Stand-alone Software Research Articles

Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors

BackgroundIn most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.ResultsAmong the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).ConclusionsLogistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types.

Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (E), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σt) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (E), Poisson's ratio (ν), UCS, and indirect tensile strength (σt), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.

Precision Livestock Farming IT Support Model for the Poultry Industry

The presented work proposes a practical approach to bird weight data processing and augmentation to enable production outcome forecast model training, which contributes to higher productivity. We suggest using the parametrized model, where parameter values are found through genetic optimization and thus are closely corresponding to broiler body weight factual measurements. The proposed approach is implemented as a stand-alone software system, exposing the models through containerized web services enabling different use scenarios.

Volcanic crisis management supported by near real-time lava flow hazard assessment at Piton de la Fournaise, La Réunion

Since 1979, Piton de la Fournaise (La Réunion) has erupted on average two times per year, with 95 % of these eruptions occurring within an uninhabited caldera. However, lava flows have occasionally impacted populated regions on the island, as in 1977 and 1986. Since 2014, an integrated satellite data–driven multinational response to effusive crises has been developed to rapidly assess lava inundation area and flow runout distance. In 2018, this protocol was implemented as a standalone software to provide a lava flow hazard map showing the probability of flow coverage and runouts as a function of discharge rate. Since 2019, the produced short-term hazard map is shared with local civil protection in the first few hours following the start of an eruption to aid in mitigation actions. Multiple exchanges between scientists, the observatory, and civil protection has improved the delivered hazard map, ensuring a common understanding, a product which is of use and usable, and helping to build effective mitigation strategies at Piton de la Fournaise. In this work we illustrate this effective near real-time protocol with case studies and document how the produced short-term hazard map has been tailored to meet the needs of civil protection.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models.

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Vegetated Roofs as a Means of Sustainable Urban Development: A Scoping Review

Urbanisation affects the water cycle and heat balance in a negative way. Vegetated roofs have the potential to minimise the effects of urbanisation. A scoping review is presented here to examine how vegetated roofs are being evolved as an effective tool of sustainable urban stormwater management and overall urban development. It has been found that research on vegetated roofs has been increasing significantly and it can contribute towards achieving multiple sustainable development goals (SDGs). It has also been found that the uptake of vegetated roofs has been slow. A lack of regulatory acceptance caused by an absence of experimental data and a subsequent knowledge gap establishing the effectiveness of vegetated roofs are major reasons behind this slow uptake. Future research on vegetated roofs and their subsequent evolutions should put a focus on gathering experimental data towards establishing a performance benchmark for detention, retention and water quality in urban settings. Such data can be utilised towards developing a stand-alone guideline and software for green roof design.

Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan

The development of computer-aided detection (CAD) using artificial intelligence (AI) and machine learning (ML) is rapidly evolving. Submission of AI/ML-based CAD devices for regulatory approval requires information about clinical trial design and performance criteria, but the requirements vary between countries. This study compares the requirements for AI/ML-based CAD devices approved by the US Food and Drug Administration (FDA) and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. A list of 45 FDA-approved and 12 PMDA-approved AI/ML-based CAD devices was compiled. In the USA, devices classified as computer-aided simple triage were approved based on standalone software testing, whereas devices classified as computer-aided detection/diagnosis were approved based on reader study testing. In Japan, however, there was no clear distinction between evaluation methods according to the category. In the USA, a prospective randomized controlled trial was conducted for AI/ML-based CAD devices used for the detection of colorectal polyps, whereas in Japan, such devices were approved based on standalone software testing. This study indicated that the different viewpoints of AI/ML-based CAD in the two countries influenced the selection of different evaluation methods. This study’s findings may be useful for defining a unified global development and approval standard for AI/ML-based CAD.

Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into diverse human interaction domains that require robust human-robot communication systems. Visual communication techniques have shown promise in their ability to communicate concrete information to observers. Such techniques, often described as a UAV ‘gesture,’ may be especially useful in the domain of unmanned aerial flight as they can be integrated as a stand-alone software solution in contrast to light or sound-based systems that often require additional hardware and add weight to a vehicle. Gestures may also be useful in contexts where long distance operation reduces the effectiveness of sound-based communication strategies. As gesture is a visual communication technique, it is critical that gestures are designed to optimize an observer’s ability to visually perceive the shape of a gesture’s motion. Factors such as low visual differentiability between gestures within a set may reduce an observer’s ability to classify the shape of a gestural motion. In this letter, we discuss the results from multiple gesture perception surveys. We also develop and evaluate techniques to predict, in advance, how participants may perceive a UAV gesture. We demonstrate that participant gesture classification accuracy correlates to trajectory distance measures and present a method for evaluating high-differentiabilty gesture sets. This letter will enable gesture designers to create gesture sets that are differentiable with high-confidence.

Homoglyph Attack Detection Model Using Machine Learning and Hash Function

Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection.

Multipurpose Cloud-Based Compiler Based on Microservice Architecture and Container Orchestration

Compilation often takes a long time, especially for large projects or when identifying better optimization options. Currently, compilers are mainly installed on local machines and used as standalone software. Despite the availability of several online compilers, they do not offer an efficient all-in-one package for private account management, command line interface (CLI), code advisors, and optimization techniques. Today, the widespread usage of Software as a Service (SaaS) is ever-growing, and compilers are not an exception. In this paper, we describe a symmetric approach to compilation and how to compile code on distributed systems. Although some improvements in cloud compilers have been made, it is possible to harness the potential of the most-modern technologies and architecture patterns toward designing efficient, in-cloud compilers. In this paper, we propose an architecture design of a cloud-based compiler that is fully compatible with orchestration technologies, such as Kubernetes, providing a higher level of scalability, reliability, security, and maintainability. Microservice architecture alongside containerization and orchestration technologies assist us in making a scalable system that provides a high level of availability. We propose this architecture so that the system can handle a higher workload as it receives a large number of compilation requests per second. Distributed compilation is a prominent benefit of this approach, as each phase of the compilation can be executed in a separate server, which supplies a kind of workload mitigation to the whole system. In other words, we propose a new perspective for an intelligent way of advisor, error detection, and optimization of compilers. We also propose an implementation example of the developed architecture. Finally, we analyze the results from an experimental implementation, proving that we can compile code from more than 100k requests concurrently on a cloud cluster with one master node and three worker nodes.

GTFtools: a software package for analyzing various features of gene models.

Gene-centric bioinformatics studies frequently involve the calculation or the extraction of various features of genes such as splice sites, promoters, independent introns and untranslated regions (UTRs) through manipulation of gene models. Gene models are often annotated in gene transfer format (GTF) files. The features are essential for subsequent analysis such as intron retention detection, DNA-binding site identification and computing splicing strength of splice sites. Some features such as independent introns and splice sites are not provided in existing resources including the commonly used BioMart database. A package that implements and integrates functions to analyze various features of genes will greatly ease routine analysis for related bioinformatics studies. However, to the best of our knowledge, such a package is not available yet. We introduce GTFtools, a stand-alone command-line software that provides a set of functions to calculate various gene features, including splice sites, independent introns, transcription start sites (TSS)-flanking regions, UTRs, isoform coordination and length, different types of gene lengths, etc. It takes the ENSEMBL or GENCODE GTF files as input and can be applied to both human and non-human gene models like the lab mouse. We compare the utilities of GTFtools with those of two related tools: Bedtools and BioMart. GTFtools is implemented in Python and not dependent on any third-party software, making it very easy to install and use. GTFtools is freely available at www.genemine.org/gtftools.php as well as pyPI and Bioconda.

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

Systematic Design of Solvent Recovery Pathways: Integrating Economics and Environmental Metrics

The increasing trend of solvent usage by chemical industries has caused an upsurge in hazardous waste solvents and, consequently, increased their toxicity potential within the environment. The increase in overall carbon and ecological footprints due to conventional waste handling techniques such as incineration and onsite and offsite disposal can hardly be overstated. Even though solvent recovery methods present a better alternative to these techniques, analysis is generally centered on economics. This paper presents a framework that evaluates the economic and sustainability metrics of solvent recovery processes. In the first approach, standalone software (SimaPro) was used to evaluate the impact of the recovery process. The framework was extended by incorporating the economics, Sustainable Process Index (SPI), and Emergy metrics. Thus, solvent recovery challenges have been transformed into a multiobjective optimization problem that is solved through superstructure and mixed-integer nonlinear programming techniques. The applicability of the framework is illustrated by two comprehensive case studies with varying complexities, and the results are compared with conventional methods. The results indicate that about 76–85% of the ecological and energy burden can be prevented if solvent recovery is the preferred option to incineration.

A deep learning-based method for the prediction of DNA interacting residues in a protein.

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15636 DNA interacting and 298503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.

CoR-SketchAR: Cooperative Sketch-Based Real-Time Augmented Reality Authoring Tool for Crowd Simulation

In this study, we propose CoR-SketchAR, an augmented reality (AR) environment authoring tool that uses dry-erase markers for real-time collaboration among users. The most important requirement for multi-user collaboration in an AR environment is that the 3D virtual object must be placed at a specific location and can be easily changed by multiple users. Because marker-based registration techniques, which are widely used for matching virtual objects with real ones, require a marker for each object, and creating a crowd simulation environment with objects of various shapes and sizes requires the use of a large number of markers, which is time consuming and expensive. CoR-SketchAR, instead, creates an urban AR environment by drawing sketches with easily altered dry-erase marker. Then, system recognizes the shapes and colors of the sketches automatically. Those recognized shapes and colors provide the exact positions for overlaying the 3D virtual objects, which are the environment factors, on the real environment in augmented reality manner. We can even specify the path the crowd are moving along with a simple sketch stroke. We apply the computer vision technique to recognize the colors and shapes of sketches. By altering the size, shape and color sketches, the system is able to create a wide variety of dynamic urban environments. To validate the proposed techniques, we built two stand-alone software systems to check the usability of the proposed system (a 2D screen-based environmental authoring tool and a sketch-based environmental authoring tool) and conducted experiments in which two users collaborated with each other to create an environment with a specific authoring tool and then report surveys. In the experiments, users collaborated in pairs to create environmental elements, such as highways, buildings, trees, and the starting and goal positions of crowds. After recognizing them, the system then automatically creates a 3D environment, and crowds are animated accordingly. Based on a user survey, we observed that participants who used sketch-based environmental authoring tools were more active and accessible than those who used 2D screen-based authoring tools. The results of the study show that CoR-SketchAR can be further used to create a dynamical crowd simulation on a large scale using beam projectors or portable devices by simply adding sketches based on different scenarios.

Scenario of E-Accounting Practice in Ready-made Garments (RMG) Industry of Bangladesh

E--accounting is referred as the internet based accounting software usage in performing accounting functions. Responding to the evidence that Bangladesh’s top business sector, Ready-made Garments (RMG) factories largely using e-accounting, the study is aimed to explore the scenario of the practice in those factories. To achieve these objectives, the study deploys mixed research strategy constructed with interview and survey. Based on the prevailed e-accounting literature, and interview with user RMG factories authority and vendors, structured questionnaire was formed for the survey. This firm level study considers 3552 listed RMG factories as the population. Using G-power 3.1 sample size calculating software, considering smaller effect size f2=0.04, the study has taken 300 sampled factories both of woven, knit, composite and sweater. The study conducted interview using semi-structured interview schedule at convenient way. Also using convenience sampling technique, a survey on sampled factories was executed with the use of structured questionnaire. Survey respondents were the policy level personnel of RMG factory such as owners, managing directors, executive director, factory manager, and or chief accountants. IBM SPSS 20.0 has been used to analyze survey data. Using descriptive statistics, the collected data was presented and analyzed. The study reveals that about 86% RMG factories are using internet based accounting solution for performing accounting, inventory and payroll functions. The study also results that ERP usage is more than that of stand-alone accounting software modules and private cloud usage is more popular in RMG sector than that of public, hybrid or community cloud. This study also finds that knit sector remains ahead in using e-accounting and sweater factories is slightly back in e-accounting implementation. The study bears the significance to the RMG factories and other cloud applications users. Keywords: E-Accounting, Online accounting solution usage, Ready-made Garments Industry, Bangladesh

3D Modelling of Meteorite from Astomulyo Village, Lampung, Indonesia by Close Range Photogrammetry (CRP) Methods

This research was conducted to 3D model our meteorite collection based on photo-capturing. This meteorite are from Astomulyo, Central Lampung, Indonesia. It entry the earth's atmosphere and was found on January 28, 2021. The chance of object degradation nearly appears over time, so It is very urgent to obtain the 3D model of the object for further research necessary. 3D modelling was carried out using a non-metric camera to conduct the close-range photogrammetry method. It applies to modelling the objects based on photo data using the concept of collinearity by using a central projection system on the camera to record. The collinearity will produce an object point between the camera center and the object cloud point. Hence, this process forms the object model from the appropriate image projection, the tie point with the triangular irregular network model. As a result, it produces the DTM (Digital Terrain Model) object formation using stand-alone photogrammetric software, raster graphics, and 3D modelling software.

A STUDY ON CONSUMER PERCEPTION ON E-WALLET IN AHMEDABAD

An e-wallet also known as digital wallet refers to a device that allows an individual to make electronic commerce transactions quickly and securely. The purpose of the study is to examine consumer perception regards E-wallet. The objective of the study is to finding usage of e-wallet, safety of e-wallet, service of e-wallet, how much easy to use, which device they use for making transaction, average monthly spending via e-wallet, which application they most use, which kind of transaction do they make via e-wallet etc. the study is also helping for the E-Wallet companies that how many people are aware about E-Wallet and how many people are using E-wallet for which types of transaction. How many people are aware from E-wallet. It is safer than cash money since only the legitimate owner who knows the password can operate it. A consumer e-wallet is a standalone software application that you can download and install on your computer, PDA or Smartphone. The main objective of e-wallet is to make paperless money transaction easier.

Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST.

As the development of new drugs reaches its physical and financial limits, drug repurposing has become more important than ever. For mechanistically grounded drug repurposing, it is crucial to uncover the disease mechanisms and to detect clusters of mechanistically related diseases. Various methods for computing candidate disease mechanisms and disease clusters exist. However, in the absence of ground truth, in silico validation is challenging. This constitutes a major hurdle toward the adoption of in silico prediction tools by experimentalists who are often hesitant to carry out wet-lab validations for predicted candidate mechanisms without clearly quantified initial plausibility. To address this problem, we present DIGEST (in silico validation of disease and gene sets, clusterings or subnetworks), a Python-based validation tool available as a web interface (https://digest-validation.net), as a stand-alone package or over a REST API. DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical $P$-values with just a few mouse clicks.

Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation

Objective. To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency. Approach. In SPIDERM, a ‘prep’ scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A ‘live’ scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the ‘prep’-learned subspace and ‘live’ coordinates, real-time 3D images are generated on-the-fly with computationally efficient matrix multiplication. When implemented based on a multi-contrast pulse sequence, SPIDERM further allows for data-driven image contrast regeneration to convert real-time contrast-varying images into contrast-frozen images at user’s discretion while maintaining motion states. Both digital phantom and in-vivo experiments were performed to evaluate the technical feasibility of SPIDERM. Main results. The elapsed time from the input of the central k-space line to the generation of real-time contrast-frozen 3D images was approximately 45 ms, permitting a latency of 55 ms or less. Motion displacement measured from SPIDERM and reference images showed excellent correlation (). Geometric variation from the ground truth in the digital phantom was acceptable as demonstrated by pancreas contour analysis (Dice ≥ 0.84, mean surface distance ≤ 0.95 mm). Quantitative image quality metrics showed good consistency between reference images and contrast-varying SPIDREM images in in-vivo studies (mean ). Significance. SPIDERM is capable of generating real-time multi-contrast 3D images with a low latency. An imaging framework based on SPIDERM has the potential to serve as a standalone package for MR-guided radiation therapy by offering adaptive simulation through a ‘prep’ scan and real-time image guidance through a ‘live’ scan.