Simple Graphical Interface Research Articles

Real-Time Growth Kinetics Analysis of Macromolecular Assemblies in Cells with Single Molecule Resolution.

Single molecule fluorescence microscopy has the unique advantage to provide real-time information on the spatiotemporal assembly of individual protein complexes in cellular membranes. This includes the assembly of proteins into oligomer species of numerous copy numbers. However, there is a need for improved tracing analysis of the real-time growth kinetics of these assemblies in cells with single molecule resolution. Here, we present an automated analysis software to accurately measure the real-time kinetics of assembly of individual high-order oligomer complexes. Our software comes with a simple Graphical User Interface (GUI), is available as a source code and an executable, and can analyze a full data set of several hundred to thousand molecules in less than 2 minutes. Importantly, this software is suitable for the analysis of intracellular protein oligomers, whose stoichiometry is usually more difficult to quantify due to variability in signal detection in the different areas of the cell. We validated our method with simulated ground-truth data and time-lapse images of diffraction-limited oligomeric assemblies of BAX and BAK proteins on mitochondria of cells undergoing apoptosis. Our approach provides the broad community of biologists with a fast, user-friendly tool to trace the compositional evolution of macromolecular assemblies, and potentially model their growth for a deeper understanding of the structural and biophysical mechanisms underlying their functions.

Shear Strength Prediction of Slender Concrete Beams Reinforced with FRP Rebar Using Data-Driven Machine Learning Algorithms

Estimating the shear strength of a fiber-reinforced polymer (FRP)–reinforced-concrete (RC) beam is a complex task that depends on multiple design variables. The use of FRP bars has emerged as a promising alternative to diminish the corrosion problems that are associated with steel reinforcement in adverse environments; however, an accurate and reliable method of shear strength prediction is needed to ensure the economical use of materials and robust designs. Several optimized design equations are available in the literature; however, when utilizing these equations a substantial difference is observed between the predicted outcome (Vpred) and the experimental shear strength (Vexp) result. Therefore, this paper presented a novel approach toward implementing machine learning (ML) algorithms to accurately estimate the shear strength of FRP–RC beams. A large database that consisted of 302 shear test results on FRP-reinforced slender concrete beams without stirrup was collected from the literature to formulate the most efficient prediction model. The performance of each ML algorithm model was compared with the existing design provisions and models. The model interpretation was performed through feature importance analysis to explain the model output compared with a black box. The proposed data-driven ML models demonstrated a high level of accuracy and excellent performance and were superior to the existing shear strength models. In addition, a simple graphical user interface (GUI) was developed to aid practicing engineers when estimating shear strength without the need for complicated design procedures.

XRFitProc: A novel web‐based x‐ray fluorescence fitting system

AbstractX‐ray fluorescence (XRF) spectroscopy is a widely used technique in microscopy, spanning from biology to cultural heritage applications. Its purpose is to characterize qualitatively and quantitatively, the presence of elemental species in a sample. This is accomplished through fitting the acquired data to a Gaussian model, identifying which XRF lines and associated elements are present. As a result, 2D images of cumulative count‐rate maps associated with each element are produced. This procedure is not trivial to apply efficiently in a workflow, as it requires the user to be able to set a series of parameters (e.g., beam energy, background subtraction, etc.) on top of selecting XRF lines under investigation. Furthermore, users should easily and swiftly be able to change setup parameters and evaluate the effects on the results. In the present work, we introduce a web‐based application that allows users to load the XRF data, setup a fit and inspect the results interactively within a simple graphical user interface (GUI) that enables easily going back and forth from setup to result inspection. In particular, it is possible to quickly view the count‐rate maps and curve fitting simultaneously, on any single pixel spectra present in the images. The web‐application can be accessed locally by a web‐browser, but runs remotely on a cloud, freeing from the need of installing any software and will be made publicly available in the near future. At present, it has been designed to work on both conventional and sparse XRF data such as Compressive Sensing, in an embarrassingly parallel manner.

Creating GUIs in Python for Digital Humanities Projects

In this lesson, you will use Qt Designer and Python to design and implement a simple graphical user interface and application to merge PDF files. This lesson also demonstrates how to package the application for distribution to other personal computers.

Technical note: colab_zirc_dims: a Google Colab-compatible toolset for automated and semi-automated measurement of mineral grains in laser ablation–inductively coupled plasma–mass spectrometry images using deep learning models

Abstract. Collecting grain measurements for large detrital zircon age datasets is a time-consuming task, but a growing number of studies suggest such data are essential to understanding the complex roles of grain size and morphology in grain transport and as indicators for grain provenance. We developed the colab_zirc_dims Python package to automate deep-learning-based segmentation and measurement of mineral grains from scaled images captured during laser ablation at facilities that use Chromium targeting software. The colab_zirc_dims package is implemented in a collection of highly interactive Jupyter notebooks that can be run either on a local computer or installation-free via Google Colab. These notebooks also provide additional functionalities for dataset preparation and for semi-automated grain segmentation and measurement using a simple graphical user interface. Our automated grain measurement algorithm approaches human measurement accuracy when applied to a manually measured n=5004 detrital zircon dataset. Errors and uncertainty related to variable grain exposure necessitate semi-automated measurement for production of publication-quality measurements, but we estimate that our semi-automated grain segmentation workflow will enable users to collect grain measurement datasets for large (n≥5000) applicable image datasets in under a day of work. We hope that the colab_zirc_dims toolset allows more researchers to augment their detrital geochronology datasets with grain measurements.

New high-precision strong lensing modeling of Abell 2744

We present a new strong lensing (SL) model of the Hubble Frontier Fields (HFF) galaxy cluster Abell 2744, at z = 0.3072, by exploiting archival Hubble Space Telescope (HST) multiband imaging and Multi Unit Spectroscopic Explorer (MUSE) follow-up spectroscopy. The lens model considers 90 spectroscopically confirmed multiple images (from 30 background sources), representing the largest secure sample for this cluster field prior to the recently acquired James Webb Space Telescope (JWST) observations. The inclusion of the substructures within several extended sources as model constraints allowed us to accurately characterize the inner total mass distribution of the cluster and the position of the cluster critical lines. We included the lensing contribution of 225 cluster members, 202 of which are spectroscopically confirmed. We complemented this sample with 23 photometric member galaxies that are identified with a convolution neural network methodology with a high degree of purity. We also measured the internal velocity dispersion of 85 cluster galaxies, down to mF160W = 22, to independently estimate the role of the subhalo mass component in the lens model. We investigated the effect of the cluster environment on the total mass reconstruction of the cluster core with two different mass parameterizations. We considered the mass contribution from three external clumps, either based on previous weak lensing studies, or extended HST imaging of luminous members around the cluster core. In the latter case, the observed positions of the multiple images were better reproduced, with a remarkable accuracy of 0.​​″37, a factor of ∼2 smaller than previous lens models, which exploited the same HST and MUSE data sets. As part of this work, we developed and made publicly available a Strong Lensing Online Tool (SLOT) to exploit the predictive power and the full statistical information of this and future models, through a simple graphical interface. We plan to apply our new high-precision SL model to the first analysis of the Grism Lens-Amplified Survey from Space-JWST-Early Release Science (GLASS-JWST-ERS) program, specifically to measure the intrinsic physical properties of high-z galaxies from robust magnification maps.

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Today, diabetes is one of the most common chronic diseases in the world due to the people’s sedentary lifestyle which led to many health issues like heart attack, kidney frailer and blindness. Additionally, most of the people are unrealizable about the early-stage diabetes symptoms to prevent it. The above reasons were encouraging to develop a diabetes prediction system using machine learning techniques. The Pima Indian Diabetes Dataset (PIDD) was utilized for this framework as it is common and appropriate dataset in .CSV format. While there were not any duplicate or null values, however, some zero values were replaced, four outlier records were removed and data standardization were performed in the dataset. In addition, this project methodology divided into two phases of model selection. In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. Then six different algorithms (Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and Naïve Bayes) were applied. In the second phase, the four best performed algorithms (with best estimated parameters for each of them) were chosen and used as an input for the voting classifier, because it applies to find the best algorithm between a group of multiple options. The result was satisfying, and Random Forest was achieved 98.69% in second stage, while its accuracy level was 81.04% in the previous one and it utilized to predict diabetes via a simple graphic user interface.

MtFRC: depth-dependent resolution quantification of image features in 3D fluorescence microscopy.

Quantifying lateral resolution as a function of depth is important in the design of 3D microscopy experiments. However, for many specimens, resolution is non-uniform within the same optical plane because of factors such as tissue variability and differential light scattering. This precludes application of a simple resolution metric to the image as a whole. In such cases, it can be desirable to analyse resolution only within specific, well-defined features. An algorithm and software are presented to characterize resolution as a function of depth in features of arbitrary shape in 3D samples. The tool can be used to achieve an objective comparison between different preparation methods, imaging parameters, and optical systems. It can also inform the design of experiments requiring resolution of structures at a specific scale. The method is demonstrated by quantifying the improvement in resolution of two-photon microscopy over confocal in the central brain of Drosophila melanogaster. Measurement of image quality increases by tuning a single parameter, laser power, is also shown. An ImageJ plugin implementation is provided for ease of use via a simple Graphical User Interface, with outputs in table, graph, and colourmap formats. Software and source code are available at https://www.imperial.ac.uk/rowlands-lab/resources/.

Artefact removal from micrographs with deep learning based inpainting

We present a novel inpainting algorithm for microstructural image data using generative adversarial networks. This enables fast artefact removal via a simple graphical user interface.

The Training of Making Graphical User Interface (GUI) Using Python for Teachers and Students of Engineering Vocational School in Purwakarta

The purpose of this community service activity is to introduce the Python programming language to teachers and students of vocational school in Purwakarta exploiting an application to create a simple graphical user interface (GUI). Python is a programming language that is currently trending. The training was carried out in 3 stages, namely preparation, implementation and evaluation. As a result, participants were able to follow this training well and were able to create their own version of a simple GUI. Participants were also able to integrate their GUI with Arduino. After this activity, participants are expected to be able to explore how to make simple GUI using the modules that have been given.

INTERACTIVE GRAPHIC APPLICATION FOR IMAGE COLORATION

Nowadays we have thousands of products related to image adjusting adding filters, blurring, removing noise, etc. Also, we have applications related to image coloration, however, their functionality is quite narrow. With the help of the interactive graphic application for image coloration, it would be easier to adjust, particularly recolor, fully recolor, and even color black and white images from scratch. The application considers users’ choices by interactive instruments: texting the preferred color by some phrase “once in a blue moon”, choosing colors with a color picker, choosing a palette, choosing a palette with random colors, and adjusting a palette. All these instruments are applied to both color and black & white images. An example of using this application is in urban planning or design, where it is quite taught work for architects to make up with the idea of a new color palette for the particular project. For such cases, we eve provided functionality for fully coloring some raw project in form of black & white image. The paper has the description and the results of the creation of a graphic application, the basis of which are: windows graphical user interface, mathematical clustering method K-means, and a convolutional neural network. Using of clustering method K-means deserves a separate section, as there was used not the primary method, but modified – the applying of k-means to colors of an image, each iteration of the algorithm touches every pixel in the image, it is expensive for volumetric images, thus after analyzing different approaches, I decided to choose the so-called weighted K-means approach, in which each point C represents a certain number of pixels, the array of C point is called bins. In addition to the clustering method, the system uses the recurrent neural network to process text values or generate colors. As the result, user can connect to the system through a simple windows graphical interface that allows him to upload the image for further processing. The practical value of this work is the developed high-quality application which is ready for use and implementation in the relevant fields.

MVsim is a toolset for quantifying and designing multivalent interactions

Arising through multiple binding elements, multivalency can specify the avidity, duration, cooperativity, and selectivity of biomolecular interactions, but quantitative prediction and design of these properties has remained challenging. Here we present MVsim, an application suite built around a configurational network model of multivalency to facilitate the quantification, design, and mechanistic evaluation of multivalent binding phenomena through a simple graphical user interface. To demonstrate the utility and versatility of MVsim, we first show that both monospecific and multispecific multivalent ligand-receptor interactions, with their noncanonical binding kinetics, can be accurately simulated. Further, to illustrate the conceptual insights into multivalent systems that MVsim can provide, we apply it to quantitatively predict the ultrasensitivity and performance of multivalent-encoded protein logic gates, evaluate the inherent programmability of multispecificity for selective receptor targeting, and extract rate constants of conformational switching for the SARS-CoV-2 spike protein and model its binding to ACE2 as well as multivalent inhibitors of this interaction. MVsim and instructional tutorials are freely available at https://sarkarlab.github.io/MVsim/.

Codebase release 0.5 for HarmonicBalance.jl

HarmonicBalance.jl is a publicly available Julia package designed to simplify and solve systems of periodic time-dependent nonlinear ordinary differential equations. Time dependence of the system parameters is treated with the harmonic balance method, which approximates the system’s behaviour as a set of harmonic terms with slowly-varying amplitudes. Under this approximation, the set of all possible steady-state responses follows from the solution of a polynomial system. In HarmonicBalance.jl, we combine harmonic balance with contemporary implementations of symbolic algebra and the homotopy continuation method to numerically determine all steady-state solutions and their associated fluctuation dynamics. For the exploration of involved steady-state topologies, we provide a simple graphical user interface, allowing for arbitrary solution observables and phase diagrams. HarmonicBalance.jl is a free software available at https://github.com/NonlinearOscillations/HarmonicBalance.jl.

HarmonicBalance.jl: A Julia suite for nonlinear dynamics using harmonic balance

HarmonicBalance.jl is a publicly available Julia package designed to simplify and solve systems of periodic time-dependent nonlinear ordinary differential equations. Time dependence of the system parameters is treated with the harmonic balance method, which approximates the system’s behaviour as a set of harmonic terms with slowly-varying amplitudes. Under this approximation, the set of all possible steady-state responses follows from the solution of a polynomial system. In HarmonicBalance.jl, we combine harmonic balance with contemporary implementations of symbolic algebra and the homotopy continuation method to numerically determine all steady-state solutions and their associated fluctuation dynamics. For the exploration of involved steady-state topologies, we provide a simple graphical user interface, allowing for arbitrary solution observables and phase diagrams. HarmonicBalance.jl is a free software available at https://github.com/NonlinearOscillations/HarmonicBalance.jl.

A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.

Complex high-dimensional datasets that are challenging to analyze are frequently produced through ‘-omics’ profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in ‘-omics’ data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in ‘-omics’ data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected genes in a model to classify the three subtypes and compared the results to models built using all genes and all differentially expressed genes. Genes identified with the molecular feature selection tool performed better than the other models in this study in all comparison metrics: accuracy, precision, recall, and F1-score using a significantly smaller set of genes. In addition, we developed a simple graphical user interface for the molecular feature selection tool, which is available for free download. This user-friendly interface is a valuable tool for the identification of potential biomarkers in gene expression datasets and is an asset for biomarker discovery studies.

MuFinder: A program to determine and analyse muon stopping sites

Significant progress has recently been made in calculating muon stopping sites using density functional theory. The technique aims to address two of the most common criticisms of the muon-spin spectroscopy (μ+SR) technique, namely, where in the sample does the muon stop, and what is its effect on its local environment. We have designed and developed a program called MuFinder that enables users to carry out these calculations through a simple graphical user interface (GUI). The procedure for calculating muon sites by generating initial muon positions, relaxing the structures, and then clustering and analysing the resulting candidate sites, can be done entirely within the GUI. The local magnetic field at the muon site can also be computed, allowing the connection between the muon sites obtained and experiment to be made. MuFinder will make these computations significantly more accessible to non-experts and help to establish muon site calculations as a routine part of μ+SR experiments. Program summaryProgram Title: MuFinderCPC Library link to program files:https://doi.org/10.17632/pwwt7p9hv8.1Developer's repository link:https://gitlab.com/BenHuddart/mufinderLicensing provisions: GPLv3Programming language: PythonNature of problem: To automate the process of calculating muon stopping sites using density functional theory, thereby making these calculations accessible to non-experts.Solution method: A Python-based graphical user interface (GUI) through which users can calculate muon stopping sites using the structural relaxation method. The program makes use of newly-developed algorithms for generating candidate initial muon positions and for clustering muon positions obtained from the structural relaxations. Analysis of the muon sites, including calculation of the local dipolar magnetic field, is also possible within the GUI.

Mitigating mismatch power loss of series–parallel and total-cross-tied array configurations using novel enhanced heterogeneous hunger games search optimizer

The location of shaded or faulty Photovoltaic modules in the PV array has a negative impact on the harvested power from the entire array. To overcome this significant limitation, PV reconfiguration is a considerable technique developed via interchanging the PV modules’ location physically or electrically. By this inspiration, in this article, the authors propose a novel enhanced heterogeneous hunger games search optimizer (EHHGS) based PV reconfiguration. The innovated EHHGS introduces a modified variant for the basic hunger game search optimizer (HGS) to achieve a high diversity and robust exploitation of the optimal solutions. The EHHGS is applied to identify the optimal relocation for the shaded or faulty modules in two configurations of PV connected array: total-cross-tied array (TCT) and Series–parallel one (S–P). The proposed approach has applied symmetric and asymmetric connected PV arrays with dimensions of 9 × 9 and 10 × 8 throughout five different shade patterns. Moreover, for providing a flexible tool for the user/researcher to detect and observe the benefits achieved via the PV reconfiguration strategy, a simple graphical user interface (GUI) for the PV reconfiguration strategy of TCT or S–P PV connected array using meta-heuristic algorithms is designed. This implemented GUI can extend for any size of PV arrays, different optimization algorithms, and different connection schemes. The proposed EHHGS, HGS, and set of recent optimizers, including harris hawk optimizer (HHO), marine predators algorithm (MPA), and artificial ecosystem-based optimization (AEO), handle a new simplified objective function to boost the optimizer’s ability in catching the optimal modules’ location to alleviate the mismatched power in the studied arrays. Several statistical metrics are computed for providing an unbiased comparison. Through the comparisons, the proposed EHHGS exhibits superior performance. It achieves the best re-design for the considered arrays that helps in avoiding the mismatch losses in the cases of the partial shaded/faulty modules and enhances the power generated profiles. EHHGS enhances the power by percentages of 44.42%, 11.9%, 33.36%, 20. 86% and 13.17% compared to the TCT-connected system. In the case of the S–P connection, the proposed EHHGS generates 47.2% and 10.45%, 30.75%, 17.25%, and 26.27% higher power.

WebMILE

In recent years, we have seen the success of network representation learning (NRL) methods in diverse domains ranging from computational chemistry to drug discovery and from social network analysis to bioinformatics algorithms. However, each such NRL method is typically prototyped in a programming environment familiar to the developer. Moreover, such methods rarely scale out to large-scale networks or graphs. Such restrictions are problematic to domain scientists or end-users who want to scale a particular NRL method-of-interest on large graphs from their specific domain. In this work, we present a novel system, WebMILE to democratize this process. WebMILE can scale an unsupervised network embedding method written in the user's preferred programming language on large graphs. It provides an easy-to-use Graphical User Interface (GUI) for the end-user. The user provides the necessary input (embedding method file, graph, required packages information) through a simple GUI, and WebMILE executes the input network embedding method on the given input graph. WebMILE leverages a pioneering multi-level method, MILE (alternatively DistMILE if the user has access to a cluster), that can scale a network embedding method on large graphs. The language agnosticity is achieved through a simple Docker interface. In this demonstration, we will showcase how a domain scientist or end-user can utilize WebMILE to rapidly prototype and learn node embeddings of a large graph in a flexible and efficient manner - ensuring the twin goals of high productivity and high performance.

Centralized Management IoT Platform

On information technology topics, the word “innovation” is increasingly present every day. Regarding the Internet of Things (IoT) subject, it’s no different. Every year new IoT products and services are created and presented that allow users to make their lives easier and simpler, connecting people to devices remotely and automatically, generating mobility and operability of services through the heterogeneity of devices connected to the internet. Based on obstacles found in daily homes, this paperwork aims study the development of a unique and easy use platform. On this platform, it is possible to have IoT devices centralized on the same local network unit so that they can be managed and manipulated through a simple and intuitive graphical interface. Thus, management is unified and practical for any type of user who is interested in using this technology. In this thesis, good practices and the best solutions researched within the practice of IoT management were studied in different scenarios. Covering types of technologies, proposed architectures, configuration processes and compatibility analysis of features and functionality of different devices currently on the market. Thus, this work aims to present in detail the study of the prototype of a unified platform that allows configuring, monitoring, and managing the integration between heterogeneous devices currently on the market for residential users

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.