Format Conversion Research Articles

Reconfigurable all-optical format conversion for 16QAM/8QAM by employing PSA in HNLF.

Extensive investigations are undertaken on the feasibility of utilizing phase sensitive amplification (PSA) in highly nonlinear fiber (HNLF) to achieve a reconfigurable 16QAM/8QAM all-optical format conversion for optical data center networks. A comprehensive theoretical model is developed and subsequently verified, based on numerical simulations undertaken to explore the effectiveness of the nonlinear effects of phase insensitive amplification, PSA, and self phase modulation for the proposed all-optical format conversion scheme. It is demonstrated that the proposed scheme can achieve a reconfigurable all-optical format conversion from a 16QAM signal to two quadrature phase shift keying (QPSK) signals or from an 8QAM signal to one QPSK signal and one binary phase shift keying signal with data rates of 92Gbps and 69Gbps for 16QAM and 8QAM signals, respectively.

BeEM: fast and faithful conversion of mmCIF format structure files to PDB format

BackgroundAlthough mmCIF is the current official format for deposition of protein and nucleic acid structures to the protein data bank (PDB) database, the legacy PDB format is still the primary supported format for many structural bioinformatics tools. Therefore, reliable software to convert mmCIF structure files to PDB files is needed. Unfortunately, existing conversion programs fail to correctly convert many mmCIF files, especially those with many atoms and/or long chain identifies.ResultsThis study proposed BeEM, which converts any mmCIF format structure files to PDB format. BeEM conversion faithfully retains all atomic and chain information, including chain IDs with more than 2 characters, which are not supported by any existing mmCIF to PDB converters. The conversion speed of BeEM is at least ten times faster than existing converters such as MAXIT and Phenix. Part of the reason for the speed improvement is the avoidance of conversion between numerical values and text strings.ConclusionBeEM is a fast and accurate tool for mmCIF-to-PDB format conversion, which is a common procedure in structural biology. The source code is available under the BSD licence at https://github.com/kad-ecoli/BeEM/.

The VEGA web service: multipurpose online tools for molecular modelling and docking analyses.

The paper presents the VEGA Online web service, which includes a set of freely available tools deriving from the development of the VEGA suite of programs. In detail, the paper is focused on two tools: the VEGA Web Edition (WE) and the Score tool. The former is a versatile file format converter including relevant features for 2D/3D conversion, for surface mapping and for editing/preparing input files. The Score application allows rescoring docking poses and in particular includes the MLP Interactions Scores (MLPInS) for describing hydrophobic interactions. To the best of our knowledge, this web service is the only available resource by which one can calculate both the virtual log P of a given input molecule according to the MLP approach plus the corresponding MLP surface.

XLUM: an open data format for exchange and long-term preservation of luminescence data

Abstract. The concept of open data has become the modern science meme, and major funding bodies and publishers support open data. On a daily basis, however, the open data mandate frequently encounters technical obstacles, such as a lack of a suitable data format for data sharing and long-term data preservation. Such issues are often community-specific and best addressed through community-tailored solutions. In Quaternary sciences, luminescence dating is widely used for constraining the timing of event-based processes (e.g. sediment transport). Every luminescence dating study produces a vast body of primary data that usually remains inaccessible and incompatible with future studies or adjacent scientific disciplines. To facilitate data exchange and long-term data preservation (in short, open data) in luminescence dating studies, we propose a new XML-based structured data format called XLUM. The format applies a hierarchical data storage concept consisting of a root node (node 0), a sample (node 1), a sequence (node 2), a record (node 3), and a curve (node 4). The curve level holds information on the technical component (e.g. photomultiplier, thermocouple). A finite number of curves represent a record (e.g. an optically stimulated luminescence curve). Records are part of a sequence measured for a particular sample. This design concept allows the user to retain information on a technical component level from the measurement process. The additional storage of related metadata fosters future data mining projects on large datasets. The XML-based format is less memory-efficient than binary formats; however, its focus is data exchange, preservation, and hence XLUM long-term format stability by design. XLUM is inherently stable to future updates and backwards-compatible. We support XLUM through a new R package xlum, facilitating the conversion of different formats into the new XLUM format. XLUM is licensed under the MIT licence and hence available for free to be used in open- and closed-source commercial and non-commercial software and research projects.

SimoSet: A 3D Object Detection Dataset Collected from Vehicle Hybrid Solid-State LiDAR

Three-dimensional (3D) object detection based on point cloud data plays a critical role in the perception system of autonomous driving. However, this task presents a significant challenge in terms of its practical implementation due to the absence of point cloud data from automotive-grade hybrid solid-state LiDAR, as well as the limitations regarding the generalization ability of data-driven deep learning methods. In this paper, we introduce SimoSet, the first vehicle view 3D object detection dataset composed of automotive-grade hybrid solid-state LiDAR data. The dataset was collected from a university campus, contains 52 scenes, each of which are 8 s long, and provides three types of labels for typical traffic participants. We analyze the impact of the installation height and angle of the LiDAR on scanning effect and provide a reference process for the collection, annotation, and format conversion of LiDAR data. Finally, we provide baselines for LiDAR-only 3D object detection.

Advancing Simultaneous Localization and Mapping with Multi-Sensor Fusion and Point Cloud De-Distortion

This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation.

Complex Analysis and Residue Theorem

The study of the properties of analytical functions is described as complex analysis. The residue theorem is an important conclusion in complex analysis. This paper introduces the origin of imaginary numbers from Cardano Formula defines the conversion of complex number formats from the Euler Theorem, and proves the intermediate theorem Cauchy Integral Formula before reaching our final conclusion and the goal of the paper, Residue Theorem. The name of the theorem comes from the concept of residue, which is defined using a functions Laurent series. We could then derive the Residue Theorem from the Cauchy Integral Theorem, also called the Cauchy-Goursat Theorem. We will be able to formalize our prior, ad hoc method of computing integrals over contours encompassing singularities. Additionally, it is a theorem that may be applied to zero-pole qualities and curved integral properties. The Residue Theorem is the basis of many essential mathematical facts revolving around line integrals, particularly in solving ODEs and PDEs and describing physics models.

The road to integrate climate change projections with regional land‐use–biodiversity models

Abstract Current approaches to project spatial biodiversity responses to climate change mainly focus on the direct effects of climate on species while regarding land use and land cover as constant or prescribed by global land‐use scenarios. However, local land‐use decisions are often affected by climate change and biodiversity on top of socioeconomic and policy drivers. To realistically understand and predict climate impacts on biodiversity, it is, therefore, necessary to integrate both direct and indirect effects (via climate‐driven land‐use change) of climate change on biodiversity. In this perspective paper, we outline how biodiversity models could be better integrated with regional, climate‐driven land‐use models. We initially provide a short, non‐exhaustive review of empirical and modelling approaches to land‐use and land‐cover change (LU) and biodiversity (BD) change at regional scales, which forms the base for our perspective about improved integration of LU and BD models. We consider a diversity of approaches, with a special emphasis on mechanistic models. We also look at current levels of integration and at model properties, such as scales, inputs and outputs, to further identify integration challenges and opportunities. We find that LU integration in BD models is more frequent than the other way around and has been achieved at different levels: from overlapping predictions to simultaneously coupled simulations (i.e. bidirectional effects). Of the integrated LU‐BD socio‐ecological models, some studies included climate change effects on LU, but the relative contribution of direct vs. indirect effects of climate change on BD remains a key research challenge. Important research avenues include concerted efforts in harmonizing spatial and temporal resolution, disentangling direct and indirect effects of climate change on biodiversity, explicitly accounting for bidirectional feedbacks, and ultimately feeding socio‐ecological systems back into climate predictions. These avenues can be navigated by matching models, plugins for format and resolution conversion, and increasing the land‐use forecast horizon with adequate uncertainty. Recent developments of coupled models show that such integration is achievable and can lead to novel insights into climate–land use–biodiversity relations. Read the free Plain Language Summary for this article on the Journal blog.

SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis

BackgroundCurrent proteomic technologies are fast-evolving to uncover the complex features of sequence processes, variations and modifications. Thus, protein sequence database and the corresponding softwares should also be improved to solve this issue.ResultsWe developed a state-of-the-art toolkit (SeqWiz) for constructing next-generation sequence databases and performing proteomic-centric sequence analyses. First, we proposed two derived data formats: SQPD (a well-structured and high-performance local sequence database based on SQLite), and SET (an associated list of selected entries based on JSON). The SQPD format follows the basic standards of the emerging PEFF format, which also aims to facilitate the search of complex proteoform. The SET format is designed for generating subsets with with high-efficiency. These formats are shown to greatly outperform the conventional FASTA or PEFF formats in time and resource consumption. Then, we mainly focused on the UniProt knowledgebase and developed a collection of open-source tools and basic modules for retrieving species-specific databases, formats conversion, sequence generation, sequence filter, and sequence analysis. These tools are implemented by using the Python language and licensed under the GNU General Public Licence V3. The source codes and distributions are freely available at GitHub (https://github.com/fountao/protwiz/tree/main/seqwiz).ConclusionsSeqWiz is designed to be a collection of modularized tools, which is friendly to both end-users for preparing easy-to-use sequence databases as well as bioinformaticians for performing downstream sequence analysis. Besides the novel formats, it also provides compatible functions for handling the traditional text based FASTA or PEFF formats. We believe that SeqWiz will promote the implementing of complementary proteomics for data renewal and proteoform analysis to achieve precision proteomics. Additionally, it can also drive the improvement of proteomic standardization and the development of next-generation proteomic softwares.

Simultaneous multichannel RZ to NRZ format conversion for LP01 and LP11 using a few-mode fiber Bragg grating.

We propose a novel format conversion scheme, which can implement multichannel format conversion from return-to-zero (RZ) to non-return-to-zero (NRZ) for both LP01 and LP11 simultaneously by designing a few-mode fiber Bragg grating (FM-FBG) with comb spectra. To achieve filtering for all channels of the two modes, the FM-FBG response spectra of LP11 is designed to shift with that of LP01 by the WDM-MDM channel spacing. This approach is realized by carefully selecting the specifications of the few-mode fiber (FMF) to fulfill the requirements of the effective refractive index difference between LP01 and LP11. Each single-channel outline of the FM-FBG response spectra is designed according to the algebraic difference between the RZ and NRZ spectra. Numerical results show that both LP01 and LP11 channels with 300-GHz-spaced RZ signals at 40 Gbit/s can be converted into NRZ signals simultaneously, and the converted NRZ signals have high Q-factor and their eye diagrams are clean and open.

Performance Verification of Integrated Module for Automatic Analysis of Digital Maps in Earthwork Sites

Research on digitalization of construction sites using UAV (Unmanned Aerial Vehicles), UGV (Unmanned Ground Vehicles), and three-dimensional scanners is rapidly progressing. However, most 3D digital map generation and analysis technologies are still performed individually by utilizing libraries and software that require human intervention. In this study, we developed a digital map automatic analysis framework by reviewing five algorithms required for digital map generation and analysis for 3D digital map generation and analysis automation, defining the optimal forward and backward relationship between algorithms. In addition, based on the developed framework, the "digital map automatic analysis integrated module" is developed by considering the scan data format conversion and data processing process for automation. Furthermore, the effectiveness of the development module was verified by automating and speed testing the developed "digital map automatic analysis integrated module".

Towards realistic privacy-preserving deep learning over encrypted medical data.

Cardiovascular disease supposes a substantial fraction of healthcare systems. The invisible nature of these pathologies demands solutions that enable remote monitoring and tracking. Deep Learning (DL) has arisen as a solution in many fields, and in healthcare, multiple successful applications exist for image enhancement and health outside hospitals. However, the computational requirements and the need for large-scale datasets limit DL. Thus, we often offload computation onto server infrastructure, and various Machine-Learning-as-a-Service (MLaaS) platforms emerged from this need. These enable the conduction of heavy computations in a cloud infrastructure, usually equipped with high-performance computing servers. Unfortunately, the technical barriers persist in healthcare ecosystems since sending sensitive data (e.g., medical records or personally identifiable information) to third-party servers involves privacy and security concerns with legal and ethical implications. In the scope of Deep Learning for Healthcare to improve cardiovascular health, Homomorphic Encryption (HE) is a promising tool to enable secure, private, and legal health outside hospitals. Homomorphic Encryption allows for privacy-preserving computations over encrypted data, thus preserving the privacy of the processed information. Efficient HE requires structural optimizations to perform the complex computation of the internal layers. One such optimization is Packed Homomorphic Encryption (PHE), which encodes multiple elements on a single ciphertext, allowing for efficient Single Instruction over Multiple Data (SIMD) operations. However, using PHE in DL circuits is not straightforward, and it demands new algorithms and data encoding, which existing literature has not adequately addressed. To fill this gap, in this work, we elaborate on novel algorithms to adapt the linear algebra operations of DL layers to PHE. Concretely, we focus on Convolutional Neural Networks. We provide detailed descriptions and insights into the different algorithms and efficient inter-layer data format conversion mechanisms. We formally analyze the complexity of the algorithms in terms of performance metrics and provide guidelines and recommendations for adapting architectures that deal with private data. Furthermore, we confirm the theoretical analysis with practical experimentation. Among other conclusions, we prove that our new algorithms speed up the processing of convolutional layers compared to the existing proposals.

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

To effectively manage the heterogeneous and discrete knowledge of the bridge maintenance domain, this study adopts knowledge graph technology to build a knowledge base of bridge maintenance, called the bridge maintenance knowledge graph (BMKG). The BMKG uses an ontology as the knowledge organization and representation framework and a graph database as the knowledge storage tool. To facilitate the construction of the BMKG, a hybrid method combining a top-down approach and a bottom-up approach is proposed. Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. Thirdly, ontology reasoning is adopted to perform consistency check on the BMDO. Lastly, the BMDO model is stored in the Neo4j graph database through data format conversion, thus completing the construction of the BMKG. The BMKG is applied in a typical scenario of bridge maintenance to demonstrate its application value. Results show that the proposed hybrid method can create a knowledge graph that can realize the transformation from discrete data into interconnected knowledge. Knowledge graph offers a novel idea to create a knowledge base in the bridge maintenance domain.

Rapid identification of chronic kidney disease in electronic health record database using computable phenotype combining a common data model.

Rapid identification of chronic kidney disease in electronic health record database using computable phenotype combining a common data model.

Abstract 4295: Streamlining the reconstruction of subclonal evolution in DNA sequencing data

Abstract In many cancer types, the evolution of subclonal malignant cells leads to a diverse tumor population that can affect treatment efficacy. In addition, evolution throughout treatment can lead to resistant subclonal populations and eventual relapse. To effectively treat these cancers, it is essential to understand the subclonal architecture of the tumor, including the somatic mutations that differentiate each population. Many tools have been developed to attempt to cluster mutations or reconstruct the clonal architecture of a given tumor sample. However, few can accommodate multiple samples, which is common in longitudinal or metastasis studies. In addition, these tools typically focus on just one of the steps necessary for a full analysis (e.g., variant clustering, subclone hierarchical tree reconstruction, and tree visualization). Many require non-standard data formats, necessitating data format conversion between each step. These requirements make it very challenging for investigators without a deep understanding of each tool to perform a full subclonal analysis. To make the identification of temporal and spatial heterogeneity more streamlined and accessible to analysts with basic programming knowledge, we have developed a pipeline that takes somatic mutations in standard VCF file format as input and produces meaningful results that are easy to interpret. Variant data is extracted and formatted for the input to PyClone-VI (Gillis and Roth, 2020), which is used to cluster somatic variants based on their allele frequencies at each time point. SuperSeeker, an improvement on the SubcloneSeeker method developed within our lab (Qiao et al., 2014), implements advanced tumor subclone reconstruction algorithms to jointly analyze multiple samples and construct hierarchical trees that account for all subclones observed in a patient. A sample trace, or the cellular fraction of each subclone found in each sample, is also provided. The output of SuperSeeker is an updated VCF file with the tree and sample trace information added to the header. Finally, a GraphViz rendering of the hierarchical tree is made for easy visualization. In addition to this rendering, the final VCF file can also be used for interactive analysis and visualization of the results using our Oncogene.iobio web tool. This pipeline makes identifying temporal and spatial heterogeneity more efficient, requiring only standard file types as input, and basic command line knowledge. In a recently published study, we investigated subclonal evolution in 38 patients with CLL being treated with a BTKi. The initial subclonal architecture analysis for each patient in this study was laborious and time intensive. Using this pipeline, we can now identify subclonal evolution in these patients and create an interactive visualization more efficiently. This approach can streamline analyses, increase efficiency, and lead to a deeper understanding of a tumor’s subclonal evolution. Citation Format: Gage Black, Yi Qiao, Xiaomeng Huang, Gabor Marth. Streamlining the reconstruction of subclonal evolution in DNA sequencing data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4295.

A dataset of vegetation phenology of Inner Mongolia (2001-2020)

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important part of the ecological security barrier of the Mongolian Plateau, and a key area for resource development, environmental protection and ecological security in China. The study on the vegetation phenological changes of Inner Mongolia is of great significance for understanding the characteristics of climate change and extreme climate events in Inner Mongolia. Based on the normalized differential vegetation index (NDVI) in the high-spatial resolution MOD13Q1 data product, we used Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exported NDVI long time series data from 2000 to 2021. Then, we adopted the dynamic threshold method to obtain a dataset of vegetation phenology of Inner Mongolia from 2001 to 2020. With a resolution of 250 m, this dataset contains remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. It can provide data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.

Transmission characteristics of all-optically converted QPSK and 16QAM signals

Various types of all-optical modulation format conversions have been proposed for realizing low latency and efficient connection of networks that have different modulation formats. All-optical PAM4 to QPSK and 16QAM modulation format conversions using an HNLF and NOLM have been demonstrated for connecting data center networks. However, there has been no study on the transmission characteristics of the converted signal over a long distance. In this study, we numerically investigate the spectral efficiency and transmission characteristics of the QPSK and 16QAM signals generated by the all-optical modulation format converters. The all-optically converted 50 Gbps class QPSK and 100 Gbps class 16QAM signals require a bandwidth of 55 GHz to maintain the EVM below 10%. We demonstrate that the converted QPSK and 16QAM signals can be transmitted up to 19,500 km and 4,000 km, respectively, in both single and 8 channels transmission by preserving the sufficient channel spacing of 62.5 GHz. Because the transmission distance of the typical inter-data-center network is shorter than 200 km, the converted signals are applicable to inter-datacenter networks. Moreover, the QPSK and 16QAM signals converted from PAM4 signals after 40 km transmission in an intra-data center network are valid for transmission in inter-data-center networks.

DecodeSTORM: A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

A Novel Strategy to Achieve Video Transcoding Using Cloud Computing

One of the fundamental challenges faced in deploying multimedia systems is delivering smooth and uninterrupted audio-visual information anywhere and anytime. In such systems, multimedia content is compressed within a certain format, this requires format conversion for various devices. Thus, a transcoding mechanism is required to make the content adaptive for various devices in the network. Video transcoding converts one digitally encoded format into another, this involves translating any file format containing video and audio at the same time. This is an essential feature for devices that do not support a specific format of media or have limited storage that requires a reduced file size. Through this paper, we provide a novel way of transcoding the block-based video coding schemes using cloud architecture by establishing a video pipelining architecture. The solution discussed in this paper would enable the end users to extract videos in any format and resolution seamlessly, combined with the scalability, reliability, and cost-effectiveness of the cloud. The proposed idea would be lucrative for all the video streaming applications that are currently relying on their legacy infrastructure for video transcoding.

MRADSIM-Converter: A new software for STEP to GDML conversion

Radiation effects analysis of instruments operative in harsh radiation environment is crucial for performance and functionality of electronic devices and components. Engineering design of instruments is usually carried out in Computer Aided Design (CAD) engineering software. Geant4-based Monte Carlo codes are extensively used for particle transport simulation and analysis. However, Geant4 is not prepared to accept CAD Standard for The Exchange of Product data (STEP) format. MRADSIM-Converter is a new software for STEP to Geometry Description Markup Language (GDML) format conversion, readable by Geant4-based Monte Carlo codes. Its validation with two different converters confirms its higher speed for importing CAD geometries with arbitrary size and complexity having a user-friendly interface for modifying volumes properties. Program summaryProgram title: MRADSIM-ConverterCPC Library link to program files:https://doi.org/10.17632/8kzsyn79gh.1Licensing provisions: LGPLProgramming language: C++Supplementary material: CMake, Qt5, OpenCascadeNature of problem: Geant4 toolkit simulation code developed at CERN is widely used for high energy physics community as well as health physics and radioprotections communities. However, Geant4 is not prepared to receive the CAD formats file, and the geometry implementation in Geant4 is a time-consuming task as it requires the creation of each volume using specific classes.Solution method: MRADSIM-Converter is a software designed to perform a conversion between the files written in STEP format, prepared by CAD software, into the GDML format, accepted as input in Geant4. All existent volumes in the input STEP file are visualized in the Graphical User Interface (GUI), where it is possible to interactively assign the material to the components and modify each volume's properties, such as material, color, and name.Additional comments including restrictions and unusual features: MRADSIM-Converter is tested under Ubuntu, versions 18 and 20. All the libraries necessary to run the program are provided in the package hence no additional library or software is required to be installed. Under MacOS and Windows, MRADSIM-Converter is running through virtual machine (VM) with above-mentioned Ubuntu versions.