Set Of Open-source Tools Research Articles

Semi-automated approaches for interrogating spatial heterogeneity of tissue samples

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components’ interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues’ composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.

Asynchronous and Slow-Wave Oscillatory States in Connectome-Based Models of Mouse, Monkey and Human Cerebral Cortex

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available.

Development of Drainage Tools for Facilitating the Creation of Topologically-Sound, Unidirectional Flow Tile Networks

HighlightsNew publicly accessible drainage design QGIS plugins were developed.These tools were successfully used to create topologically-sound subsurface drainage layout networks.The tools are easy to use and reduce the level of expertise required to prepare drainage networks that can be successfully sized and buried.Abstract. An important step in designing a drainage system is laying out the network. Successful installation and sizing of the system require that the network be topologically sound and unidirectional. Online digitization of drainage systems does not always result in networks that meet these criteria. Tile lines may not be consistently digitized from upstream to downstream, and there may be disconnected segments, dangles, and overshoots. We have developed a set of open-source tools (plugins) in QGIS that convert a series of laterals and mains digitized on a map into a topologically-sound drainage network to facilitate both burying and sizing the drainage lines. Steps include reversing node numbering, if necessary, to ensure that tile nodes are numbered from upstream to downstream, connecting the ends of tile lines that are within a certain tolerance of each other, correcting overshoots, and setting an order for burying or calculating cumulative flow in line segments. An application example is presented using a proposed tile network layout at the university farm to demonstrate the capacity of these new tools to fix topologically disconnected tile lines and generate sound tile networks that can be successfully buried and sized. These easy-to-use tools are compatible with all versions of QGIS3 and may be freely downloaded or installed from the official QGIS Plugin Repository. Keywords: Line Geometry, Plugins, QGIS Software, Tile Layout Routines, Tile Nodes and Lines.

Integrative Oncology Leadership Collaborative (IOLC): Making tools (and a new PRO) for whole person cancer care to be routine and regular.

355 Background: Nearly two-thirds (62%) of people with cancer want to know about non-tumor directed therapies such as exercise, nutrition counseling, massage, and meditation before starting conventional treatment, but only 33% of oncologists agree with that timeline, according to a 2022 online survey . In 2021-2022, Samueli Integrative Health Programs (now Healing Works Foundation) funded a two-year integrative oncology leadership collaborative (IOLC) with a goal to further whole person cancer care, also called integrative oncology. The IOLC adapted a new patient reported outcome (PRO), and patient education and workflow resources used in whole person primary care for use in cancer care. The Personal Health Inventory (PHI), a person-focused PRO intake tool to elicit “what matters”, was made oncology-specific and deployed at several centers including a VA Medical Center. The IOLC created or adapted additional resources, resulting in a set of open-source tools for use in mainstream oncology practice. Methods: 13 cancer care organizations met monthly in 90-min virtual format from April-Dec 2021. From Jan-Dec 2022 sessions were held twice monthly over 1-hr. Year One allowed group cohesion in discussion-based review of the current state of whole person cancer care and covered foundational topics to include guideline-based supportive care and group medical visits. IOLC members suggested changes to the primary care PHI along with development of new oncology-specific whole person care resources. In Year Two, IOLC members were encouraged to beta test the PHI and related-resources in their practice settings and a Patient Empowerment and Advocacy Collaborative (PAEC) was developed as part of the group to bring in direct patient experience. The oncology-specific PHI was trialed within the Dayton VA Whole Health Oncology clinic from Oct 2021-June 2023. It proved feasible for use at any stage of the cancer care experience in 25 intake encounters. Results: PHI oncology-specific adaptations included replacing “Healthy Days” with FACT-G7 and the Patient Dignity Question (PDQ). Nineteen “pocket guides” were created and posted free online to support whole person cancer care. The patient voice was expanded through the formation of the PAEC in Year Two, and the group led two joint sessions where advocates provided input on IOLC tools’ development. Conclusions: A group of cancer care professionals and patients, working together in a sponsored community of practice, created no-cost resources, including a new PRO called the PHI, to further whole person integrative cancer care. The PHI demonstrated feasibility in a veteran population with cancer and can be offered at any time in the oncology trajectory, from diagnosis to end of life, surviving to thriving. Next steps are to formally test these tools in various oncology practice settings.

Open Science Drone Toolkit: Open source hardware and software for aerial data capture.

Despite the increased access to scientific publications and data as a result of open science initiatives, access to scientific tools remains limited. Uncrewed aerial vehicles (UAVs, or drones) can be a powerful tool for research in disciplines such as agriculture and environmental sciences, but their use in research is currently dominated by proprietary, closed source tools. The objective of this work was to collect, curate, organize and test a set of open source tools for aerial data capture for research purposes. The Open Science Drone Toolkit was built through a collaborative and iterative process by more than 100 people in five countries, and comprises an open-hardware autonomous drone and off-the-shelf hardware, open-source software, and guides and protocols that enable the user to perform all the necessary tasks to obtain aerial data. Data obtained with this toolkit over a wheat field was compared to data from satellite imagery and a commercial hand-held sensor, finding a high correlation for both instruments. Our results demonstrate the possibility of capturing research-grade aerial data using affordable, accessible, and customizable open source software and hardware, and using open workflows.

Streamlined Data Processing for Determination of Equilibrium Dissociation Constants with Accurate Constant via Transient Incomplete Separation (ACTIS)

The determination of accurate equilibrium dissociation constants, Kd, of protein-small molecule complexes is important but challenging as all established methods have inherent sources of inaccuracy. Accurate Constant via Transient Incomplete Separation (ACTIS) is a new method for Kd determination using transient incomplete separation of the complex from the unbound small molecule in a pressure-driven flow inside a capillary. ACTIS is accurate, and its accuracy is invariant to variations in geometries of both the fluidic system and the flow. Furthermore, ACTIS is implemented using a simple fluidic system supporting its accuracy and providing a simple-to-follow/copy template for instrumentation. Despite the simple and robust instrumentation/acquisition, the current data processing workflow is cumbersome, time consuming, and prone to hard-to-trace human errors therefore hindering ACTIS' ability to become a practical reference method for Kd determination. This technical note describes a streamlined workflow for processing ACTIS data; the workflow is implemented as a set of open-source software tools called prACTISed (https://github.com/prACTISedProgram/prACTISed). These tools allow all steps of data processing to be performed in a fast and straightforward fashion. These practical software tools complement the simple instrumentation serving both developers and users of ACTIS.

Ethical Hacking for a Good Cause: Finding Missing People using Crowdsourcing and Open-Source Intelligence (OSINT) Tools

Over 600,000 people go missing every year in the US alone. Despite the extensive resources allocated to investigating these cases, the high volume of missing person cases constitutes one of the biggest challenges for law enforcement agencies. One approach to tackle this challenge is using crowdsourcing. That is, volunteers use freely available tools and techniques to aid the existing efforts to investigate missing person cases. Open-Source Intelligence (OSINT) refers to gathering information from publicly available sources and analyzing it through a comprehensive set of open-source tools to produce meaningful and actionable intelligence. OSINT has been applied to address various societal challenges and crimes, including environmental abuse, human rights violations, child exploitation, domestic violence, disasters, and locating missing people. Building on this premise, this case examines a crowdsourced initiative called Trace Labs that aims to assist law enforcement agencies in solving missing person cases using OSINT tools. The case emphasizes socio-technical aspects of cybersecurity, highlighting both the bright and dark sides of technology. It demonstrates the potential of information systems to serve the public good by examining topics such as open-source software, crowdsourcing, and intelligence gathering, while acknowledging that the very same underlying technology can be used for malicious purposes.

Wave-particle interactions toolset: A python-based toolset to model wave-particle interactions in the magnetosphere

Wave particle interactions are known to be an efficient yet unquantified driver of the variability of particle populations in Earth’s magnetosphere, and their quantification and understanding through modelling has been a subject of ceaseless and extensive research during the last decades. Moreover, there is an increasing interest in techniques for radiation belt remediation, which refers to artificially controlling energetic particle populations in the near-Earth space environment via the scattering of particles from artificially generated electromagnetic waves. Whereas numerous modelling techniques are described in literature, there is a lack of a unified open-source toolset that incorporates the equations and parameterizations used by different wave-particle interaction models in a user-friendly environment. We present WPIT, the Wave-Particle Interactions Toolset, an open source, Python-based set of tools for modelling the interactions between energetic charged particles and VLF waves in the magnetosphere through test particle simulations. WPIT incorporates key routines related to wave-particle interactions in Python modules and also in Jupyter Notebook environment, enabling the traceability of all relevant equations in terms of their derivation and key assumptions, together with the programming environment and integrated graphics that enable users to conduct state-of-the-art wave-particle interaction simulations rapidly and efficiently. WPIT can be used either as a stand-alone simulation tool or as a library of routines that the user can extract and incorporate into an independent simulation. We present an analytic description of the code, the methodology used, and examples based on each of the WPIT modules. WPIT examples include the exact reproduction of simulation results that have been reported in literature, based on the same sets of parameters and assumptions, allowing the user to expand upon state-of-the-art. Finally, using the WPIT toolset, we perform a parametric analysis on the onset of nonlinear interactions between electrons with whistler-mode waves by varying the relevant parameters of the waves (amplitude, wave normal angle and frequency), the particles (pitch angle and energy) and the plasma environment (electron density and ion composition).

Beacon v2 Reference Implementation: a toolkit to enable federated sharing of genomic and phenotypic data.

Beacon v2 is an API specification established by the Global Alliance for Genomics and Health initiative (GA4GH) that defines a standard for federated discovery of genomic and phenotypic data. Here, we present the Beacon v2 Reference Implementation (B2RI), a set of open-source software tools that allow lighting up a local Beacon instance 'out-of-the-box'. Along with the software, we have created detailed 'Read the Docs' documentation that includes information on deployment and installation. The B2RI is released under GNU General Public License v3.0 and Apache License v2.0. Documentation and source code is available at: https://b2ri-documentation.readthedocs.io. Supplementary data are available at Bioinformatics online.

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale

BackgroundSince the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.MethodsThe project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected resultsThis study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.DiscussionThe project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.

JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport Systems

This paper deals with an intelligent data analytics platform - Jaison-Paul Data Analytics Platform (JP-DAP) - for metro rail transport systems. JP-DAP is intended to ensure smooth functioning, improved customer experience, ridership forecasting, and efficient administration of metro rail transportation systems by integrating and analysing its many data sources. It consists of a middleware which is built on the top of a Hadoop Distributed File System (HDFS) and Spark framework, along with a set of open-source software tools like Apache Hive, Pandas, Google TensorFlow and Spark ML-lib for real-time and legacy data processing. The benchmarking of JP-DAP was conducted using TestDFSIO and have found that it performs well according to industry standards. The specific use case for this project is Kochi Metro Rail Limited (KMRL). The analysis of Automated Fare Collection data from KMRL on JP-DAP framework have produced descriptive statistics visualisation of inflow and outflow analysis, travel patterns during weekdays and weekends, origin-destination matrix, etc.. Moreover JP-DAP framework is capable of producing short term passenger flow predictions using SVR machine learning algorithm with linear, radial basis function and polynomial kernels. Our experiments have shown that SVR linear kernel gives the most accurate results with the least errors in predicting the next day’s passenger count using the previous five weekdays data. The station usage (one-to-all) prediction using Long Short-Term Memory (LSTM) is also integrated to this framework. The visualisation as well as analytical outcomes of JP-DAP framework have also been made available to the external world using a rich set of REST APIs and are projected on to a web-dashboard.

TextFormats: Simplifying the definition and parsing of text formats in bioinformatics.

Text formats are common in bioinformatics, as they allow for editing and filtering using standard tools, as well as, since text formats are often human readable, manual inspection and evaluation of the data. Bioinformatics is a rapidly evolving field, hence, new techniques, new software tools, new kinds of data often require the definition of new formats. Often new formats are not formally described in a standard or specification document. Although software libraries are available for accessing the most common formats, writing parsers for text formats, for which no library is currently available, is a very common though tedious task, utilized by many researchers in the field. This manuscript presents the open source software library and toolset TextFormats (available at https://github.com/ggonnella/textformats), which aims at simplifying the definition and parsing of text formats. Formats specifications are written in a simple data description format using an interactive wizard. Automatic generation of data examples and automatic testing of specifications allow for checking for correctness. Given the specification for a text format, TextFormats allows parsing and writing data in that format, using several programming languages (Nim, Python, C/C++) or the provided command line and graphical user interface tools. Although designed as a general purpose software, the main target application field, for the above mentioned reasons, is expected to be in bioinformatics: Thus, the specifications of several common existing bioinformatics formats are included.

Vehicle Routing Simulation for Prediction of Commuter’s Behaviour

We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in the long-term perspective from both, individual experience, and widely available social knowledge. The approach is illustrated as practical application to modeling commuter behavior in the city of Winnipeg, Manitoba, Canada. All simulations in the paper are fully reproducible as they have been carried out by utilizing a set of opensource libraries and tools that we have developed for the Julia programming language and that are openly available on GitHub.

The lrd package: An R package and Shiny application for processing lexical data.

Recall testing is a common assessment to gauge memory retrieval. Responses from these tests can be analyzed in several ways; however, the output generated from a recall study typically requires manual coding that can be time intensive and error-prone before analyses can be conducted. To address this issue, this article introduces lrd (Lexical Response Data), a set of open-source tools for quickly and accurately processing lexical response data that can be used either from the R command line or through an R Shiny graphical user interface. First, we provide an overview of this package and include a step-by-step user guide for processing both cued- and free-recall responses. For validation of lrd, we used lrd to recode output from cued, free, and sentence-recall studies with large samples and examined whether the results replicated using lrd-scored data. We then assessed the inter-rater reliability and sensitivity and specificity of the scoring algorithm relative to human-coded data. Overall, lrd is highly reliable and shows excellent sensitivity and specificity, indicating that recall data processed using this package are remarkably consistent with data processed by a human coder.

Structural Analysis in Transit System Using Network Theory Case of Guadalajara, Mexico

Structural analysis in a transit network is a key aspect used to evaluate in a planning process. In this sense, the use of network science was applied in this work to generate a framework of the main structural features of a transport network. In this case, an alternative transport network in Guadalajara, Mexico was taken as an example. The network properties selected were grade of accessibility, spatial friction, and vulnerability. In the case of the grade of accessibility, this propriety makes reference to the efficiency of the travel time that the network gives due to its structural features. The spatial friction measures how direct in terms of distance the trips that the network provides are, and the vulnerability relates to the ease with which the network can comprise its performance by affectations to their nodes or links. In this sense, this work presents a detailed methodology and a set of open-source tools that can be used to measure these key structural elements for decision making.

The Virtual Online Supermarket: An Open-Source Research Platform for Experimental Consumer Research

It is controversially discussed if and which interventions policymakers should implement to promote healthier, more sustainable, and more ethical food choices. Often, policy measures suffer from a lack of data. This is especially true for the growing field of online grocery shopping. Yet, it not always feasible to test the impact of each possible policy intervention in the field. Here, computer-simulated shopping experiments offer a complementary approach. Recent evidence suggests that they heighten the realism of consumer experiments and collect valid data at a relatively low cost. In this paper, we introduce an open-source toolset that offers multiple avenues to develop and run experiments in the context of online grocery shopping. Hence, it supports researchers and policy makers in evaluating instore-intervention aiming to support more sustainable food choices.

A cookbook for using model diagnostics in integrated stock assessments

Integrated analysis has increasingly been the preferred approach for conducting stock assessments and providing the basis for management advice for fish and invertebrate stocks around the world. Many decisions are required when developing integrated stock assessments. For example, the analyst needs to decide whether the model fits the data, if the optimization was successful, if estimates are consistent retrospectively, and if the model is suitable to predict future stock responses to fishing. This study provides practical guidelines for implementing selected diagnostic tools that can assist analysts in identifying problems with model specifications and alternatives that can be explored to minimize or eliminate such problems. Emphasis is placed on reviewing the implementation and interpretation of contemporary model diagnostic tools. We first describe each diagnostic approach and its utility. We then proceed by providing a “cookbook recipe” on how to implement each of the diagnostics, together with an interpretation of the results, using two worked examples of integrated stock assessments with Stock Synthesis. Further, we provide a conceptual flow chart that lays out a generic process of model development and selection using the presented model diagnostics. Based on this, we propose the following four properties as objective criteria for evaluating the plausibility of a model: (1) model convergence, (2) fit to the data, (3) model consistency, and (4) prediction skill. It would greatly benefit the stock assessment community if the next generation of stock assessment models could include the diagnostic tests presented in this study as a set of open source tools.

Integrated Tool Set for Control, Calibration, and Characterization of Quantum Devices Applied to Superconducting Qubits

Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, the highly detailed system characterization required to understand the underlying error sources is an arduous process and impractical with increasing chip size. Open-loop optimal control techniques allow for the improvement of gates but are limited by the models they are based on. To rectify the situation, we provide an integrated open-source tool-set for Control, Calibration and Characterization, capable of open-loop pulse optimization, model-free calibration, model fitting and refinement. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget, all based on a high-performance, feature-rich simulator. We illustrate our methods using simulated fixed-frequency superconducting qubits for which we learn model parameters with less than 1% error and derive a coherence limited cross-resonance (CR) gate that achieves 99.6% fidelity without need for calibration.

Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot.

Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data.

Using knowledge discovery to propose a two-phase model checking for safety analysis of graph transformations

Safety is one of the most important features of modern software systems, especially safety-critical systems such as nuclear power plants, which can be checked exactly by model checking. Model checking is a formal verification technique that analyzes system properties through exploring all reachable states (state space) of a model of a system. The problem of the technique is that it confronts the state space explosion in large and complex systems due to exponential memory usage. Recent researches show that a partial and intelligent exploration of the state space can be a suitable solution to overcome this problem. In this paper, we propose a two-phase model checking for safety analysis of systems specified formally through graph transformations. In the first phase, the beam-search algorithm explores the state space to a specific number of states. In case of failure of the phase, the second phase starts: in systems specified through graph transformations, the rule applied on the previous state can determine the rule that can perform on the next state. In other words, the rule on current state depends on only the rule applied to previous state, not the one on earlier states. Hence, a Markov chain (MC) is estimated to capture dependencies between the sequence of applied rules in the state space explored by the beam-search algorithm. The MC is then employed to explore the remainder of the state space intelligently. To evaluate the effectiveness of the two-phase model checking, we implement it in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results show that the two-phase model checking has the high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques.