Open-source Tool Research Articles

A quantitative image analysis platform for assessing trophoblast differentiation

Immunofluorescence microscopy is extensively used in characterization of trophoblast differentiation in vitro. However, such data is primarily used to confirm the presence of protein markers or qualitatively compare levels of protein markers across experimental conditions. Imaging data, when processed and analyzed appropriately can provide quantitative and spatial information, and provide biological insight. Towards this end, here we present MATroph, an open-source MATLAB-based computational tool to process images generated by immunofluorescent microscopy. MATroph automatically executes a series of image processing operations, including the classification of red, blue, and green channels from images, background extraction, morphological operations, and image filtering. From the isolated blue channels corresponding to nuclear staining, this tool generates numerical values for cell number. Additionally, relative levels and spatial location of proteins are obtained by mapping red and green channel pixels to blue pixels by assigning minimum pixel distance between the blue and other color objects. Thus, this tool provides information about intracellular protein accumulation areas. Additionally, this tool can also classify cells as single cells or part of colonies, and extract information on protein levels for each; this is particularly useful for quantitative studies on extravillous trophoblast maturation. We provide a user-guide to analyze the relative levels of markers relevant to human trophoblast stem cell self-renewal and differentiation. Importantly, MATroph is composed of a simple MATLAB algorithm, and its implementation requires minimal expertise in programming.

Cardiac Substructure Dose and Survival in Stereotactic Radiotherapy for Lung Cancer: Results of the Multi-Centre SSBROC Trial

Background and PurposeStereotactic ablative body radiotherapy (SABR) is increasingly used for early-stage lung cancer, however the impact of dose to the heart and cardiac substructures remains largely unknown. The study investigated doses received by cardiac substructures in SABR patients and impact on survival. Materials and MethodsSSBROC is an Australian multi-centre phase II prospective study of SABR for stage I non-small cell lung cancer. Patients were treated between 2013 and 2019 across 9 centres. In this secondary analysis of the dataset, a previously published and locally developed open-source hybrid deep learning cardiac substructure automatic segmentation tool was deployed on the planning CTs of 117 trial patients. Physical doses to 18 cardiac structures and EQD2 converted doses (α/β = 3) were calculated. Endpoints evaluated include pericardial effusion and overall survival. Associations between cardiac doses and survival were analysed with the Kaplan–Meier method and Cox proportional hazards models. ResultsCardiac structures that received the highest physical mean doses were superior vena cava (22.5 Gy) and sinoatrial node (18.3 Gy). The highest physical maximum dose was received by the heart (51.7 Gy) and right atrium (45.3 Gy). Three patients developed grade 2, and one grade 3 pericardial effusion. The cohort receiving higher than median mean heart dose (MHD) had poorer survival compared to those who received below median MHD (p = 0.00004). On multivariable Cox analysis, male gender and maximum dose to ascending aorta were significant for worse survival. ConclusionsPatients treated with lung SABR may receive high doses to cardiac substructures. Dichotomising the patients according to median mean heart dose showed a clear difference in survival. On multivariable analyses gender and dose to ascending aorta were significant for survival, however cardiac substructure dosimetry and outcomes should be further explored in larger studies.

Open Archaeology, Open Source? Collaborative practices in an emerging community of archaeological software engineers

In this article, we investigate modes of collaboration in this emerging community of practice using 'open-archaeo ', a curated list of archaeological software, and data on the activity of associated GitHub repositories and users. We conduct an exploratory quantitative analysis to characterise the nature and intensity of these collaborations and map the collaborative networks that emerge from them. We document uneven adoption of open source collaborative practices beyond the basic use of git as a version control system and GitHub to host source code. Most projects do make use of collaborative features and, through shared contributions, we can trace a collaborative network that includes the majority of archaeologists active on GitHub. However, a majority of repositories have 1-3 contributors, with only a few projects distinguished by an active and diverse developer base. Direct collaboration on code or other repository content - as opposed to the more passive, social media-style interaction that GitHub supports – remains very limited. In other words, there is little evidence that archaeologists ' adoption of open-source tools (git and GitHub) has been accompanied by the decentralised, participatory forms of collaboration that characterise other open-source communities. On the contrary, our results indicate that research software engineering in archaeology remains largely embedded in conventional professional norms and organisational structures of academia.

Hit and run: Forensic vehicle event reconstruction through driver-based cloud data from Progressive's snapshot application

Driving Insurance Applications (DIAs) have emerged as a valuable resource in the ever-evolving digital landscape. Automobile owners are storing extensive data on driving behaviors and patterns. This study pioneers the forensic analysis of Progressive's Snapshot application, focusing on the extraction and potential forensic use of data that remains inaccessible through the mobile application's interface. In our approach we focused on four research questions: How accurate is location and speed data collected by Progressive Snapshot?, What forensically relevant data can we extract from the Progressive Cloud that is unavailable to the user from the mobile application interface?, Can we employ anti-forensics techniques, specifically fake location data, to create false trip details?, Can we reconstruct a hit-and-run scenario from trip event details? To answer these questions, we developed PyShot, a Python-based open-source tool, to extract data from the Progressive cloud. Our tests confirmed Snapshot's accuracy in recording speed and location. Despite efforts to fake the Global Positioning System (GPS) location, the cloud still maintained accurate records. PyShot revealed more detailed driving data, like dangerous maneuvers and distracted driving, compared to the mobile application. This study also explores the forensic reconstruction of hit-and-run incidents, using a mannequin and focusing on Progressive's server data. Analyzing event categories, geographical coordinates, and timestamps provides insights into the capabilities and constraints of this application in forensic investigations. The findings offer valuable insights into the forensic capability of data retained by DIAs, contributing to their potential use in forensic investigations.

Проектування цифро-аналогових перетворювачів сигналів для сенсорних мікросистем на кристалі

The results of computer simulation of the developed R-2R digital-to-analogue converter design are given. Results with output voltage values for different digital input code are also given. The topology of such device is proposed, and its operation is modulated. Thus, the MicroWind software application was used to modelling and develop the operation of the digital signal to analogue converter, as it is an open-source software tool for all users, which allows the design and simulation of an integrated circuit at the physical description (IC) level.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Automated Geospatial Approach for Assessing SDG Indicator 11.3.1: A Multi-Level Evaluation of Urban Land Use Expansion across Africa

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia’s urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries.

Website Penetration Analysis Against XSS Attacks using Payload Method

This research aims to analyze the effectiveness of various penetration testing methods in identifying and mitigating XSS (Cross-Site Scripting) vulnerabilities in web applications. XSS is a type of web security attack that takes advantage of weaknesses in web applications to insert malicious code into web pages displayed to users. This attack can steal user data, take over user sessions, or spread malware. This research uses a penetration testing method with a black-box approach, where the researcher does not know the construction of the system being tested. Tests were conducted on 10 random websites, including 5 open-source websites and 5 commercial websites. The test results show that the payload method used is effective in exploiting XSS vulnerabilities on some websites. Of the 10 websites tested, 6 of them were successfully exploited using different payload methods. This research highlights the importance of using open-source penetration testing tools in detecting and addressing security vulnerabilities in web applications. These tools are easy to implement, supported by extensive documentation, and have a strong community. This research also emphasizes the importance of a deep understanding of how penetration testing tools work to identify and address security vulnerabilities. To address XSS vulnerabilities, this research recommends good programming techniques such as programming language updates, use of OOP (Object-Oriented Programming), MVC (Model-View-Controller) concepts, and use of frameworks. Further research can be done to develop and test new payload methods, explore the use of other penetration testing tools, and test security vulnerabilities in other types of web applications.

Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review.

Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.

LExCI: A framework for reinforcement learning with embedded systems

Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system.

Maize plant growth period identification based on MobileNet and design of growth control system

To address the current inefficiencies and subjective nature of manual observation in maize cultivation, with the aim of achieving high efficiency and productivity, this study focused on the DeMaya D3 maize variety. It proposes a maize growth stage recognition method based on the MobileNet model, which is a lightweight convolutional neural network architecture. The method was tested and achieved recognition accuracies of 0.98, 0.96, 0.92, 0.85, and 0.97 for different growth stages, respectively. Additionally, a maize growth prediction model was developed. Based on data collected from experimental plots regarding maize plant height and stem diameter, the Prophet model and an optimized version of the Prophet model were used to forecast maize growth trends. The Prophet model is an open-source tool for time series forecasting. Comparative analysis was conducted between the predictions of the original Prophet model and the optimized version. The relative errors of the Prophet model predictions were 0.85%, 2.11%, and 0.79%, while those of the optimized Prophet model were 0.76%, 0.47%, and 0.71%. Compared to the Prophet model, the optimized model reduced errors by 0.09%, 1.64%, and 0.08%, respectively. The maize plant growth control system was designed to obtain the information through the collection layer. The decision-making layer judged the soil nutrient absorption and growth status. Finally, the management layer controlled water and fertilizer.

FaultQuake: An open-source Python tool for estimating Seismic Activity Rates in faults

In regions experiencing ongoing aseismic deformation, fault’s Activity Rate (AR) calculations often lead to an overestimation of hazard potential. This study proposes a novel methodology that integrates the Seismic Coupling Coefficient (SCC) into the fault Seismic Activity Rate (SAR) calculation process to discriminate seismic moment rates. We introduce FaultQuake, an open-source Python tool equipped with a Graphical User Interface (GUI), designed to implement this methodology and accurately estimate SAR for faults. These activity rates can be included in Probabilistic Seismic Hazard Assessment (PSHA) frameworks and assist in differentiating the seismic and aseismic deformation. FaultQuake also presents an innovative embedded workflow, the Optimal Value Computation Workflow (OVCW), based on Conflation of Probabilities (CoP), for calculating the Maximum Magnitude (Mmax) from the empirical relationships and the observed magnitudes (Mobs) assigned to a single fault. This enhancement improves the estimation of seismic moment rates and the SAR calculation process. FaultQuake outputs are provided in the format of OpenQuake engine input files to facilitate the PSHA process. We present a sample case study focusing on the PSHA of a region in southern Iran characterized by a substantial aseismic deformation to illustrate the practical application of FaultQuake in seismic hazard analysis. Peak Ground Acceleration (PGA) maps for 10% and 2% Probabilities of Exceedance (PoE) are plotted to compare PGAs with and without applying the FaultQuake algorithm. The results provide an enhanced view of the area’s hazard with mitigation of the overestimation, resulting in more representative hazard maps. The source codes of FaultQuake are available at the FaultQuake GitHub repository, contributing to the computer and geoscience community.

MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses.

Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.

HubEnsembles: Ensembling Methods in R.

Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. The R package hubEnsembles provides a flexible framework for ensembling various types of predictions, including point estimates and probabilistic predictions. A range of common methods for generating ensembles are supported, including weighted averages, quantile averages, and linear pools. The hubEnsembles package fits within a broader framework of open-source software and data tools called the "hubverse", which facilitates the development and management of collaborative modelling exercises.

Validation of 3D and 2D thermal and electromagnetic models for high-frequency induction heating in crystal growth processes

Induction heating is crucial to various crystal growth techniques, including the Floating Zone (FZ) method, which is used for producing high-quality single silicon crystals. In this work, 3D and 2D axisymmetric numerical models for the high-frequency induction heating in the FZ process are implemented in open-source software tools Elmer and OpenFOAM. The models are compared and validated with in-situ measurements conducted on a model experiment of the FZ process, consisting of a flat inductor that heats a tin rod. The magnetic flux density distribution around the inductor is measured using a 3D induction coil probe, and the rod’s surface temperature is measured using an infrared camera. Both numerical models were found to be in good agreement with the experimental data, with deviations mainly due to geometric approximations and simplified thermal modeling. The present measurement setup can be used as a benchmark for other numerical models of high-frequency induction processes.

Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce 'Lightning Pose', an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We released a cloud application that allows users to label data, train networks and process new videos directly from the browser.

ROSMOSE: A web-based decision support tool for the design and optimization of industrial and urban energy systems

Energy efficiency is crucial for the sustainable operation of industrial and urban sectors. However, practicing engineers seldom have access to open-source tools that can readily evaluate and compare scenarios. In this work, a novel web-based tool called ROSMOSE is developed and proposed for analyzing the energy efficiency of industrial and urban systems and comparing potential process integration options. This optimization framework computes and graphically represents the minimum energy requirements, the pinch temperatures, and the Grand and integrated Carnot composite curves of process systems. Results are used to design utility systems that meet energy demands, while minimizing a specified objective function, such as total cost or environmental impacts. The Quarto environment, in which ROSMOSE is built upon, allows the integration of various open-source software and tools, such as database handling software, process modeling suites, and optimization solvers, along with interactive data visualization and reporting tools. This paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, and cream and cheese production to demonstrate the tool features. Other integration options, such as the use of heat pumps or solar panels, and the production of soft drinks or biogas as value-added byproducts, are also proposed and evaluated. As a result, from 75 % to 90 % in fuel savings and up to 80 % CO2 emissions reduction in the dairy plant are identified by optimizing the utility systems integration. Moreover, complete electrification via heat pumps eliminates any fossil fuel needs and it results in 85 % energy savings and up to 96 % CO2 emissions reduction. The activation of renewable electricity sources, such as solar power, made plausible this fully electrified scenario. Finally, optimized waste management strategies led to in-house fuel production and net export of biogas to the grid, but they also reduce cheese yield by prioritizing the biogas production from whey upgrading process. In this way, decision-makers have access to a clear performance comparison for a list of system configurations to draw informed trade-offs. A similar approach could be adopted to aid decision-making for the design and operation of other industrial and urban energy systems using ROSMOSE.

Hyperedu online learning program for hyperspectral remote sensing: Concept, implementation and lessons learned

The increasing importance of Earth observation information for society and environment, particularly in the expanding domain of hyperspectral remote sensing, along with the significantly increasing data availability, emphasizes the demand for online education to address the growing need for expertise in this domain. We present a comprehensive review of existing online education programs on Earth observation that underline the limited availability of online resources for hyperspectral remote sensing.To respond to this demand, we introduce HYPERedu, an online education program on hyperspectral remote sensing that has been developed as part of the EnMAP satellite mission since 2019. The program’s core components, content, and participation statistics, along with insights from participant feedback, are presented. HYPERedu offers a wide range of resources, including slide collections, tutorials, video screencasts, and online courses, making them accessible to a broad audience for university teaching, training schools and individual learning. The program’s effectiveness as well as challenges and insights encountered during program development are discussed. We emphasize the importance of collaborative learning, sustainability efforts for long-term material accessibility and quality assurance, and the need to bridge the digital divide with offline options, open-source tools, and multilingual content. Effective content presentation is vital, focusing on consistent branding, concise modules, interactive features, and narrative-style videos. Additionally, we explore the future of online Earth observation learning, considering enhanced participant interaction and the integration of new learning concepts. Finally, we underscore the significance of outreach and training for satellite mission utilization and advocate for open education to promote open science, data, software, and accessibility for inclusive education.

Using Radiometric and Categorical Change to Create High-Accuracy Maps of Historical Land Cover Change in Watersheds of the Great Lakes Basin

Great Lakes Basin landscapes are undergoing rapid land cover and land use (LCLU) change. The goal for this study was to identify changes in land cover occurring in the Great Lakes Basin over three time periods to provide insights into historical land cover changes occurring on a bi-national watershed scale. To quantify potential impacts of anthropogenic changes on important yet vulnerable Great Lakes Wetland ecosystems, the historical changes in land cover over time are assessed via remote sensing. The goal is to better understand legacy effects on current conditions, including wetland gain and loss and the impacts of upland ecosystems on wetland health and water quality. Three key time periods with respect to Great Lakes water level changes and coastal wetland plant invasions were mapped using Landsat-derived land cover maps: 1985, 1995, and 2010. To address change between the three time periods of interest, we incorporate both radiometric and categorical change analysis and open-source tools available for assessing time series data including LandTrendr and TimeSync. Results include maps of annual land cover transition from 1985 to 1995 and 1995 to 2010 basin-wide and by ecoregion and an assessment of the magnitude and direction of change by land cover type. Basin-wide validated change results show approximately 776,854 ha of land changed from c.1980–1995 and approximately 998,400 ha of land changed from c.1995–2010. Both time periods displayed large net decreases in both deciduous forest and agricultural land and net increases in suburban cover. Change by ecoregion is reviewed in this study with many of the change types in central plains showing change in and out of agriculture and suburban land covers, the mixed wood plain ecoregion consisted of a mixture of agricultural, suburban, and forestry changes, and all top five change types in the mixed wood shield consisted of various stages of the forestry cycle for both time periods. In comparison with previous LCLU change studies, overall change products showed similar trends. The discussion reviews why, while most changes had accuracies better than 84%, accuracies found for change from urban to other classes and from other classes to agriculture were lower due to unique aspects of change in these classes which are not relevant for most change analyses applications. The study found a consistent loss in the deciduous forest area for much of the time studied, which is shown to influence the aquatic nitrogen implicated in the expansion of the invasive plant Phragmites australis in the Great Lakes Basin. This underscores the importance of LCLU maps, which allow for the quantification of historical land change in the watersheds of the Great Lakes where invasive species are expanding.