Open-source Interface Research Articles

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Development of a Hydrogen Fuel Cell Prototype Vehicle Supported by Artificial Intelligence for Green Urban Transport

In the automotive sector, the zero emissions area has been dominated by battery electric vehicles. However, prospective users cite charging times, large batteries, and the deployment of charging stations as a counter-argument. Hydrogen will offer a solution to these areas, in the future. This research focuses on the development of a prototype three-wheeled vehicle that is named Neumann H2. It integrates state-of-the-art energy storage systems, demonstrating the benefits of solar-, battery-, and hydrogen-powered drives. Of crucial importance for the R&D platform is the system’s ability to record its internal states in a time-synchronous format, providing valuable data for researchers and developers. Given that the platform is equipped with the ROS2 Open-Source interface, the data are recorded in a standardized format. Energy management is supported by artificial intelligence of the “Reinforcement Learning” type, which selects the optimal energy source for operation based on different layers of high-fidelity maps. In addition to powertrain control, the vehicle also uses artificial intelligence to detect the environment. The vehicle’s environment-sensing system is essentially designed to detect, distinguish, and select environmental elements through image segmentation using camera images and then to provide feedback to the user via displays.

A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior.

To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

Enhancing study room reservations at the Oklahoma State University Libraries: A case study on user interface development

This case study explores the transformation of study room reservations at the Oklahoma State University (OSU) Libraries. Initially reliant on manual methods, the library transitioned to vendor-supported software and would later develop a custom, open source user interface (UI) to streamline booking efficiency. These improvements help demonstrate the effectiveness of tailored solutions in meeting user needs and improving system functionality for all stakeholders.

Optically detected magnetic resonance with an open source platform

Localized electronic spins in solid-state environments form versatile and robust platforms for quantum sensing, metrology and quantum information processing. With optically detected magnetic resonance (ODMR), it is possible to prepare and readout highly coherent spin systems, up to room temperature, with orders of magnitude enhanced sensitivities and spatial resolutions compared to induction-based techniques, allowing for single spin manipulations. While ODMR was first observed in organic molecules, many other systems have since then been identified. Among them is the nitrogen-vacancy (NV) center in diamond, which is used both as a nanoscale quantum sensor for external fields and as a spin qubit. Other systems permitting ODMR are rare earth ions used as quantum memories and many other color centers trapped in bulk or 2-dimensional host materials. In order to allow the broadest possible community of researchers and engineers to investigate and develop novel ODMR-based materials and applications, we review here the setting up of ODMR experiments using commercially available hardware. We also present in detail the dedicated collaborative open-source interface named Qudi and describe the features we added to speed-up data acquisition, relax instrument requirements and extend its applicability to ensemble measurements. Covering both hardware and software development, this article aims to overview the setting of ODMR experiments and provide an efficient, portable and collaborative interface to implement innovative experiments to optimize the development time of ODMR experiments for scientists of any backgrounds.

Pengembangan Aplikasi Kalkulator Multifungsi Menggunakan Flutter Framework

Flutter is an open-source user interface (UI) software development kit (SDK) created by Google, designed for building natively compiled applications for mobile, web, and desktop platforms from a single codebase. It employs the Dart programming language and provides a rich set of pre-built widgets, offering developers the flexibility to craft visually appealing and interactive user interfaces. This paper presents an overview of the Flutter framework, discussing its core features, architecture, and benefits. Flutter's "hot reload" feature allows for real-time code changes without the need for a full application restart, enabling faster development and experimentation. The framework's performance is optimized by leveraging Skia, a graphics engine for smooth rendering, resulting in fast and fluid UI experiences. The Flutter ecosystem encompasses a thriving community, extensive documentation, and a wide range of plugins that extend functionality and simplify integration with native platform features. Its cross-platform capabilities streamline the development process and reduce time-to-market, making it a popular choice among developers and businesses alike. The paper also highlights some potential challenges faced when adopting Flutter, such as platform-specific issues and limited native functionality access. However, the benefits of code reusability, fast development cycles, and consistent UI across platforms outweigh these challenges for many projects. Overall, Flutter has emerged as a robust and versatile framework for building high-performance applications across diverse platforms. Its continuous evolution, strong community support, and commitment from Google position it as a promising solution for modern app development in the rapidly evolving world of technology.

Versatile software and hardware combo enabling photon counting acquisition and real-time display for multiplexing, 2D and continuous 3D two-photon imaging applications.

.Significance: rPySight brings a flexible and highly customizable open-software platform built around a powerful multichannel digitizer; combined, it enables performing complex photon counting-based experiments. We exploited advanced programming technology to share the photon counting stream with the graphical processing unit (GPU), making possible real-time display of two-dimensional (2D) and three-dimensional (3D) experiments and paving the road for other real-time applications.Aim: Photon counting improves multiphoton imaging by providing better signal-to-noise ratio in photon-deprived applications and is becoming more widely implemented, as indicated by its increasing presence in many microscopy vendor portfolios. Despite the relatively easy access to this technology offered in commercial systems, these remain limited to one or two channels of data and might not enable highly tailored experiments, forcing most researchers to develop their own electronics and code. We set to develop a flexible and open-source interface to a cutting-edge multichannel fast digitizer that can be easily integrated into existing imaging systems.Approach: We selected an advanced multichannel digitizer capable of generating 70M tags/s and wrote an open software application, based on Rust and Python languages, to share the stream of detected events with the GPU, enabling real-time data processing.Results: rPySight functionality was showcased in real-time monitoring of 2D imaging, improved calcium imaging, multiplexing, and 3D imaging through a varifocal lens. We provide a detailed protocol for implementing out-of-the-box rPySight and its related hardware.Conclusions: Applying photon-counting approaches is becoming a fundamental component in recent technical developments that push well beyond existing acquisition speed limitations of classical multiphoton approaches. Given the performance of rPySight, we foresee its use to capture, among others, the joint dynamics of hundreds (if not thousands) of neuronal and vascular elements across volumes, as is likely required to uncover in a much broader sense the hemodynamic transform function.

Simulating transient noise bursts in LIGO with generative adversarial networks

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $1\text{ }\text{ }{\mathrm{min}}^{\ensuremath{-}1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ generative adversarial networks (GAN), a state-of-the-art deep learning algorithm inspired by game theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of $\ensuremath{\sim}{10}^{3}$ glitches from Hanford and Livingston detectors in less than 1 sec. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.

LiquidDiffract: software for liquid total scattering analysis

LiquidDiffract is an open source, Python-based graphical application for X-ray total scattering analysis of liquids and disordered solids. The software implements procedures to obtain information on macroscopic bulk properties and local atomic-scale structure of monatomic or polyatomic samples from X-ray total scattering data. LiquidDiffract provides an easy to use interface with tools to perform background subtraction; calculation, normalisation, and refinement of the reciprocal-space structure factor and real-space correlation functions; and the extraction of structural information such as bond lengths, coordination number, and bulk density. The software is well suited to investigations of amorphous materials at extreme conditions, such as studies of high-pressure melt structure, polyamorphic phase transitions, and liquid equations of state. The open-source distribution and graphical interface will be of particular benefit to researchers who are new to the field. In this article we describe the distribution, system requirements, and installation of LiquidDiffract, and detail the data processing workflow and underlying numerical methods.

Laborem Box: A scalable and open source platform to design remote lab experiments in electronics

Hybrid teaching (face-to-face and distance learning) enables students to better prepare and complete their courses. In science, technology, engineering and mathematics, it is important that practical training be an integral part of the curriculum. Laborem project developed at the technological university institute in Bayonne, France, enables undergraduate students to carry out part of their lab experiments in electronics remotely. Started in 2011, Laborem platform was based on proprietary solutions. Since 2017, the platform has migrated to open source software (PyScada) and open source interface box (Laborem Box), which was developed in order to enable the connection of several circuit boards to be studied. These boards, called plugs, are easily interchangeable and enable teachers to quickly adapt the proposed circuits to their course. The software also provides a simple front panel to adapt the human machine interface that is available for students. Laborem Box consists of a 3D printable box, a power supply board, a set of plugs, and a motherboard that enables students to study the selected plug. In addition, a single board computer is embedded and a hard disk can be used if necessary. This paper is intended to describe the hardware and software design of Laborem platform, and to serve as a guide to explain how to duplicate and deploy this system, primarily dedicated to undergraduate students for learning basic electronics.

Decoding the Role of Astrocytes in the Entorhinal Cortex in Alzheimer’s Disease Using High-Dimensional Single-Nucleus RNA Sequencing Data and Next-Generation Knowledge Discovery Methodologies: Focus on Drugs and Natural Product Remedies for Dementia

Introduction: Alzheimer’s disease (AD) is a major cause of the development of cognitive decline and dementia. AD and associated dementias (ADRD) are the major contributors to the enormous burden of morbidity and mortality worldwide. To date, there are no robust therapies to alleviate or cure this debilitating disease. Most drug treatments focus on restoring the normal function of neurons and the cells that cause inflammation, such as microglia in the brain. However, the role of astrocytes, the brain’s housekeeping cells, in the development of AD and the initiation of dementia is still not well understood. Objective: To decipher the role of astrocytes in the entorhinal cortex of AD patients using single nuclear RNA sequencing (snRNASeq) datasets from the Single Cell RNA-seq Database for Alzheimer’s Disease (scREAD). The datasets were originally derived from astrocytes, isolated from the entorhinal cortex of AD brain and healthy brain to decipher disease-specific signaling pathways as well as drugs and natural products that reverse AD-specific signatures in astrocytes. Methods: We used snRNASeq datasets from the scREAD database originally derived from astrocytes isolated from the entorhinal cortex of AD and healthy brains from the Gene Expression Omnibus (GEO) (GSE138852 and GSE147528) and analyzed them using next-generation knowledge discovery (NGKD) platforms. scREAD is a user-friendly open-source interface available at https://bmbls.bmi.osumc.edu/scread/that enables more discovery-oriented strategies. snRNASeq data and metadata can also be visualized and downloaded via an interactive web application at adsn.ddnetbio.com. Differentially expressed genes (DEGs) for each snRNASeq dataset were analyzed using iPathwayGuide to compare and derive disease-specific pathways, gene ontologies, and in silico predictions of drugs and natural products that regulate AD -specific signatures in astrocytes. In addition, DEGs were analyzed using the L1000FWD and L1000CDS2 signature search programming interfaces (APIs) to identify additional drugs and natural products that mimic or reverse AD-specific gene signatures in astrocytes. Results: We found that PI3K/AKT signaling, Wnt signaling, neuroactive ligand-receptor interaction pathways, neurodegeneration pathways, etc. were significantly impaired in astrocytes from the entorhinal cortex of AD patients. Biological processes such as glutamate receptor signaling pathway, regulation of synapse organization, cell-cell adhesion via plasma membrane adhesion molecules, and chylomicrons were negatively enriched in the astrocytes from the entorhinal cortex of AD patients. Gene sets involved in cellular components such as postsynaptic membrane, synaptic membrane, postsynapse, and synapse part were negatively enriched (p < 0.01). Moreover, molecular functions such as glutamate receptor activity, neurotransmitter receptor activity, and extracellular ligand-gated ion channels were negatively regulated in the astrocytes of the entorhinal cortex of AD patients (p < 0.01). Moreover, the application of NGKD platforms revealed that antirheumatic drugs, vitamin-E, emetine, narciclasine, cephaeline, trichostatin A, withaferin A, dasatinib, etc. can potentially reverse gene signatures associated with AD. Conclusions: The present study highlights an innovative approach to use NGKD platforms to find unique disease-associated signaling pathways and specific synthetic drugs and natural products that can potentially reverse AD and ADRD-associated gene signatures.

A Dynamic Optimization Tool to Size and Operate Solar Thermal District Heating Networks Production Plants

The aim of the ISORC/OPTIMISER project is to increase and improve the use of solar thermal energy in district heating networks. One of the main tasks of the project is to develop an optimization tool for the sizing and operation of a solar district heating network. This is the first optimization tool using an open-source interface (Julia, JuMP) and solver (Ipopt) to solve nonlinear problems. This paper presents the multi-period optimization problem which is implemented to consider the dynamic variations in a year, represented by four typical days, with an hourly resolution. The optimum is calculated for a total duration of 20 years. First, this paper presents the modeling of the different components of a solar district heating network production plant: district network demand, storage and three sources, i.e., a fossil (gas) and two renewable (solar and biomass) sources. In order to avoid prohibitive computational time, the modeling of sources and storage has to be fairly simple. The multi-period optimization problem was formulated. The chosen objective function is economic: The provided economic model is accurate and use nonlinear equations. Finally the formulated problem is a nonlinear Programming problem. Optimization of the studied case exhibits consistent operating profiles and design. A comparison is made of different types of storage connection at the production site, highlighting the relevance of placing the storage at the solar field outlet. The optimum configuration supplies 49% of demand using solar energy, achieving a renewable rate of 69% in combination with the biomass boiler.

ProteomicsDB: toward a FAIR open-source resource for life-science research.

ProteomicsDB (https://www.ProteomicsDB.org) is a multi-omics and multi-organism resource for life science research. In this update, we present our efforts to continuously develop and expand ProteomicsDB. The major focus over the last two years was improving the findability, accessibility, interoperability and reusability (FAIR) of the data as well as its implementation. For this purpose, we release a new application programming interface (API) that provides systematic access to essentially all data in ProteomicsDB. Second, we release a new open-source user interface (UI) and show the advantages the scientific community gains from such software. With the new interface, two new visualizations of protein primary, secondary and tertiary structure as well an updated spectrum viewer were added. Furthermore, we integrated ProteomicsDB with our deep-neural-network Prosit that can predict the fragmentation characteristics and retention time of peptides. The result is an automatic processing pipeline that can be used to reevaluate database search engine results stored in ProteomicsDB. In addition, we extended the data content with experiments investigating different human biology as well as a newly supported organism.

IMU-Based Hand Gesture Interface Implementing a Sequence-Matching Algorithm for the Control of Assistive Technologies

Assistive technologies (ATs) often have a high-dimensionality of possible movements (e.g., assistive robot with several degrees of freedom or a computer), but the users have to control them with low-dimensionality sensors and interfaces (e.g., switches). This paper presents the development of an open-source interface based on a sequence-matching algorithm for the control of ATs. Sequence matching allows the user to input several different commands with low-dimensionality sensors by not only recognizing their output, but also their sequential pattern through time, similarly to Morse code. In this paper, the algorithm is applied to the recognition of hand gestures, inputted using an inertial measurement unit worn by the user. An SVM-based algorithm, that is aimed to be robust, with small training sets (e.g., five examples per class) is developed to recognize gestures in real-time. Finally, the interface is applied to control a computer’s mouse and keyboard. The interface was compared against (and combined with) the head movement-based AssystMouse software. The hand gesture interface showed encouraging results for this application but could also be used with other body parts (e.g., head and feet) and could control various ATs (e.g., assistive robotic arm and prosthesis).

General, robust, and efficient polyhedron intersection in the Interface Reconstruction Library

Intersecting a polyhedron with a half-space or another polyhedron is required in various fields, including computer graphics, computer-aided design, and many methods used in computational physics. Often, the polyhedra involved are complicated non-convex objects, and intersecting them constitutes a significant computational cost. To address this, we present a general, geometrically robust, and efficient polyhedron intersection algorithm based on the half-edge data structure and implemented in the open-source Interface Reconstruction Library. We then employ this algorithm in a novel graph-based approach to distribute the volume of a polyhedron over a computational mesh. To demonstrate their performance, the two methods are tested on randomly generated configurations. Compared to the open-source packages VOFTools and R3D, the polyhedron intersection algorithm shows superior speed over the range of convex and non-convex polyhedra tested. The volume distribution method also proves to be discretely conservative, even for meshes containing degenerate faces.

Phytoplankton Photophysiology Utilities: A Python Toolbox for the Standardization of Processing Active Chlorophyll-a Fluorescence Data

The uptake and application of single turnover chlorophyll fluorometers to the study of phytoplankton ecosystem status and microbial functions has grown considerably in the last two decades. However, standardization of measurement protocols, processing of fluorescence transients and quality control of derived photosynthetic parameters is still lacking and makes community goals of large global databases of high-quality data unrealistic. We introduce the Python package Phytoplankton Photophysiology Utilities (PPU), an adaptable and open-source interface between Fast Repetition Rate and Fluorescence Induction and Relaxation instruments and python. The PPU package includes a variety of functions for the loading, processing and quality control of single turnover fluorescence transients from many commercially available instruments. PPU provides the user with greater flexibility in the application of the Kolber-Prasil-Falkowski model; tools for plotting, quality control, correcting instrument biases and high-throughput processing with ease; and a greater appreciation for the uncertainties in derived photosynthetic parameters. Using data from three research cruises across different biogeochemical regimes, we provide example applications of PPU to fit raw active chlorophyll-a fluorescence data from three commercial instruments and demonstrate tools which help to reduce uncertainties in the final fitted parameters.

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research.

The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT.

Over-The-Top (OTT) Media Application using Flutter

An Over-The-Top (OTT) Media is a streaming media service offered directly to the viewers through internet. The OTT service providers provides the program types like news, movies, daily soaps, web shows as well as cable TV. Nowadays, the businesses all over the world are offering an unbelievable amount of variety in media content and the traditional way of broadcasting on cable TV cannot efficiently represent or contain the wide range of media content. Considering these problems, we are making an OTT application for Next Screen Productions. So, they can produce their media content on internet through this application. We will be using Flutter, which is an open-source User Interface (UI) toolkit and Dart Programming Language which are created by Google. For storage of data, we are using Firebase. For recommendation of content, recommendation algorithms will be used.

Twitter Explorer

We present an open-source interface for scientists to explore Twitter data through interactive network visualizations. Combining data collection, transformation and visualization in one easily accessible framework, the twitter explorer connects distant and close reading of Twitter data through the interactive exploration of interaction networks and semantic networks. By lowering the technological barriers of data-driven research, it aims to attract researchers from various disciplinary backgrounds and facilitates new perspectives in the thriving field of computational social science.