Open Source Software Tools Research Articles

An investigation of NMR‐Derived Structures for the SARS Coronavirus E Channel Protein

During the current Covid‐19 pandemic, college laboratories are shut down and undergraduate students who need research experience are having a hard time gaining those skills. Against this backdrop we developed a pilot project that would introduce students to internationally available online biochemical databases, open‐source modeling software and animation tools. The students received individual hands‐on training to generate data allowing them to engage in discovery and contextualize learning as they would in a face‐to face laboratory setting. We describe an investigation of NMR‐based structures of a SARS coronavirus E channel protein, structures found in the 5X29 pdb file deposited by Surya, Li, &, Torres, and described in (2018) Biochim. Biophys. Acta 1860: 1309‐1317. These small proteins form ion channels that are of interest because live attenuated vaccines target these channels promising prevention and treatment for coronavirus infections. The SARS coronavirus E channel protein, a right‐handed alpha‐helical bundle, is composed of 5 chains, A through E. Each chain has 16 NMR structures in the database. Incorporating computational and molecular visualization tools students generate data to observe, measure, and communicate the dihedral angle backbone movement of the whole pentamer, the separate chains, and individual amino acid residues on a bond‐by‐bond basis. All the open access software and programs used worked within a Windows/Mac environment except one program that used Linux command lines to create models from the PDB file. Afterwards these models were animated to demonstrate and analyze dihedral angle movement within structures. The students used this work to fulfill the laboratory component of their undergraduate independent study program at in the Biological Sciences and Chemistry Departments of Lehman College, a 4‐year college within the CUNY system.

AHPSort-GAIA: a visualisation tool for the sorting of alternative in AHP portrayed through a case in the food and drink industry

Although Multi-criteria Decision Making methods have been extensively used for choice problems, their descriptive use has rarely been considered. The descriptive component is important because it allows decision makers to better understand the problem. In this paper, we add the descriptive method GAIA as an extension to the AHPSort method that helps policy makers to gain insights into their decision problems, through the portraying of a case in the food and drink industry. This descriptive component is implemented as a visual analysis. The proposed extension has been implemented in an open-source software tool that allows users to visualise the different performances of food suppliers within a review process and provide feedback for improvements within the food and drink industry.

MmSIM: an open toolbox for accessible structured illumination microscopy.

Since the first practical super-resolution structured illumination fluorescence microscopes (SIM) were demonstrated more than two decades ago, the method has become increasingly popular for a wide range of bioimaging applications. The high cost and relative inflexibility of commercial systems, coupled with the conceptual simplicity of the approach and the desire to exploit and customize existing hardware, have led to the development of a large number of home-built systems. Several detailed hardware designs are available in the scientific literature, complemented by open-source software tools for SIM image validation and reconstruction. However, there remains a lack of simple open-source software to control these systems and manage the synchronization between hardware components, which is critical for effective SIM imaging. This article describes a new suite of software tools based on the popular Micro-Manager package, which enable the keen microscopist to develop and run a SIM system. We use the software to control two custom-built, high-speed, spatial light modulator-based SIM systems, evaluating their performance by imaging a range of fluorescent samples. By simplifying the process of SIM hardware development, we aim to support wider adoption of the technique. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Cost-effectiveness analysis with unordered decisions

IntroductionCost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered. ObjectiveTo develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease. MethodsWe explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer. ResultsThe evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay. ConclusionProblems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface.

Open-source deep-learning software for bioimage segmentation.

Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.

A dynamic optimization framework for basic oxygen furnace operation

About 70% of the steel used worldwide is produced via the Basic Oxygen Furnace (BOF). This process has limited automation, and normal operation relies on invaluable operators’ knowledge and past experience. In this paper, we present a framework for dynamic optimization of BOF operation. The dynamic optimization problems utilize a first-principles based dynamic model, and are solved using a hybrid method in which the states are integrated using a differential–algebraic equation (DAE) solver over an initial time interval and full discretization over the remainder of the time horizon. Optimization case studies are presented in which the impact of constraints and different objective formulations are explored. The results suggest that the proposed framework can potentially aid steelmakers to significantly reduce process costs while meeting production and quality targets. The framework is implemented in Python using the open-source software tool, CasADi.

Optimized Atomic Partial Charges and Radii Defined by Radical Voronoi Tessellation of Bulk Phase Simulations.

We present a novel method for the computation of well-defined optimized atomic partial charges and radii from the total electron density. Our method is based on a two-step radical Voronoi tessellation of the (possibly periodic) system and subsequent integration of the total electron density within each Voronoi cell. First, the total electron density is partitioned into the contributions of each molecule, and subsequently the electron density within each molecule is assigned to the individual atoms using a second set of atomic radii for the radical Voronoi tessellation. The radii are optimized on-the-fly to minimize the fluctuation (variance) of molecular and atomic charges. Therefore, our method is completely free of empirical parameters. As a by-product, two sets of optimized atomic radii are produced in each run, which take into account many specific properties of the system investigated. The application of an on-the-fly interpolation scheme reduces discretization noise in the Voronoi integration. The approach is particularly well suited for the calculation of partial charges in periodic bulk phase systems. We apply the method to five exemplary liquid phase simulations and show how the optimized charges can help to understand the interactions in the systems. Well-known effects such as reduced ion charges below unity in ionic liquid systems are correctly predicted without any tuning, empiricism, or rescaling. We show that the basis set dependence of our method is very small. Only the total electron density is evaluated, and thus, the approach can be combined with any electronic structure method that provides volumetric total electron densities—it is not limited to Hartree–Fock or density functional theory (DFT). We have implemented the method into our open-source software tool TRAVIS.

The Effect of Spray Water on an Automotive LIDAR Sensor: A Real-Time Simulation Study

This paper presents the first of its kind real-time simulation of the effect of spray water on an automotive LIDAR sensor. The simulation is based on physically measurable quantities in order to facilitate its validation and extension. Both the sensor and the environment are simulated using open source software tools: Blender and Cycles, with the objective of facilitating standardization and broader adoption. The parameters required to construct and calibrate the simulation are extracted from real measurements done on a test area and highway covering different water film thicknesses and vehicle speeds. The simulation is validated in different stages, first by comparing the trajectories of the particles with a regular solver and secondly, by the spatial distribution of the virtual and real point clouds with the measurement results. The resulting framework can be easily expanded to cover scenarios that are more complex and other phenomena counterproductive to the performance of a LIDAR sensor like dirt, exhaust gases, snow, rain and fog. The virtually generated point clouds can be used to validate the performance of the software required for the segmentation, classification and tracking of objects generated by the LIDAR sensor, thereby reducing development costs and increasing software quality.

Nonlinear MPC for collision-free and deadlock-free navigation of multiple nonholonomic mobile robots

In this paper, we present an online nonlinear Model Predictive Control (MPC) method for collision-free, deadlock-free navigation by multiple autonomous nonholonomic Wheeled Mobile Robots (WMRs). Our proposed method solves a nonlinear constrained optimization problem at each time step over a specified horizon to compute a sequence of optimal control inputs that drive the robots to target poses along collision-free trajectories, where the robots’ future states are predicted according to a unicycle kinematic model. To reduce the computational complexity of the optimization problem, we formulate it without stabilizing terminal constraints or terminal costs. We describe a computationally efficient approach to programming and solving the optimization problem, using open-source software tools for fast nonlinear optimization and applying the multiple-shooting method. We also provide rigorous proofs of the feasibility of the optimization problem and the stability of the proposed method. To validate the performance of our MPC method, we implement it in both 3D robot simulations and experiments with real nonholonomic WMRs for different multi-robot navigation scenarios with up to six robots. In all scenarios, the robots successfully navigate to their goal poses without colliding with one another or becoming trapped in a deadlock.

Explainable machine learning methods as a tool for higher education improvement

Group work and communication problems that occurred during the pandemic have significantly increased the use of new technologies, especially in higher education, where methods and systems of artificial intelligence, primarily machine learning, are increasingly used in the automation of various aspects of the teaching process. An application of these methods introduces completely new ethical and legal problems, related to the nature and manner of use of automatically created knowledge in various social processes. Confidence in automated generated knowledge and the issue of responsibility for the results of decisions that are made on the basis of that knowledge are trying to be solved by using the so-called Explainable Machine Learning methods. The paper discusses the application of these methods in the automation of various aspects of higher education, and demonstrates practical examples of their use in predicting student performance and teacher evaluation. In the practical examples, only publicly available data on the realization of parts of the teaching process were used, as well as open source software tools from the Weka data mining system and several libraries of explainable machine learning methods for the R and Python programming languages.

An uncertainty-based protocol for the setup and measurement of soot–black carbon emissions from gas flares using sky-LOSA

Abstract. Gas flaring is an important source of atmospheric soot–black carbon, especially in sensitive Arctic regions. However, emissions have traditionally been challenging to measure and remain poorly characterized, confounding international reporting requirements and adding uncertainty to climate models. The sky-LOSA optical measurement technique has emerged as a powerful means to quantify flare black carbon emissions in the field, but broader adoption has been hampered by the complexity of its deployment, where decisions during setup in the field can have profound, non-linear impacts on achievable measurement uncertainties. To address this challenge, this paper presents a prescriptive measurement protocol and associated open-source software tool that simplify acquisition of sky-LOSA data in the field. Leveraging a comprehensive Monte Carlo-based general uncertainty analysis (GUA) to predict measurement uncertainties over the entire breadth of possible measurement conditions, general heuristics are identified to guide a sky-LOSA user toward optimal data collection. These are further extended in the open-source software utility, SetupSkyLOSA, which interprets the GUA results to provide detailed guidance for any specific combination of location, date–time, and flare, plume, and ambient conditions. Finally, a case study of a sky-LOSA measurement at an oil and gas facility in Mexico is used to demonstrate the utility of the software tool, where potentially small regions of optimal instrument setup are easily and quickly identified. It is hoped that this work will help increase the accessibility of the sky-LOSA technique and ultimately the availability of field measurement data for flare black carbon emissions.

DSM Generation from Single and Cross-Sensor Multi-View Satellite Images Using the New Agisoft Metashape: The Case Studies of Trento and Matera (Italy)

DSM generation from satellite imagery is a long-lasting issue and it has been addressed in several ways over the years; however, expert and users are continuously searching for simpler but accurate and reliable software solutions. One of the latest ones is provided by the commercial software Agisoft Metashape (since version 1.6), previously known as Photoscan, which joins other already available open-source and commercial software tools. The present work aims to quantify the potential of the new Agisoft Metashape satellite processing module, considering that to the best knowledge of the authors, only two papers have been published, but none considering cross-sensor imagery. Here we investigated two different case studies to evaluate the accuracy of the generated DSMs. The first dataset consists of a triplet of Pléiades images acquired over the area of Trento and the Adige valley (Northern Italy), which is characterized by a great variety in terms of geomorphology, land uses and land covers. The second consists of a triplet composed of a WorldView-3 stereo pair and a GeoEye-1 image, acquired over the city of Matera (Southern Italy), one of the oldest settlements in the world, with the worldwide famous area of Sassi and a very rugged morphology in the surroundings. First, we carried out the accuracy assessment using the RPCs supplied by the satellite companies as part of the image metadata. Then, we refined the RPCs with an original independent terrain technique able to supply a new set of RPCs, using a set of GCPs adequately distributed across the regions of interest. The DSMs were generated both in a stereo and multi-view (triplet) configuration. We assessed the accuracy and completeness of these DSMs through a comparison with proper references, i.e., DSMs obtained through LiDAR technology. The impact of the RPC refinement on the DSM accuracy is high, ranging from 20 to 40% in terms of LE90. After the RPC refinement, we achieved an average overall LE90 <5.0 m (Trento) and <4.0 m (Matera) for the stereo configuration, and <5.5 m (Trento) and <4.5 m (Matera) for the multi-view (triplet) configuration, with an increase of completeness in the range 5–15% with respect to stereo pairs. Finally, we analyzed the impact of land cover on the accuracy of the generated DSMs; results for three classes (urban, agricultural, forest and semi-natural areas) are also supplied.

Bayesian reaction optimization as a tool for chemical synthesis.

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates1. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems2. Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models3. Bayesian optimization has also been recently applied in chemistry4-9; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimizationoutperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run.

A Thermal-Hydraulic Model for the Stagnation of Solar Thermal Systems with Flat-Plate Collector Arrays

Stagnation is the transient state of a solar thermal system under high solar irradiation where the useful solar gain is zero. Both flat-plate collectors with selective absorber coatings and vacuum-tube collectors exhibit stagnation temperatures far above the saturation temperature of the glycol-based heat carriers within the range of typical system pressures. Therefore, stagnation is always associated with vaporization and propagation of vapor into the pipes of the solar circuit. It is therefore essential to design the system in such a way that vapor never reaches components that cannot withstand high temperatures. In this article, a thermal-hydraulic model based on the integral form of a two-phase mixture model and a drift-flux correlation is presented. The model is applicable to solar thermal flat-plate collectors with meander-shaped absorber tubes and selective absorber coatings. Experimental data from stagnation experiments on two systems, which are identical except for the optical properties of the absorber coating, allowed comparison with simulations carried out under the same boundary conditions. The absorber of one system features a conventional highly selective coating, while the absorber of the other system features a thermochromic coating, which exhibits a significantly lower stagnation temperature. Comparison of simulation results and experimental data shows good conformity. This model is implemented into an open-source software tool called THD for the thermal-hydraulic dimensioning of solar systems. The latest version of THD, updated by the results of this article, enables planners to achieve cost-optimal design of solar thermal systems and to ensure failsafe operation by predicting the steam range under the initial and boundary conditions of worst-case scenarios.

Algorithms for the selection of fluorescent reporters

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through.

Are You Still Working on This? An Empirical Study on Pull Request Abandonment

The great success of numerous community-based open source software (OSS) is based on volunteers continuously submitting contributions, but ensuring sustainability is a persistent challenge in OSS communities. Although the motivations behind and barriers to OSS contributors’ joining and retention have been extensively studied, the impacts of, reasons for and solutions to contribution abandonment at the individual level have not been well studied, especially for pull-based development. To bridge this gap, we present an empirical study on pull request abandonment based on a sizable dataset. We manually examine 321 abandoned pull requests on GitHub and then quantify the manual observations by surveying 710 OSS developers. We find that while the lack of integrators’ responsiveness and the lack of contributors’ time and interest remain the main reasons that deter contributors from participation, limitations during the processes of patch updating and consensus reaching can also cause abandonment. We also show the significant impacts of pull request abandonment on project management and maintenance. Moreover, we elucidate the strategies used by project integrators to cope with abandoned pull requests and highlight the need for a practical handover mechanism. We discuss the actionable suggestions and implications for OSS practitioners and tool builders, which can help to upgrade the infrastructure and optimize the mechanisms of OSS communities.

A Comprehensive Roadmap to 50 Years of (Satellite) Earth Observation Resources for the Island of Ireland (1972 – 2023)

A comprehensive review of optical (panchromatic, multispectral and hyperspectral) and synthetic aperture radar satellite imagery coverage for terrestrial and near-shore areas of interest on the island of Ireland. The review spans 50 years and includes approximately 170+ retired and operational Earth Observation (EO) satellite sensors, and a further 60+ EO satellite sensors which are planned to be launched over the next five years. Satellite characteristics and sensor capabilities are listed in tabular form and the extents of the spatial and temporal coverage of the island of Ireland are estimated for each sensor (as available). Guidance on how to access this data is provided, with an emphasis on gaining free or reduced cost access for research and scientific purposes. Information on free open source software tools for viewing, processing and analysing EO imagery is provided, along with a selection of online resource and reference materials.

A Comprehensive Roadmap to 50 Years of (Satellite) Earth Observation Resources for the Island of Ireland (1972 – 2023)

A comprehensive review of optical (panchromatic, multispectral and hyperspectral) and synthetic aperture radar satellite imagery coverage for terrestrial and near-shore areas of interest on the island of Ireland. The review spans 50 years and includes approximately 170+ retired and operational Earth Observation (EO) satellite sensors, and a further 60+ EO satellite sensors which are planned to be launched over the next five years.Satellite characteristics and sensor capabilities are listed in tabular form and the extents of the spatial and temporal coverage of the island of Ireland are estimated for each sensor (as available). Guidance on how to access this data is provided, with an emphasis on gaining free or reduced cost access for research and scientific purposes.Information on free open source software tools for viewing, processing and analysing EO imagery is provided, along with a selection of online resource and reference materials.

EUKulele: Taxonomic annotation of the unsung eukaryotic microbes

The assessment of microbial species biodiversity is essential in ecology and evolutionary biology (Reaka-Kudla et al. 1996), but especially challenging for communities of microorganisms found in the environment (Das et al. 2006, Hillebrand et al. 2018). Beyond providing a census of organisms in the ocean, assessing marine microbial biodiversity can reveal how microbes respond to environmental change (Salazar et al. 2017), clarify the ecological roles of community members (Hehemann et al. 2016), and lead to biotechnology discoveries (Das et al. 2006). Computational approaches to characterize taxonomic diversity and phylogeny based on the quality of available data for environmental sequence datasets is fundamental for advancing our understanding of the role of these organisms in the environment. Even more pressing is the need for comprehensive and consistent methods to assign taxonomy to environmentally-relevant microbial eukaryotes. Here, we present EUKulele, an open-source software tool designed to assign taxonomy to microeukaryotes detected in meta-omic samples, and complement analysis approaches in other domains by accommodating assembly output and providing concrete metrics reporting the taxonomic completeness of each sample.

Genome Recovery, Functional Profiling, and Taxonomic Classification from Metagenomes.

Recovering and annotating bacterial genomes from metagenomes involves a series of complex computational tools that are often difficult to use for researches without a specialistic bioinformatic background. In this chapter we review all the steps that lead from raw reads to a collection of quality-controlled, functionally annotated bacterial genomes and propose a working protocol using state-of-the-art, open source software tools.