Open-source Plugin Research Articles

Future land use and land cover simulations with cellular automata-based artificial neural network: A case study over Delhi megacity (India)

According to United Nations projections, future global urban growth will mostly occur in Asian megacities. In this study, a Cellular Automata based Artificial Neural Network (CA-ANN) model is used to simulate the future land use and land cover (LULC) over Delhi megacity (India). Delhi, projected to become the world's most populated city by 2030, is an example of a data poor city in Asia, having millions of climate vulnerable people. The CA-ANN model of Modules for Land Change Simulation (MOLUSCE), an open-source plugin, is first tested to simulate the LULC for 2009. Based on good validation results-structural similarity (SSIM; 0.8288), overall accuracy (79.78 %), kappa index of agreement (KIA; 77.25 %), and minimum validation overall error (0.0379), the same model set-up is used to carry out LULC simulation for 2030. This model is found to be simple, efficient, and computationally less expensive tool, and can be used to model future LULCs with a minimal set of inputs, a constraint often found in data poor cities. Results show continued increase in built-up area from 38.3 % (2014) to 53.8 % (2030), at the expense of cultivable areas, forests, and wastelands. The study incorporates past and future LULC change trajectories to highlight the changing LULC dynamics of the megacity from 1977 to 2030. Rate of urban sprawl, calculated using compound annual growth rate (CAGR) is projected to be 2.51 % for 2014–2030, substantially higher than the estimates for 2006–2014 (0.62 %). Further, the past and future urban growth patterns for Delhi are found to mimic other big Asian cities. The database generated from the present study has wide applicability for scientific research community, governmental bodies, profit and non-profit organizations for topics concerning-future urban climate research, climate risk and adaption policy frameworks, climate finance budgeting, future town planning, etc.

Introducing MapSWAT: An open source QGIS plugin integrated with google earth engine for efficiently generating ready-to-use SWAT+ input maps.

Hydrological modeling plays a significant role in addressing global water challenges. Streamlining the implementation of hydrological models enables more people to participate in designing solutions to water issues. This study presents the development and workflow of a QGIS-based tool called MapSWAT, designed to facilitate the application of the Soil and Water Assessment Tool (SWAT+). MapSWAT is an open-source plugin written in Python for collecting and producing ready-to-use SWAT + input maps. It can generate SWAT + input maps for any location globally, including limited data regions, by connecting to the Google Earth Engine. The potential for and feasibility of MapSWAT have been demonstrated through its application in a case study in southeast Spain. The results indicate that MapSWAT is a useful tool for efficiently generating SWAT + input maps that optimizes the hydrological modeling process with SWAT+. The MapSWAT software is freely and publicly available at https://github.com/AdrLBallesteros/MapSWAT.

A mesoscale computational approach to predict ABD matrix of thin woven composites

The ABD matrix is a fundamental method to characterize the overall stiffness behavior of laminated composite structures. Although classical laminate theory has been widely used, it has limitations in predicting the ABD matrix for woven composites. To address this issue, this paper presents a mesoscale homogenization approach aimed at computing the ABD matrix for thin woven composites accurately. The mesoscale representative volume element (RVE) of the woven composite is generated using TexGen and imposed with periodic boundary conditions to enforce the Kirchhoff thin plate assumption. The ABD matrix is computed by conducting six separate finite element simulations, each representing one simple in-plane or out-of-plane deformation in a specified direction. Moreover, to facilitate the implementation of the method, an open-source plugin tool was developed within ABAQUS CAE, automating the ABD matrix calculation for various types of woven composites including 2D weave, 3D weave, and multiaxial. The accuracy of the proposed method was validated through benchmark calculations against existing literature results.

A new bio imagery user-friendly tool for automatic morphometry measurement on muscle cell cultures and histological sections

TRUEFAD (TRUE Fiber Atrophy Distinction) is a bioimagery user-friendly tool developed to allow consistent and automatic measurement of myotube diameter in vitro, muscle fiber size and type using rodents and human muscle biopsies. This TRUEFAD package was set up to standardize and dynamize muscle research via easy-to-obtain images run on an open-source plugin for FIJI. We showed here both the robustness and the performance of our pipelines to correctly segment muscle cells and fibers. We evaluated our pipeline on real experiment image sets and showed consistent reliability across images and conditions. TRUEFAD development makes possible systematical and rapid screening of substances impacting muscle morphology for helping scientists focus on their hypothesis rather than image analysis.

DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map.

We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data. The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown proteins. We implemented our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All supplemental materials are available at osf.io/5tx4q.

Performance Analysis of Devops Practice Implementation Of CI/CD Using Jenkins

Continuous Integration and Continuous Delivery (CI/CD) are methods used in agile development to automate and speed up the process of building, testing, and validating services. To support and simplify all development and deployment processes, several methods such as containerized and CI/CD automation are needed. In this research, a DevOps Practice is carried out which includes process integration, deployment, and testing automatically using a tool called Jenkins. These tools are open source automation servers to help the Continuous Integration and Continuous Deployment process. Jenkins is equipped with various open source plugins that can be used to simplify and assist CI/CD automation and testing processes. The implementation of CI/CD in performance testing makes the testing process integrated, automated, and can be run on a regular basis.

Dusk2Dawn: an EEGLAB plugin for automatic cleaning of whole-night sleep electroencephalogram using Artifact Subspace Reconstruction.

Whole-night sleep electroencephalogram (EEG) is plagued by several types of large-amplitude artifacts. Common approaches to remove them are fraught with issues: channel interpolation, rejection of noisy intervals, and independent component analysis are time-consuming, rely on subjective user decisions, and result in signal loss. Artifact Subspace Reconstruction (ASR) is an increasingly popular approach to rapidly and automatically clean wake EEG data. Indeed, ASR adaptively removes large-amplitude artifacts regardless of their scalp topography or consistency throughout the recording. This makes ASR, at least in theory, a highly-promising tool to clean whole-night EEG. However, ASR crucially relies on calibration against a subset of relatively clean "baseline" data. This is problematic when the baseline changes substantially over time, as in whole-night EEG data. Here we tackled this issue and, for the first time, validated ASR for cleaning sleep EEG. We demonstrate that ASR applied out-of-the-box, with the parameters recommended for wake EEG, results in the dramatic removal of slow waves. We also provide an appropriate procedure to use ASR for automatic and rapid cleaning of whole-night sleep EEG data or any long EEG recording. Our procedure is freely available in Dusk2Dawn, an open-source plugin for EEGLAB.

SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data

Decentralized solar PhotoVoltaic (PV) is one of the most promising energy sources for cities and individuals pursuing energy self-sufficiency. Especially, the already available rooftop surfaces are a major contributor to push for rooftop mounted PV systems. However, accurate PV potential estimation of individual buildings is still a challenging task since many parameters must be considered such as meteorological factors, panel technology, geographical location, available roof surface area, surface azimuth and tilt angle. In this study, we created an efficient approach that can be used for roof surface's PV potential estimation based on point cloud data and capable of processing various scales from single building to city scale. In the proposed approach, each roof surface's features were utilized for PV potential estimation by employing the PVGIS database. PV potential estimation was carried out on daily, monthly, and annual periods to provide a better estimation. Also, we developed a flexible and easy to use open-source plugin based on the QGIS software for rooftop mounted PV potential estimation capable of estimating every roof surface's PV potential. The method was tested on 80 buildings selected from ROOFN3D dataset. The proposed approach achieved an overall accuracy of 84% and an F1 score of 0.92.

Modeling dissolved and particulate organic carbon dynamics at basin and sub-basin scales

Dissolved organic carbon (DOC) and particulate organic carbon (POC) play a fundamental role in biogeochemical cycles of freshwater ecosystems. However, the lack of readily available distributed models for carbon export has limited the effective management of organic carbon fluxes from soils, through river networks and to receiving marine waters. We develop a spatially semi-distributed mass balance modeling approach to estimate organic carbon flux at a sub-basin and basin scales, using commonly available data, to allow stakeholders to explore the impacts of alternative river basin management scenarios and climate change on riverine DOC and POC dynamics. Data requirements, related to hydrological, land-use, soil and precipitation characteristics are easily retrievable from international and national databases, making it appropriate for data-scarce basins. The model is built as an open-source plugin for QGIS and can be easily integrated with other basin scale decision support models on nutrient and sediment export.We tested the model in Piave river basin, in northeast Italy. Results show that the model reproduces spatial and temporal changes in DOC and POC fluxes in relation to changes in precipitation, basin morphology and land use across different sub-basins. For example, the highest DOC export were associated with both urban and forest land use classes and during months of elevated precipitation. We used the model to evaluate alternative land use scenarios and the impact of climate on basin level carbon export to Mediterranean.

Tracking unlabeled cancer cells imaged with low resolution in wide migration chambers via U-NET class-1 probability (pseudofluorescence)

Cell migration is a pivotal biological process, whose dysregulation is found in many diseases including inflammation and cancer. Advances in microscopy technologies allow now to study cell migration in vitro, within engineered microenvironments that resemble in vivo conditions. However, to capture an entire 3D migration chamber for extended periods of time and with high temporal resolution, images are generally acquired with low resolution, which poses a challenge for data analysis. Indeed, cell detection and tracking are hampered due to the large pixel size (i.e., cell diameter down to 2 pixels), the possible low signal-to-noise ratio, and distortions in the cell shape due to changes in the z-axis position. Although fluorescent staining can be used to facilitate cell detection, it may alter cell behavior and it may suffer from fluorescence loss over time (photobleaching).Here we describe a protocol that employs an established deep learning method (U-NET), to specifically convert transmitted light (TL) signal from unlabeled cells imaged with low resolution to a fluorescent-like signal (class 1 probability). We demonstrate its application to study cancer cell migration, obtaining a significant improvement in tracking accuracy, while not suffering from photobleaching. This is reflected in the possibility of tracking cells for three-fold longer periods of time. To facilitate the application of the protocol we provide WID-U, an open-source plugin for FIJI and Imaris imaging software, the training dataset used in this paper, and the code to train the network for custom experimental settings.

The predictive value of YAP-1 and POU2F3 for the efficacy of immuno-chemotherapy in extensive-stage SCLC patients

IntroductionRecently, several clinical trials of immunotherapy for extensive-stage small-cell lung cancer (ES-SCLC) have shown limited benefits because of unselected patients. Thus, we aimed to explore whether YES-associated protein 1 (YAP-1) and POU domain class 2 transcription factor 3 (POU2F3) could identify SCLC patients with durable benefits from immunotherapy as potential biomarkers. MethodsWe performed IHC of YAP-1 and POU2F3, and RNA-seq on tissues of ES- SCLC patients. An open-source plugin based on IHC-profiler was conducted to calculate the expression levels of YAP-1 and POU2F3. ResultsPatients with ES-SCLC were retrospectively investigated in the Guangdong Provincial People's Hospital from January 2018 to July 2021, and 21 patients whoever received atezolizumab plus etoposide/carboplatin (ECT) regimen also had tissue samples reachable. The median IHC-score of YAP-1 in responders (CR/PR patients) was significantly lower than in nonresponders (SD/PD patients) at 13.97 (95% CI: 8.97–16.30) versus 23.72 (95% CI: 8.13–75.40). The IHC-score of YAP-1 and PFS showed a negative correlation by Spearman (r=-0.496). However, POU2F3 did not show a correlation with efficacy. Besides, patients with YAP-1 high expression had IL6, MYCN, and MYCT1 upregulated, while analysis of immune cell infiltration only showed that M0 macrophages were significantly higher. ConclusionsThe expression of YAP-1 negatively correlated with the efficacy of ECT in ES-SCLC patients while POU2F3 did not reveal the predictive value. However, prospective investigations with a large sample size are needed.

Comparative Study of Moodle Plugins to Facilitate the Adoption of Computer-Based Assessments

The use of Learning Management Systems (LMS) has had rapid growth over the last decades. Great efforts have been recently made to assess online students’ performance level, due to the COVID-19 pandemic. Faculty members with limited experience in the use of LMS such as Moodle, Edmodo, MOOC, Blackboard and Google Classroom face challenges creating online tests. This paper presents a descriptive and comparative study of the existing plugins used to import questions into Moodle, classifying them according to the necessary computing resources. Each of the classifications were compared and ranked, and features such as the support for gamification and the option to create parameterised questions are explored. Parameterised questions can generate a large number of different questions, which is very useful for large classes and avoids fraudulent behaviour. The paper outlines an open-source plugin developed by the authors: FastTest PlugIn, recently approved by Moodle. FastTest PlugIn is a promising alternative to mitigate the detected limitations in analysed plugins. FastTest PlugIn was validated in seminars with 230 faculty members, obtaining positive results about expectations and potential recommendations. The features of the main alternative plugins are discussed and compared, describing the potential advantages of FastTest PlugIn.

Automatic intra-subject registration and fusion of multimodal cochlea 3D clinical images.

BackgroundThe postoperative imaging assessment of Cochlear Implant (CI) patients is imperative. The main obstacle is that Magnetic Resonance imaging (MR) is contraindicated or hindered by significant artefacts in most cases with CIs. This study describes an automatic cochlear image registration and fusion method that aims to help radiologists and surgeons to process pre-and postoperative 3D multimodal imaging studies in cochlear implant (CI) patients.Methods and findingsWe propose a new registration method, Automatic Cochlea Image Registration (ACIR-v3), which uses a stochastic quasi-Newton optimiser with a mutual information metric to find 3D rigid transform parameters for registration of preoperative and postoperative CI imaging. The method was tested against a clinical cochlear imaging dataset that contains 131 multimodal 3D imaging studies of 41 CI patients with preoperative and postoperative images. The preoperative images were MR, Multidetector Computed Tomography (MDCT) or Cone Beam Computed Tomography (CBCT) while the postoperative were CBCT. The average root mean squared error of ACIR-v3 method was 0.41 mm with a standard deviation of 0.39 mm. The results were evaluated quantitatively using the mean squared error of two 3D landmarks located manually by two neuroradiology experts in each image and compared to other previously known registration methods, e.g. Fast Preconditioner Stochastic Gradient Descent, in terms of accuracy and speed.ConclusionsOur method, ACIR-v3, produces high resolution images in the postoperative stage and allows for visualisation of the accurate anatomical details of the MRI with the absence of significant metallic artefacts. The method is implemented as an open-source plugin for 3D Slicer tool.

Cell-TypeAnalyzer: A flexible Fiji/ImageJ plugin to classify cells according to user-defined criteria.

Fluorescence microscopy techniques have experienced a substantial increase in the visualization and analysis of many biological processes in life science. We describe a semiautomated and versatile tool called Cell-TypeAnalyzer to avoid the time-consuming and biased manual classification of cells according to cell types. It consists of an open-source plugin for Fiji or ImageJ to detect and classify cells in 2D images. Our workflow consists of (a) image preprocessing actions, data spatial calibration, and region of interest for analysis; (b) segmentation to isolate cells from background (optionally including user-defined preprocessing steps helping the identification of cells); (c) extraction of features from each cell; (d) filters to select relevant cells; (e) definition of specific criteria to be included in the different cell types; (f) cell classification; and (g) flexible analysis of the results. Our software provides a modular and flexible strategy to perform cell classification through a wizard-like graphical user interface in which the user is intuitively guided through each step of the analysis. This procedure may be applied in batch mode to multiple microscopy files. Once the analysis is set up, it can be automatically and efficiently performed on many images. The plugin does not require any programming skill and can analyze cells in many different acquisition setups.

Development and Implementation of Electronic Library for Protection of Copyrights

Today, many different publications such as journals and books are presented in electronic environment. Copyright concept is still discussed about the distribution of the books and paid publications digitally. The main objective of electronic library application to be developed is to display a certain part of the books and publications including the information that the users intend to reach. In this study, such operations as the search of phrases within various portable document format (PDF) documents like e-books, theses and technical reports or on the bookmarks of the documents, and the generation of its results as a PDF document have been showed. It has been viewed that the phrase search process gives more accurate results than the search on bookmarks, but is slower. As a result of the studies conducted on PDF documents specially created according to parameters such as file size, number of pages, content type and number of bookmarks, it has been observed that these parameters affect search performance at different degrees. In addition, an open source plug-in that can perform these operations has been also developed for the electronic library application. The plug-in developed independently of electronic library application in Java programming language can easily be added to the web applications. Apart from the plug-in, there is also a lot of information required for users to create and manage their own PDF documents.

Volumetric morphometry reveals spindle width as the best predictor of mammalian spindle scaling.

The function of cellular structures at the mesoscale is dependent on their geometry and proportionality to cell size. The mitotic spindle is a good example why length and shape of intracellular organelles matter. Spindle length determines the distance over which chromosomes will segregate, and spindle shape ensures bipolarity. While we still lack a systematic and quantitative understanding of subcellular morphology, new imaging techniques and volumetric data analysis promise novel insights into scaling relations across different species. Here, we introduce Spindle3D, an open-source plug-in that allows for the quantitative, consistent, and automated analysis of 3D fluorescent data of spindles and chromatin. We systematically analyze different mammalian cell types, including somatic cells, stem cells, and one- and two-cell embryos, to derive volumetric relations of spindle, chromatin, and the cell. Taken together, our data indicate that mitotic spindle width is a robust indicator of spindle volume, which correlates linearly with chromatin and cell volume both within single cell types and across mammalian species.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.

Alchemical Transfer Approach to Absolute Binding Free Energy Estimation.

The alchemical transfer method (ATM) for the calculation of standard binding free energies of noncovalent molecular complexes is presented. The method is based on a coordinate displacement perturbation of the ligand between the receptor binding site and the explicit solvent bulk and a thermodynamic cycle connected by a symmetric intermediate in which the ligand interacts with the receptor and solvent environments with equal strength. While the approach is alchemical, the implementation of the ATM is as straightforward as that for physical pathway methods of binding. The method is applicable, in principle, with any force field, as it does not require splitting the alchemical transformations into electrostatic and nonelectrostatic steps, and it does not require soft-core pair potentials. We have implemented the ATM as a freely available and open-source plugin of the OpenMM molecular dynamics library. The method and its implementation are validated on the SAMPL6 SAMPLing host-guest benchmark set. The work paves the way to streamlined alchemical relative and absolute binding free energy implementations on many molecular simulation packages and with arbitrary energy functions including polarizable, quantum-mechanical, and artificial neural network potentials.

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

The advent of Geographical Information Systems (GIS) has changed the way people think and interact with the world. The main objectives of this paper are: (i) to provide an overview of 10 years (2010–2020) regarding the creation/development of GIS open-source applications; and (ii) to evaluate the GIS open-source plugins for environmental science. In the first objective, we evaluate the publications regarding the development of GIS open-source geospatial software in the last 10 years, considering desktop, web GIS and mobile applications, so that we can analyze the impact of this type of application for different research areas. In the second objective, we analyze the development of GIS open-source applications in the field of environmental sciences (with more focus on QGIS plugins) in the last 10 years and discuss the applicability and usability of these GIS solutions in different environmental domains. A bibliometric analysis was performed using Web of Science database and VOSViewer software. We concluded that, in general, the development of GIS open-source applications has increased in the last 10 years, especially GIS mobile applications, since the big data and Internet of Things (IoT) era, which was expected given the new advanced technologies available in every area, especially in GIS.

Efficient 3d finite element modeling of cyclic elasto-plastic rolling contact

Railway rails accumulate large plastic deformations due to cyclic rolling contact loading. The plastic deformations alter the rail geometry, affect material behavior, and cause crack formation and growth. The complex interactions between these phenomena require high fidelity simulations to be understood. 3d finite element simulations are accurate, but their computational cost limits the possible number of simulated cycles. We propose a cyclic finite element simulation in which the wheel and rail remain in contact throughout the simulation. It uses periodic boundary conditions, shadow elements, and model reductions. Compared to previous work, it is 25 times faster. The method is available as an open-source plugin to Abaqus, enabling other researchers to study rolling contact loading coupled with large plastic deformations.