Web-based Graphical Interface Research Articles

Crop Yield Prediction Using Machine Learning

The majority of India’s agricultural products have been negatively impacted by climate change in terms of performance over the past 20 years. Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on the agricultural field, this project will assist the farmers in learning the yield of their crop, enabling them to make the best choices. By creating a working prototype of an interactive prediction system, it tries to find a solution. It will be put into practise to implement such a system with a user-friendly web-based graphic user interface and the machine learning algorithm. The farmer will have access to the prediction’s outcomes. So, there are various ways or algorithms for this type of data analytics in crop prediction, and we can anticipate crop production with the aid of those algorithms. It employs the random forest algorithm. There are no suitable technologies or solutions to deal with the scenario we are in, despite the analysis of all these concerns and problems, including weather, temperature, humidity, rainfall, and moisture. In India, there are numerous ways to boost agricultural economic development. Data mining can be used to forecast crop yield growth. Data mining is, in general, the process of reviewing data from various angles and distilling it into pertinent information. The most well-known and effective supervised machine learning algorithm, random forest, can perform both classification and regression tasks. It works by building a large number of decision trees during training time and producing output of the class that is the mean prediction (for regression) or mode of the classes (for classification) of the individual trees.

Using Theia Trace Viewer to Visualize JPF Traces

Java Pathfinder (JPF) is currently the most feature-rich platform for an in-depth analysis of programs that run on the Java Virtual Machine (JVM). However, using JPF and interpreting its results is a challenge. Dependencies on specific Java versions inhibit the adoption of JPF, and its textual output makes it hard to understand its analysis results. To this end, we present the Theia Trace Viewer (TTV) for JPF traces, which features a web-based graphical user interface. It does not only make it easier to get an overview of JPF traces, but it can visualize traces without requiring an installation on the client side, thus making JPF more accessible at the technical level as well. We evaluated TTV with several groups of users, showing the potential of our approach.

Renminbi Internationalization Process: A Quantitative Literature Review

As China’s position in the global economy has gradually improved, the importance of debates on the role of the renminbi in the international monetary system has significantly increased. This paper uses bibliometric methods—Bibliometrix R-package and its web-based graphical interface Biblioshiny—applied to data imported from Web of Science and Scopus to investigate and synthesize the renminbi literature published in English between 1995 and 2021. Science mapping offers a visual representation of different networks and clusters of authors’ keywords. The performance analysis, a quantitative evaluation of the most published sources, authors and papers on renminbi internationalization in the last 25 years, shows that the interest on the topic has grown, particularly after 2009 and 2016, respectively. There is also a high degree of concentration in the field, considering that out of the 802 analyzed papers, published in 393 sources, five authors and four journals had the highest impact. The content analysis identifies the main directions in the renminbi internationalization literature and future research questions to further explore this subject. The COVID-19 pandemic and post-Ukraine war era could generate a deeper reform of the international monetary system, in which the Chinese currency will strengthen its global position alongside the US dollar and the euro.

SODAR: managing multiomics study data and metadata.

Scientists employing omics in life science studies face challenges such as the modeling of multiassay studies, recording of all relevant parameters, and managing many samples with their metadata. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for omics data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface for managing multiassay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS data management system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable APIs and command-line access for metadata and file storage. SODAR supports complex omics integration studies and can be easily installed. The software is written in Python 3 and freely available at https://github.com/bihealth/sodar-server under the MIT license.

AHA: AI-guided tool for the quantification of venom-induced haemorrhage in mice

Venom-induced haemorrhage constitutes a severe pathology in snakebite envenomings, especially those inflicted by viperid species. To both explore venom activity accurately and evaluate the efficacy of viperid antivenoms for the neutralisation of haemorrhagic activity it is essential to have available a precise, quantitative tool for empirically determining venom-induced haemorrhage. Thus, we have built on our prior approach and developed a new AI-guided tool (AHA) for the quantification of venom-induced haemorrhage in mice. Using a smartphone, it takes less than a minute to take a photo, upload the image, and receive accurate information on the magnitude of a venom-induced haemorrhagic lesion in mice. This substantially decreases analysis time, reduces human error, and does not require expert haemorrhage analysis skills. Furthermore, its open access web-based graphical user interface makes it easy to use and implement in laboratories across the globe. Together, this will reduce the resources required to preclinically assess and control the quality of antivenoms, whilst also expediting the profiling of haemorrhagic activity in venoms for the wider toxinology community.

Update 2.0 to “ViroCon: A software to compute multivariate extremes using the environmental contour method”

Update 2.0 to “ViroCon: A software to compute multivariate extremes using the environmental contour method”

Geographic name resolution service: A tool for the standardization and indexing of world political division names, with applications to species distribution modeling

Massive biological databases of species occurrences, or georeferenced locations where a species has been observed, are essential inputs for modeling present and future species distributions. Location accuracy is often assessed by determining whether the observation geocoordinates fall within the boundaries of the declared political divisions. This otherwise simple validation is complicated by the difficulty of matching political division names to the correct geospatial object. Spelling errors, abbreviations, alternative codes, and synonyms in multiple languages present daunting name disambiguation challenges. The inability to resolve political division names reduces usable data, and analysis of erroneous observations can lead to flawed results. Here, we present the Geographic Name Resolution Service (GNRS), an application for correcting, standardizing, and indexing world political division names. The GNRS resolves political division names against a reference database that combines names and codes from GeoNames with geospatial object identifiers from the Global Administrative Areas Database (GADM). In a trial resolution of political division names extracted from >270 million species occurrences, only 1.9%, representing just 6% of occurrences, matched exactly to GADM political divisions in their original form. The GNRS was able to resolve, completely or in part, 92% of the remaining 378,568 political division names, or 86% of the full biodiversity occurrence dataset. In assessing geocoordinate accuracy for >239 million species occurrences, resolution of political divisions by the GNRS enabled the detection of an order of magnitude more errors and an order of magnitude more error-free occurrences. By providing a novel solution to a significant data quality impediment, the GNRS liberates a tremendous amount of biodiversity data for quantitative biodiversity research. The GNRS runs as a web service and is accessible via an API, an R package, and a web-based graphical user interface. Its modular architecture is easily integrated into existing data validation workflows.

Draw and Listen! A Sketch-Based System for Music Inpainting

In this work we implement an interactive system for filling in missing measures in a monophonic music piece. This system takes a user’s hand-drawn curves as input and generates a melody whose rhythm and pitch contour match with the curves. Contrary to previous interactive music inpainting work, users of the proposed system do not need to understand the music notation; they just need a rough idea of the shape of the melody and draw it out. This system is implemented under the variational auto-encoder framework and is enabled by a proposed melody disentanglement scheme to disentangle relative pitch, relative rhythm and musical context. We also create a web-based graphical user interface to facilitate the user interaction. We evaluate the system on a commonly used Irish folk song dataset. Objective and subjective evaluations show that this novel interaction is intuitive and effective for melody inpainting, and the proposed neural approach outperforms two baselines we developed based on previous work, in terms of musicality and fidelity to the user’s input.

Implementation and Case Study of a Clinical Research Data Warehouse in Radiation Oncology

<h3>Purpose/Objective(s)</h3> Radiation oncology generates large volumes of multi-source data that can be harnessed to accelerate personalized cancer care. We describe the implementation of a departmental clinical data warehouse and a case demonstrating application of this tool. <h3>Materials/Methods</h3> Physician stakeholders collaborated with software engineers and developers with domain knowledge in electronic medical records (EMR), cancer registries, and radiation treatment planning systems to design a departmental radiation oncology data warehouse. Data from EMR, radiation treatment planning system, institutional cancer registry, and pathology database were merged by patient medical record number and made accessible via a web application with a graphical user interface (GUI) written in R Shiny and hosted on Shiny Server Pro (RStudio). Data sources are refreshed at weekly to quarterly intervals and extracted to Shiny Server Pro. Team meetings were held every two weeks to refine current data elements, implement process improvements, and improve data quality and integrity. The warehouse can only be accessed by departmental users who have completed data privacy training and attestation under an IRB. Using the data warehouse, we examined radiotherapy fractionation patterns among breast cancer patients in our department. Filters for demographic, prognostic, and treatment characteristics were applied to build cohorts of patients treated with hypofractionated whole breast irradiation (WBI, 2.6-2.7 dose/fraction for 15-16 fractions) and standard fractionated WBI (1.8-2 dose/fraction for 25-30 fractions). <h3>Results</h3> The data warehouse includes 66,078 unique patients seen in our department from 4/28/1997 through 12/7/2021. We identified 2,206 patients diagnosed with breast cancer who received WBI between 2010 and 2020. Use of hypofractionation increased from 16% in 2010 to 71% in 2020. Of the patients who received hypofractionated WBI in 2010, median age was 69 years, 89% had Tis (33%) or T1-2, N0 (56%) tumors, and 67% had right-sided breast cancer, compared to 58 years, 86% (13%, 73%), and 48% in 2020, respectively. Among the patients who received hypofractionated WBI and with known hormone receptor status, 100% in 2010 and 80% in 2020 were ER/PR+/HER2-. These large-scale analyses took minutes to execute and yielded hypofractionation trends similar to those in existing studies. <h3>Conclusion</h3> We created a clinical data warehouse that includes all patients treated in one radiation oncology department and aggregates health information from different data sources into a centralized and user-friendly, web-based GUI console. We used this resource to efficiently examine WBI fractionation patterns in the warehouse over a 10-year period; work is ongoing to continually improve data quality. By leveraging real-time clinical data for secondary uses, this effort can drive precision cancer medicine by making research, quality assessment, and predictive modeling a natural outgrowth of routine patient care.

Stochastic atomic modeling and optimization with fullrmc

Understanding materials' atomic structure with a high level of confidence and certainty is often regarded as a very arduous and sometimes impossible task, especially for newer, emerging technology materials exhibiting limited long-range order. Nevertheless, information about atomic structural properties is very valuable for materials science and synthesis. For non-crystalline amorphous and nanoscale materials, using conventional structural determination methods is impossible. Reverse Monte Carlo (RMC) modeling is commonly used to derive models of materials from experimental diffraction data. Here, the latest developments in the fullrmc software package are discussed. Despite its name, fullrmc provides a very flexible modeling framework for solving atomic structures with many methods beyond RMC. The stochastic nature of fullrmc allows it to explore all possible dimensions and degrees of freedom for atomic modeling and create statistical solutions to match measurements. Differing versions of fullrmc are provided as open source or for cloud computing access. The latter includes a modern web-based graphical user interface that incorporates advanced computing and structure-building modules and machine-learning-based components. The main features of fullrmc are presented, including constraint types, boundary conditions, density shape functions and the two running modes: stochastic using a Monte Carlo algorithm and optimization using a genetic algorithm. Capabilities include tools for statistical, mesoscopic and nanoscopic approaches, atomic or coarse-grained models, and smart artificial-intelligence-ready loss functions.

SimELIT: A Novel GUI-Based Comprehensive Ion Trajectory Simulation Software for Mass Spectrometry.

Ion trajectory simulation in mass spectrometry systems from injection to detection is technically challenging but very important for better understanding the ion dynamics in instrument development. Here, we present SimELIT (Simulator of Eulerian and Lagrangian Ion Trajectories), a novel ion trajectory simulation platform. SimELIT is built upon a suite of multiphysics solvers compiled into OpenFOAM (an open-source numerical solver library particularly used for computational mechanics), with a simple web-based graphical user interface (GUI) allowing users to define the details of OpenFOAM cases and run simulations. SimELIT is a modular program and can provide extensions of physics (e.g., gas flows, electrodynamic fields) and thus enable ion trajectory simulations from the ion source to detector. The current version (SimELIT) provides two numerical solvers for ion trajectory simulations─(1) a Lagrangian particle tracker in vacuum and (2) a Eulerian ion density solver in background gas in the presence of electric fields. Here, we describe the architecture of SimELIT, including its use of Docker and the React Framework, and demonstrate the computation of ion trajectories of multiple m/z values in a static/linear voltage drop in vacuum (across a 1 m long flight tube). Further, the drift motion of ions under 1 Torr pressure conditions in a static background (N2) gas through a 20 V/cm static electric field is shown. The results produced from SimELIT were compared with SIMION and theoretical estimates. In addition, we report the computation of ion trajectories in electrodynamic fields within a planar FAIMS device operating at atmospheric pressure.

The Soft Compiler: A Web-Based Tool for the Design of Modular Pneumatic Circuits for Soft Robots

Developing soft circuits from individual soft logic gates poses a unique challenge: with increasing numbers of logic gates, the design and implementation of circuits lead to inefficiencies due to mathematically unoptimized circuits and wiring mistakes during assembly. It is therefore practically important to introduce design tools that support the development of soft circuits. We developed a web-based graphical user interface, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the Soft Compiler</i> , that accepts a user-defined robot behavior as a truth table to generate a mathematically optimized circuit diagram that guides the assembly of a soft fluidic circuit. We describe the design and experimental verification of three soft circuits of increasing complexity, using the Soft Compiler as a design tool and a novel pneumatic glove as an input interface. In one example, we reduce the size of a soft circuit from the original 11 logic gates to 4 logic gates while maintaining circuit functionality. The Soft Compiler is a web-based design tool for fluidic, soft circuits and published under an open-source MIT License.

Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples

Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.

BioExcel Building Blocks Workflows (BioBB-Wfs), an integrated web-based platform for biomolecular simulations.

We present BioExcel Building Blocks Workflows, a web-based graphical user interface (GUI) offering access to a collection of transversal pre-configured biomolecular simulation workflows assembled with the BioExcel Building Blocks library. Available workflows include Molecular Dynamics setup, protein-ligand docking, trajectory analyses and small molecule parameterization. Workflows can be launched in the platform or downloaded to be run in the users’ own premises. Remote launching of long executions to user's available High-Performance computers is possible, only requiring configuration of the appropriate access credentials. The web-based graphical user interface offers a high level of interactivity, with integration with the NGL viewer to visualize and check 3D structures, MDsrv to visualize trajectories, and Plotly to explore 2D plots. The server requires no login but is recommended to store the users’ projects and manage sensitive information such as remote credentials. Private projects can be made public and shared with colleagues with a simple URL. The tool will help biomolecular simulation users with the most common and repetitive processes by means of a very intuitive and interactive graphical user interface. The server is accessible at https://mmb.irbbarcelona.org/biobb-wfs.

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

Development of a graphical user interface software for the prediction of chronic kidney disease

Chronic Kidney Disease (CKD) is a severe kidney damage that is difficult to diagnose at the early stages due to the absence of clear symptoms. Late diagnosis of CKD is a common problem in low-income countries and is often associated with lower chances of survival. This study was designed to develop a user-friendly web-based graphical user interface (GUI) software for the prediction of CKD using artificial neural networks (ANNs). The model was developed using Python programming language and trained with 1200 instances of CKD datasets obtained from the University of California Irvine (UCI) machine learning repository. This dataset was split into 80% for training and 20% for testing achieved through an iterative process. A GUI software was developed based on the model using Django, an open-source python web development framework. The model achieved an accuracy of 95.83%, a precision of 100%, a specificity of 100%, and a sensitivity of 89.80%. The GUI software was effectively used to predict CKD and could be of immense benefit as a point of care application for early CKD prediction

Democratising "Microscopi": a 3D printed automated XYZT fluorescence imaging system for teaching, outreach and fieldwork.

Commercial fluorescence microscope stands and fully automated XYZt fluorescence imaging systems are generally beyond the limited budgets available for teaching and outreach. We have addressed this problem by developing "Microscopi", an accessible, affordable, DIY automated imaging system that is built from 3D printed and commodity off-the-shelf hardware, including electro-mechanical, computer and optical components. Our design features automated sample navigation and image capture with a simple web-based graphical user interface, accessible with a tablet or other mobile device. The light path can easily be switched between different imaging modalities. The open source Python-based control software allows the hardware to be driven as an integrated imaging system. Furthermore, the microscope is fully customisable, which also enhances its value as a learning tool. Here, we describe the basic design and demonstrate imaging performance for a range of easily sourced specimens.

Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework.

BackgroundData-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility.FindingsTo overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community.ConclusionThe integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.

Detailed patient-individual reporting of lymph node involvement in oropharyngeal squamous cell carcinoma with an online interface

Purpose/ObjectiveWhereas the prevalence of lymph node level (LNL) involvement in head & neck squamous cell carcinomas (HNSCC) has been reported, the details of lymphatic progression patterns are insufficiently quantified. In this study, we investigate how the risk of metastases in each LNL depends on the involvement of upstream LNLs, T-category, HPV status and other risk factors. Materials/MethodsWe retrospectively analyzed patients with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC) treated at a single institution, resulting in a dataset of 287 patients. For all patients, involvement of LNLs I-VII was recorded individually based on available diagnostic modalities (PET, MRI, CT, FNA) together with clinicopathological factors. To analyze the dataset, a web-based graphical user interface (GUI) was developed, which allows querying the number of patients with a certain combination of co-involved LNLs and tumor characteristics. ResultsThe full dataset and GUI is part of the publication. Selected findings are: Ipsilateral level IV was involved in 27% of patients with level II and III involvement, but only in 2% of patients with level II but not III involvement. Prevalence of involvement of ipsilateral levels II, III, IV, V was 79%, 34%, 7%, 3% for early T-category patients (T1/T2) and 85%, 50%, 17%, 9% for late T-category (T3/T4), quantifying increasing involvement with T-category. Contralateral levels II, III, IV were involved in 41%, 19%, 4% and 12%, 3%, 2% for tumors with and without midline extension, respectively. T-stage dependence of LNL involvement was more pronounced in HPV negative than positive tumors, but overall involvement was similar. Ipsilateral level VII was involved in 14% and 6% of patients with primary tumors in the tonsil and the base of tongue, respectively. ConclusionsDetailed quantification of LNL involvement in HNSCC depending on involvement of upstream LNLs and clinicopathological factors may allow for further personalization of CTV-N definition in the future.

Simulation of Virtual Redundant Sensor Models for Safety-Related Applications.

Applications of safety-related control systems demand reliable and credible inputs from physical sensors, therefore there is a need to extend their capabilities to provide a validated input with high availability. Our main idea is to insert virtual sensors between physical sensors and the control system’s logic. The created solution can validate the values of real sensors and with the use of multiple virtual sensors we can achieve high availability in addition, therefore our solution is entitled as a virtual redundant sensor. It works by the digital twin’s concept and uses fusion function to calculate validated results. The fusion function is used to transform the measured values from the physical sensors according to designed numerical models. The selection of a numerical model with assigned fusion functions can be performed via the WEB-based graphical user interface. Proposal of the numerical model is created and validated on the experimental workplace with emulation of physical sensors and MQTT integration (smart IoT sensors). The results of testing have shown that our solution can be applied to validate the values of physical sensors. Proposed fusion functions calculated results according to the selected model in all cases, while non-standard cases were handled according to our definition. In addition, the high availability concept with a group of two virtual sensors has proven fast recovery and availability of results for safety-related applications as well.