive comparison showed that indeed, variations across structural levels were observed especially for minor abundant species. Utilizing modification finder (MoFi), a tool for annotating mass spectra of intact proteins, we quantify isobaric glycosylation variants at the intact protein level. Our workflow's utility is demonstrated on NISTmAb, rituximab and adalimumab, profiling their minor abundant variants for the first time across diverse structural levels. This study enhances understanding and accessibility in glycosylation analysis, spotlighting minor abundant glycovariants in therapeutic antibodies.

Simulation model of tumor-treating fields

Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm 3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.

Modeling Radioactive Decay Series Phenomenon Involving First-Order Differential Equation and Visualizing Solutions Using Open-Source Software XCOS

Numerous physical and chemical processes can be accurately described by first-order differential equations and can be solved through analytical methods. The solutions to these equations necessitate the specification of initial or boundary conditions. In the realm of physics and chemistry education, the ability to visualize the solutions of differential equations and comprehend their implications for physical/chemical changes holds significant importance. XCOS, an open-source software tool, serves as a valuable resource for both solving differential equations and graphically representing their solutions. Furthermore, XCOS aids in tackling nonlinear differential equations for which analytical solutions are often elusive. This paper delves into radioactive decay and decay series reactions by first-order differential equations. The paper explores the successful resolution and visualization of the phenomenon involving differential equations through XCOS. This approach extends to the manipulation of various parameters within the equations, further fostering inquiry-based learning.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Low-cost desktop learning factory to support the teaching of artificial intelligence

The following document details low-cost hardware and open-source available software tools that can be combined to support active teaching methodologies like Problem-Based Learning (PBL) and incorporate work-oriented technological skills in students. This proposal presents a prototype of Open Educational Resources (OER) that integrates software and hardware tools for the specific purpose of facilitating instruction in Artificial Intelligence. The hardware consists of affordable electronic devices, including an Arduino board, servo motors, sensors, a relay and a motor, all integrated into a scaled conveyor belt. On the other hand, open software was used to implement an image classification program with different features (shape, color, size, among others). The exact construction steps, circuits, and code are presented in detail and should encourage other scientists to replicate the experimental setup, especially if they are looking for experimental teaching of artificial intelligence, since the system allows object classification using the machine learning paradigm to facilitate the teaching of artificial intelligence concepts with computer vision concepts.

MTVE: Magdeburg tool for video experiments

AbstractMTVE is an open-source software tool (citeware) that can be applied in laboratory and online experiments to implement video communication. The tool enables researchers to gather video data from these experiments in a way that these videos can be later used for automatic analysis through machine learning techniques. The browser-based tool comes with an easy user interface and can be easily integrated into z-Tree, oTree (and other experimental or survey tools). It provides the experimenters control over several communication parameters (e.g., number of participants, resolution), produces high-quality video data, and circumvents the Cocktail Party Problem (i.e., the problem of separating speakers solely based on audio input) by producing separate files. Using some of the recommended Voice-to-Text AI, the experimenters can transcribe individual files. MTVE can merge these individual transcriptions into one conversation.

Declutter the MRI protocol tree: Managing and comparing sequence parameters of multiple clinical Siemens MRI systems

An MRI protocol tree on a clinical MRI system is a large database containing hundreds of protocols, each containing multiple sequences, and up to 900 parameters per sequence. Protocol variation between scan sessions or patients must be avoided as much as possible, as it may lead to financial loss and less than optimal outcomes for the patient. Without proper management, protocol variation and errors in MRI protocol trees are easily introduced and may remain undetected, leading to a cluttered protocol tree. This in turn reduces the efficiency of the radiological MRI workflow. We introduce a method and open-source software tools for managing MRI protocols on a sequence parameter level, which can detect deviations and variations in the protocol tree. It can be used offline, away from the scanner console, without disturbing the clinical workflow. These tools help to create a standardized protocol library across multiple MRI scanners, reducing variation and errors, enabling radiology departments to create optimal value for the patient and institution.

COSAP: Comparative Sequencing Analysis Platform

BackgroundRecent improvements in sequencing technologies enabled detailed profiling of genomic features. These technologies mostly rely on short reads which are merged and compared to reference genome for variant identification. These operations should be done with computers due to the size and complexity of the data. The need for analysis software resulted in many programs for mapping, variant calling and annotation steps. Currently, most programs are either expensive enterprise software with proprietary code which makes access and verification very difficult or open-access programs that are mostly based on command-line operations without user interfaces and extensive documentation. Moreover, a high level of disagreement is observed among popular mapping and variant calling algorithms in multiple studies, which makes relying on a single algorithm unreliable. User-friendly open-source software tools that offer comparative analysis are an important need considering the growth of sequencing technologies.ResultsHere, we propose Comparative Sequencing Analysis Platform (COSAP), an open-source platform that provides popular sequencing algorithms for SNV, indel, structural variant calling, copy number variation, microsatellite instability and fusion analysis and their annotations. COSAP is packed with a fully functional user-friendly web interface and a backend server which allows full independent deployment for both individual and institutional scales. COSAP is developed as a workflow management system and designed to enhance cooperation among scientists with different backgrounds. It is publicly available at https://cosap.bio and https://github.com/MBaysanLab/cosap/. The source code of the frontend and backend services can be found at https://github.com/MBaysanLab/cosap-webapi/ and https://github.com/MBaysanLab/cosap_frontend/ respectively. All services are packed as Docker containers as well. Pipelines that combine algorithms can be customized and new algorithms can be added with minimal coding through modular structure.ConclusionsCOSAP simplifies and speeds up the process of DNA sequencing analyses providing commonly used algorithms for SNV, indel, structural variant calling, copy number variation, microsatellite instability and fusion analysis as well as their annotations. COSAP is packed with a fully functional user-friendly web interface and a backend server which allows full independent deployment for both individual and institutional scales. Standardized implementations of popular algorithms in a modular platform make comparisons much easier to assess the impact of alternative pipelines which is crucial in establishing reproducibility of sequencing analyses.

Esra (elevator simulation, research & analysis): an open-source software tool for elevator traffic simulation, research, and analysis

ABSTRACT Today, although elevator companies typically have their own elevator simulation and analysis tools, these remain inaccessible to the public due to commercial concerns and rivalry in the sector. Consequently, due to the lack of modular open-source software tools, interest in the design and development of elevator systems in academia is quite limited, contrary to the vast potential of the field. This paper addresses this gap introducing an open-source software tool, the Elevator Simulator for Research and Analysis (ESRA), developed using the MATLAB programming language and an object-oriented approach. ESRA features built-in control methods, and visualization options with the capability to save and reuse traffic data, facilitating in-depth comparative analysis. Moreover, its modular architecture allows a design and development basis for further research in the field. The proposed software aims to enable developers to design, implement, analyse, and compare their control algorithms with other methods.

CLAUDIO: automated structural analysis of cross-linking data.

Cross-linking mass spectrometry has made remarkable advancements in the high-throughput characterization of protein structures and interactions. The resulting pairs of cross-linked peptides typically require geometric assessment and validation, given the availability of their corresponding structures. CLAUDIO (Cross-linking Analysis Using Distances and Overlaps) is an open-source software tool designed for the automated analysis and validation of different varieties of large-scale cross-linking experiments. Many of the otherwise manual processes for structural validation (i.e. structure retrieval and mapping) are performed fully automatically to simplify and accelerate the data interpretation process. In addition, CLAUDIO has the ability to remap intra-protein links as inter-protein links and discover evidence for homo-multimers. CLAUDIO is available as open-source software under the MIT license at https://github.com/KohlbacherLab/CLAUDIO.

Automated Wi-Fi intrusion detection tool on 802.11 networks

Wi-Fi networks enable user-friendly network connectivity in various environments, ranging from home to enterprise networks. However, vulnerabilities in Wi-Fi implementations may allow nearby adversaries to gain an initial foothold into a network, e.g., in order to attempt further network penetration. In this paper we propose a methodology for the detection of attacks originating from Wi-Fi networks, along with a Wi-Fi Network Intrusion Detection (Wi-Fi-NID) tool, developed to automate the detection of such attacks at 802.11 networks. In particular, Wi-Fi-NID has the ability to detect and trace possible illegal network scanning attacks, which originate from attacks at the Wi-Fi access layer. We extend our initial implementation to increase the efficiency of detection, based on mathematical and statistical function techniques. A penetration testing methodology is defined, in order to discover the environmental security characteristics, related with the current configuration of the devices connected to the 802.11 network. The methodology covers known Wi-Fi attacks such as de-authentication attacks, capturing and cracking WPA-WPA/2 handshake, captive portal and WPA attacks, mostly based on various open source software tools, custom tools, as well as on specialized hardware.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

POINT CLOUD SEGMENTATION IN HERITAGE PRESERVATION. ADVANCED DIGITAL PROCESS FOR HISTORICAL HOUSES

Abstract. This paper discusses the use of point cloud segmentation, both automatic and semi-automatic, in heritage conservation processes, with the aim of consciously integrating traditional and innovative methods. Thus, the research explores the potential of open-source software tools for semi-automatic point-cloud segmentation in enriching heritage knowledge and contributing to the inspection of its state of conservation. This ensures a 3D geometric view of reality and enables the identification of criticalities that may not be visible to the naked eye through colorimetric attributes. The study was conducted on Villa Leonardi, a historical house located in the city of Treia in the Marche Region (Italy) as a pilot test. The investigation focused on the east and south elevations of the building, which were suitable for two distinct trials: a semi-automatic procedure for analyzing the quality of mortar joints in an exposed masonry, and a semi-automatic procedure for mapping alterations and decay in a plastered masonry. To validate the results, manual and semi-automatic maps were compared using both qualitative and quantitative assessments. Qualitative assessment involved overlay and visual analysis, while quantitative assessment involved transforming the segmented point cloud into a mesh and calculating the resulting surface area. The critical interpretation of the two trials revealed both advantages and disadvantages; overall, experimentation highlighted the added value that advanced digital process can bring to the issue of heritage preservation.

Using Machine Learning to Predict Oil–Mineral Aggregates Formation

The formation of oil–mineral aggregates (OMAs) is essential for understanding the behavior of oil spills in estuaries and coastal waters. We utilized statistical methods (screening design) to identify the most influential variables (seven factors in total) during OMA formation. Time was the most important factor, followed by temperature and oil/clay ratio. Moreover, machine learning was applied to predict the OMA median diameter (D50). Among the three tested algorithms, the Random Forest (RF) algorithm showed the highest accuracy, with a training R2 of 0.99 and testing R2 of 0.93. An open-source software tool that integrates the RF algorithm was developed, allowing users to easily estimate the OMA D50 based on input variables. The valuable results and the practical tool we have developed enhance the understanding and management of environmental impacts associated with oil spills.

MV-Checker: A software tool for multi-valued model checking intelligent applications with trust and commitment

Intelligent applications are highly susceptible to uncertainty and inconsistency due to the intense and intricate interactions among their autonomous components (or agents), making their verification theoretically and practically challenging. This paper presents the design and implementation of a new open-source and scalable software tool for modeling and verifying intelligent applications with commitment and trust protocols under both uncertainty and inconsistency settings, using reduction-based multi-valued model checking techniques. The proposed tool is equipped with original and novel algorithms that transform our logics of multi-valued commitment (mv-CTLC) and multi-value trust (mv-TCTL) that we recently introduced to their classical two-valued commitment (CTLC) and trust (TCTL) logic versions as well as to Computational Tree Logic (CTL). Moreover, the tool transforms the mv-CTL to CTL, and it is applicable for the classical model checking by transforming the classical logics of trust and commitment to CTL. To demonstrate the practicality and applicability of the proposed tool in real settings, we present and report experimental results over two blockchain-based applications in the healthcare domain. Finally, we provide discussions and comparisons between the proposed approaches regarding scalability and efficiency. Moreover, we provide packages of more than 11 experiments, including the ones we conduct in this paper and enhanced experiments from previous works. Our findings ensure that the proposed approaches and the software tool that implements them are highly efficient and scalable, giving accurate results under varying conditions.

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

Mapping the Intellectual Structure of e-Learning Tools Research: A Bibliometric Approach

E-learning was derived as a result of the emergence of internet and the electronic devices such as computers after the technical revolution. The pandemic in 2019 marked a significant shift in the adoption and evolution of learning tools. Within this context, this research aims to assess and comprehensively review the scientific production in e-learning tools along with the time using academic publications as the key data source. The Biblioshiny package in R opensource software tool was used for statistical computing and modeling the data. The Scopus database, which is used as a citation index with a multidisciplinary scope is used as the source of a bibliographic database. The research examines 1,164 articles published in the database (Date acquisition: 05th March 2023) between 2000-2022. Articles published in English were refined after excluding the articles unpublished yet. Annual scientific production on research on e-learning tools started to grow in number after 2008 and reached its peak in 2021. The average citation number per document is 8.2 while the annual growth rate is 22%. The literature covers 102 countries. Spain, USA and Germany became the most productive countries while the most cited country is the USA. Along with the time the trending topics were diversified and the themes became more complex. Computer-aided instruction has become the most frequent word along the time. Finally, this study's implications highlight that e-learning tools have been impacted globally.