Haxe as a Swiss knife for bioinformatic applications: the SeqPHASE case story.

Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software.

MOrgAna: accessible quantitative analysis of organoids with machine learning.

Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.

MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations.

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

Finback: a web-based data collection system at SSRF biological macromolecular crystallography beamlines.

An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with a user-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.

ProSPyX: software for post-processing images of X-ray ptychography with spectral capabilities.

X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron.

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

BACKGROUND: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. OBJECTIVE: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. METHODS: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. RESULTS: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. CONCLUSIONS: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production.

The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a healthy food is promising and environmentally friendly. This study used three machine learning techniques to identify important cultivation parameters and their hidden interrelationships and optimize the biomass yield of Spirulina grown in seawater-based media. Through optimization of hyperparameters and features, eXtreme Gradient Boosting, along with the recursive feature elimination (RFE) model demonstrated optimal performance and identified 28 important features. Among them, illumination intensity and initial pH value were critical determinants of biomass, which impacted other features. Specifically, high initial pH values (>9.0) mainly increased biomass but also increased nutrient sedimentation and ammonia (NH3) losses. Both batch and continuous additions could decrease nutrient losses by increasing their availability in the seawater-based media. When illumination intensity exceeded 200 µmol photons/m2/s, it amplified the growth of Spirulina by mitigating the light attenuation caused by a high initial inoculum level and counteracted the negative effect of low temperature (<25 °C). In large-scale cultivation, production efficiency would be reduced if illumination was not maintained at a high level. High salinity and sodium bicarbonate (NaHCO3) addition promoted carbohydrate accumulation, but suitable dilution could keep the required protein content in Spirulina with relatively low media and production costs. These findings reveal the interactive influence of cultivation parameters on biomass yield and help us determine the optimal cultivation conditions for large-scale cultivation of Spirulina-based seawater system based on a developed graphical user interface website.

CHalf: Folding Stability Made Simple.

The structure of a protein defines its function and integrity and correlates with the protein folding stability (PFS). Quantifying PFS allows researchers to assess differential stability of proteins in different disease or ligand binding states, providing insight into protein efficacy and potentially serving as a metric of protein quality. There are a number of mass spectrometry (MS)-based methods to assess PFS, such as Thermal Protein Profiling (TPP), Stability of Proteins from Rates of Oxidation (SPROX), and Iodination Protein Stability Assay (IPSA). Despite the critical value that PFS studies add to the understanding of mechanisms of disease and treatment development, proteomics research is still primarily dominated by concentration-based studies. We found that a major reason for the lack of PFS studies is the lack of a user-friendly data processing tool. Here we present the first user-friendly software, CHalf, with a graphical user interface for calculating PFS. Besides calculating site-specific PFS of a given protein from chemical denature folding stability assays, CHalf is also compatible with thermal denature folding stability assays. CHalf also includes a set of data visualization tools to help identify changes in PFS across protein sequences and in between different treatment conditions. We expect the introduction of CHalf to lower the barrier of entry for researchers to investigate PFS, promoting the usage of PFS in studies. In the long run, we expect this increase in PFS research to accelerate our understanding of the pathogenesis and pathophysiology of disease.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments.

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).

GUIDE: A GUI for automated quantum chemistry calculations.

The accuracy of quantum mechanics (QM) simulations depends heavily on the quality of initial input files. Despite the popularity of QM simulation packages, achieving precise results still heavily relies on the user's proficiency in preparing the QM simulation systems. In this work, we present an easy-to-use tool called GUIDE, a YASARA plugin to assist researchers in quantum chemistry workflow automation using ORCA and MOPAC simulation packages. GUIDE lets users compute complex QM calculation workflows via an automated graphical window system. It allows for a more integrated and streamlined research process, as researchers can easily access all the necessary tools within one software without switching between multiple programs. This tool can save time and increase efficiency in computational chemistry methods. GUIDE is written in Python and is freely available for download at https://github.com/YAMACS-SML/GUIDE. The plugin is released under a GPL-3.0 license and is supported on Windows and Linux.

An integrated approach to determine the abundance, mutation rate and phylogeny of the SARS-CoV-2 genome.

The analysis of the SARS-CoV-2 genome datasets has significantly advanced our understanding of the biology and genomic adaptability of the virus. However, the plurality of advanced sequencing datasets-such as short and long reads-presents a formidable computational challenge to uniformly perform quantitative, variant or phylogenetic analysis, thus limiting its application in public health laboratories engaged in studying epidemic outbreaks. We present a computational tool, Infectious Pathogen Detector (IPD), to perform integrated analysis of diverse genomic datasets, with a customized analytical module for the SARS-CoV-2 virus. The IPD pipeline quantitates individual occurrences of 1060 pathogens and performs mutation and phylogenetic analysis from heterogeneous sequencing datasets. Using IPD, we demonstrate a varying burden (5.055-999655.7 fragments per million) of SARS-CoV-2 transcripts across 1500 short- and long-read sequencing SARS-CoV-2 datasets and identify 4634 SARS-CoV-2 variants (~3.05 variants per sample), including 449 novel variants, across the genome with distinct hotspot mutations in the ORF1ab and S genes along with their phylogenetic relationships establishing the utility of IPD in tracing the genome isolates from the genomic data (as accessed on 11 June 2020). The IPD predicts the occurrence and dynamics of variability among infectious pathogens-with a potential for direct utility in the COVID-19 pandemic and beyond to help automate the sequencing-based pathogen analysis and in responding to public health threats, efficaciously. A graphical user interface (GUI)-enabled desktop application is freely available for download for the academic users at http://www.actrec.gov.in/pi-webpages/AmitDutt/IPD/IPD.html and for web-based processing at http://ipd.actrec.gov.in/ipdweb/ to generate an automated report without any prior computational know-how.

Climate-ecosystem modelling made easy: The Land Sites Platform.

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

Initial experience with the graphical user interface for laser parameters setting of a new thulium fibre laser source device for urinary pathologies treatment.

BACKGROUND AND OBJECTIVE: We aimed to evaluate the concordance between the pre-settings ranges of thulium fibre laser (TFL) (Coloplast TFL Drive, Denmark) with easy-to-use graphical user interface and the laser settings used by a high-volume endo-urologist during surgical procedures. MATERIALS AND METHODS: In October 2022, we prospectively collected data of 67 patients who underwent TFL Drive (Coloplast, Denmark) for the management of urinary stones. Urothelial tumour (upper tract urinary cancer (UTUC) and bladder) 200 and 150 µm laser fibres were used for procedures. Stones characteristics (size and density) tumours and stenosis localizations, laser-on time (LOT), and laser settings were recorded. We also assessed the ablation speed (mm3/s), laser power (W), and Joules/mm3 values for each lithotripsy. RESULTS: A total 67 patients took part in the study. Median age was 52 (15-81) years. 55 (82%), 8 (12%), and 4 (6%) patients presented urinary stones, urothelial tumour, and stenosis, respectively. Median stone volume was 438 (36-6027) mm3 and median density was 988 (376-2000) HU. Median pulse energy was 0.6 (0.3-1.2), 0.8 (0.5-1) and 1 J for urinary stones, urothelial tumour and stenosis respectably. Endoscopically stone-free rate was 89%. Graphical user interface and surgeon accordance with the safety range were observed in 93.2%, 100% and 100% for urinary stones, UTUC and stenosis, respectively. CONCLUSION: During endoscopic procedures for urinary stones treatment, it is frequently needed to change laser parameters. These new TFL and GUI technology parameters remained in the pre-set security range in 94.1% of procedures.

Machine learning-enabled optimization of extrusion-based 3D printing.

Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.

Evaluating the usability of a co-designed power assisted exercise graphical user interface for people with stroke.

BACKGROUND: Digital advancement of power assisted exercise equipment will advance exercise prescription for people with stroke (PwS). This article reports on the remote usability evaluation of a co-designed graphical user interface (GUI) and denotes an example of how video-conference software can increase reach to participants in the testing of rehabilitation technologies. The aim of this study was to evaluate the usability of two sequential versions of the GUI. METHODS: We adopted a mixed methods approach. Ten professional user (PU) (2M/8F) and 10 expert user (EU) participants (2M/8F) were recruited. Data collection included a usability observation, a 'think aloud' walk through, task completion, task duration and user satisfaction as indicated by the Post Study System Usability Questionnaire (PSSUQ). Identification of usability issues informed the design of version 2 which included an additional submenu. Descriptive analysis was conducted upon usability issues and number of occurrences detected on both versions of the GUI. Inferential analysis enabled comparison of task duration and PSSUQ data between the PU and EU groups. RESULTS: Analysis of the 'think aloud' walkthrough data enabled identification of 22 usability issues on version 1 from a total of 100 usability occurrences. Task completion for all tasks was 100%. Eight usability issues were directly addressed in the development of version 2. Two recurrent and 24 new usability issues were detected in version 2 with a total of 86 usability occurrences. Paired two tailed T-tests on task duration data indicated a significant decrease amongst the EU group for task 1.1 on version 2 (P = 0.03). The mean PSSUQ scores for version 1 was 1.44 (EU group) and 1.63 (PU group) compared with 1.40 (EU group) and 1.41 (PU group) for version 2. CONCLUSIONS: The usability evaluation enabled identification of usability issues on version 1 of the GUI which were effectively addressed on the iteration of version 2. Testing of version 2 identified usability issues within the new submenu. Application of multiple usability evaluation methods was effective in identifying and addressing usability issues in the GUI to improve the experience of PAE for PwS. The use of video-conference software to conduct synchronous, remote usability testing is an effective alternative to face to face testing methods.

Pactolo Bar: An Approach to Mitigate the Midas Touch Problem in Non-Conventional Interaction.

New ways of interacting with computers is driving research, which is motivated mainly by the different types of user profiles. Referred to as non-conventional interactions, these are found with the use of hands, voice, head, mouth, and feet, etc. and these interactions occur in scenarios where the use of mouse and keyboard would be difficult. A constant challenge in the adoption of new forms of interaction, based on the movement of pointers and the selection of interface components, is the Midas Touch (MT) problem, defined as the involuntary action of selection by the user when interacting with the computer system, causing unwanted actions and harming the user experience during the usage process. Thus, this article aims to mitigate the TM problem in interaction with web pages using a solution centered on the Head Tracking (HT) technique. For this purpose, a component in the form of a Bar was developed and inserted on the left side of the web page, called the Pactolo Bar (PB), in order to enable or disable the clicking event during the interaction process. As a way of analyzing the effectiveness of PB in relation to TM, two stages of tests were carried out based on the collaboration of voluntary participants. The first step aims to find the data that would lead to the best configuration of the BP, while the second step aims to carry out a comparative analysis between the PB solution and the eViacam software, whose use is also focused on the HT technique. The results obtained from the use of PB were considered promising, since the analysis of quantitative data points to a significant prevention of involuntary clicks in the iteration interface and the analysis of qualitative data showed the development of a better user experience due to the ease of use, which can be noticed in elements such as the PB size, the triggering mechanism, and its positioning in the graphical interface. This study benefits in the context of the user experience, because, when using non-conventional interactions, basic items such as aspects of the graphic elements, and interaction events raise new studies that seek to mitigate the problem of the Midas Touch.

Development of an Open-Source Injection Mold Monitoring System.

In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors' knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns.

A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures.

A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.

An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology.

Deep learning techniques using convolutional neural networks (CNNs) have been successfully developed for various medical image analysis tasks. However, the skills to understand and develop deep learning models are not usually taught during radiology training, which constitutes a barrier for radiologists looking to integrate machine learning (ML) into their research or clinical practice. In this work, we developed and evaluated an educational graphical user interface (GUI) to construct CNNs for teaching deep learning concepts to radiology trainees. The GUI was developed in Python using the PyQt and PyTorch frameworks. The functionality of the GUI was demonstrated through a binary classification task on a dataset of MR images of the brain. The usability of the GUI was assessed through 45-min user testing sessions with 5 neuroradiologists and neuroradiology fellows, assessing mean task completion times, the System Usability Scale (SUS), and a qualitative questionnaire as metrics. Task completion times were compared against a ML expert who performed the same tasks. After a 20-min introduction to CNNs and a walkthrough of the GUI, users were able to perform all assigned tasks successfully. There was no significant difference in task completion time compared to a ML expert. The educational GUI achieved a score of 82.5 on the SUS, suggesting that the system is highly usable. Users indicated that the GUI seems useful as an educational tool to teach ML topics to radiology trainees. An educational GUI allows interactive teaching in ML that can be incorporated into radiology training.

MetaAnalyst: a user-friendly tool for metagenomic biomarker detection and phenotype classification.

BACKGROUND: Many metagenomic studies have linked the imbalance in microbial abundance profiles to a wide range of diseases. These studies suggest utilizing the microbial abundance profiles as potential markers for metagenomic-associated conditions. Due to the inevitable importance of biomarkers in understanding the disease progression and the development of possible therapies, various computational tools have been proposed for metagenomic biomarker detection. However, most existing tools require prior scripting knowledge and lack user friendly interfaces, causing considerable time and effort to install, configure, and run these tools. Besides, there is no available all-in-one solution for running and comparing various metagenomic biomarker detection simultaneously. In addition, most of these tools just present the suggested biomarkers without any statistical evaluation for their quality. RESULTS: To overcome these limitations, this work presents MetaAnalyst, a software package with a simple graphical user interface (GUI) that (i) automates the installation and configuration of 28 state-of-the-art tools, (ii) supports flexible study design to enable studying the dataset under different scenarios smoothly, iii) runs and evaluates several algorithms simultaneously iv) supports different input formats and provides the user with several preprocessing capabilities, v) provides a variety of metrics to evaluate the quality of the suggested markers, and vi) presents the outcomes in the form of publication quality plots with various formatting capabilities as well as Excel sheets. CONCLUSIONS: The utility of this tool has been verified through studying a metagenomic dataset under four scenarios. The executable file for MetaAnalyst along with its user manual are made available at https://github.com/mshawaqfeh/MetaAnalyst .