Command-line Arguments Research Articles

PACKET ANALYZER OF ONLINE ECG SIGNALS

This paper presents the implementation of online acquisition software for electrocardiogram (ECG) signals. This program is based on the Python language and it allows the adaptation of a commercial offline signal acquisition board into an online signal acquisition board. Here, the concept of online acquisition is applied when the signal can be analyzed in short time intervals, such as a few seconds. The developed system is divided into two parts: the acquisition module; and the graphical user interface (GUI). The acquisition module is responsible for recording and processing the raw signal, making it available to the graphics module. The developed structure allows the algorithm to be used in more complex applications, abstracting the acquisition logic. Furthermore, the acquisition module has acquisition settings, defined by the user when initializing the module. This work presents a simple example of an application using the module. In this case, the system stores the information from the acquisition module and constantly plots it on the screen. The program also integrates settings available by code, using command-line arguments. Therefore, the developed system presents itself as a powerful tool for online ECG signal acquisition, allowing the use of the board in human-machine interfaces.

The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data.

With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data to individual idiosyncrasies associated with each package. Here we describe PyMVPA BIDS-App, a fast and robust pipeline based on the data organization of the BIDS standard that performs multivariate analyses using powerful functionality of PyMVPA. The app runs flexibly with blocked and event-related fMRI experimental designs, is capable of performing classification as well as representational similarity analysis, and works both within regions of interest or on the whole brain through searchlights. In addition, the app accepts as input both volumetric and surface-based data. Inspections into the intermediate stages of the analyses are available and the readability of final results are facilitated through visualizations. The PyMVPA BIDS-App is designed to be accessible to novice users, while also offering more control to experts through command-line arguments in a highly reproducible environment.

New features of the TFmix code: Thermodynamic properties of electrons in mixtures

New features of the TFmix code: Thermodynamic properties of electrons in mixtures

Trimmer: An Automated System for Configuration-Based Software Debloating

Software bloat has negative implications for security, reliability, and performance. To counter bloat, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> , a static analysis-based system for pruning unused functionality. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> removes code that is unused with respect to user-provided command-line arguments and application-specific configuration files. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> uses concrete memory tracking and a custom inter-procedural constant propagation analysis that facilitates dead code elimination. Our system supports both context-sensitive and context-insensitive constant propagation. We show that context-sensitive constant propagation is important for effective software pruning in most applications. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse constant propagation</i> that performs constant propagation only for configuration-hosting variables and show that it performs better (higher code size reductions) compared to constant propagation for all program variables. Overall, our results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> reduces binary sizes for real-world programs with reasonable analysis times. Across 20 evaluated programs, we observe a mean binary size reduction of 22.7 percent and a maximum reduction of 62.7 percent. For 5 programs, we observe performance speedups ranging from 5 to 53 percent. Moreover, we show that winnowing software applications can reduce the program attack surface by removing code that contains exploitable vulnerabilities. We find that debloating using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> removes CVEs in 4 applications.

GenASiSBasics: Object-oriented utilitarian functionality for large-scale physics simulations (Version 4)

GenASiSBasics: Object-oriented utilitarian functionality for large-scale physics simulations (Version 4)

ICOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM—GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.

An Analysis of Conti Ransomware Leaked Source Codes

In recent years, there has been an increase in ransomware attacks worldwide. These attacks aim to lock victims’ machines or encrypt their files for ransom. These kinds of ransomware differ in their implementation and techniques, starting from how they spread, vulnerabilities they leverage, methods to hide their behaviors from antivirus software, encryption methods, and performance. The Conti ransomware is sophisticated ransomware that operates as ransomware-as-a-service. It started in 2019 and had an unprecedented human impact by targeting healthcare systems and cost $45 million. This paper analyzes the Conti ransomware source codes leaked on February 27, 2022, by an anonymous individual. We first look at the general code structure. Then, we analyze its flow, starting with its application programming interface disguise techniques, anti hook mechanisms, command-line arguments, and finally, its multithreaded encryption. We also perform a static and dynamic analysis of the latest known Conti sample in an isolated environment and compare its behavior to its source code flows.

CAFE 5 models variation in evolutionary rates among gene families.

Genome sequencing projects have revealed frequent gains and losses of genes between species. Previous versions of our software, Computational Analysis of gene Family Evolution (CAFE), have allowed researchers to estimate parameters of gene gain and loss across a phylogenetic tree. However, the underlying model assumed that all gene families had the same rate of evolution, despite evidence suggesting a large amount of variation in rates among families. Here, we present CAFE 5, a completely re-written software package with numerous performance and user-interface enhancements over previous versions. These include improved support for multithreading, the explicit modeling of rate variation among families using gamma-distributed rate categories, and command-line arguments that preclude the use of accessory scripts. CAFE 5 source code, documentation, test data and a detailed manual with examples are freely available at https://github.com/hahnlab/CAFE5/releases. Supplementary data are available at Bioinformatics online.

RepeatFS: a file system providing reproducibility through provenance and automation.

MotivationReproducibility is of central importance to the scientific process. The difficulty of consistently replicating and verifying experimental results is magnified in the era of big data, in which bioinformatics analysis often involves complex multi-application pipelines operating on terabytes of data. These processes result in thousands of possible permutations of data preparation steps, software versions and command-line arguments. Existing reproducibility frameworks are cumbersome and involve redesigning computational methods. To address these issues, we developed RepeatFS, a file system that records, replicates and verifies informatics workflows with no alteration to the original methods. RepeatFS also provides several other features to help promote analytical transparency and reproducibility, including provenance visualization and task automation.ResultsWe used RepeatFS to successfully visualize and replicate a variety of bioinformatics tasks consisting of over a million operations with no alteration to the original methods. RepeatFS correctly identified all software inconsistencies that resulted in replication differences.Availabilityand implementationRepeatFS is implemented in Python 3. Its source code and documentation are available at https://github.com/ToniWestbrook/repeatfs.Supplementary information Supplementary data are available at Bioinformatics online.

Bionitio: demonstrating and facilitating best practices for bioinformatics command-line software.

BackgroundBioinformatics software tools are often created ad hoc, frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices. This results in the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability, and interoperability; and erroneous or inaccurate results.FindingsWe have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command-line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardized building and packaging, user documentation, code documentation, a standard open source software license, software revision control, and containerization.ConclusionsBionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users because tools are more easily installed and consistent in their usage. Bionitio is released as open source software under the MIT License and is available at https://github.com/bionitio-team/bionitio.

FESTR: Finite-Element Spectral Transfer of Radiation spectroscopic modeling and analysis code (New Version Announcement)

We report on the release of a new version of the FESTR (P. Hakel, Comput. Phys. Commun. 207 (2016) 415–425., https://github.com/LANLhakel/FESTR) spectral postprocessor of hydrodynamic simulations of hot, dense plasmas. Based on given time histories of one-, two-, and three-dimensional spatial distributions of materials, and their local temperature and density conditions, spectroscopically-resolved signals are computed. The effects of radiation emission and absorption by the plasma on the emergent spectra are simultaneously taken into account. This program can also be used independently of hydrodynamic calculations to analyze available experimental data with the goal of inferring plasma conditions. NEW VERSION PROGRAM SUMMARYProgram Title: FESTRProgram Files doi:http://dx.doi.org/10.17632/npvtbd7kfg.2Licensing provisions: BSD 3-clause licenseProgramming language: C++Journal reference of previous version: P. Hakel, Comput. Phys. Commun. 207 (2016) 415–425.Does the new version supersede the previous version?: YesReasons for the new version: Parallelization, bug fixes, new features and copyrightSummary of revisions: The copyright notice was changed to reflect the transition of the Los Alamos National Laboratory M&O contract from Los Alamos National Security, LLC, to Triad National Security, LLC. The code was parallelized across time steps using MPI and selected loops were parallelized with OpenMP threading; the makefile now takes additional command-line arguments to invoke these capabilities. The makefile was generalized to use wildcard functionality and placed into the new directory named “common” along with MPI and unit-test relevant source files. Object files are now assembled into library (“*.a”) archive files before linking with the object file containing the “main” function (this change should facilitate the reuse of FESTR functions in other possible future projects.) New “tracking” members were added to classes Detector and Ray to mark whether partial spectral results (along the specified Rays) should also be output (the default is “no” i.e., “false”); relevant unit tests were added or modified accordingly. Several bugs were found and corrected. Additional details and the full history of the development of this program can be found in the “README.md” and “about.txt” files located in the top directory of this package.Nature of problem: Calculation of spectral signals by postprocessing hydrodynamics simulations. Analysis of experimental spectroscopic data to infer plasma temperature and density conditions, and its chemical composition. Simultaneous treatment of spatial non-uniformity (on 3D unstructured meshes) along with spectroscopic-quality radiation transport.Solution method: Rays are cast across a 3D unstructured mesh that characterizes local temperature, density, and chemical composition of the material. Analytic solution to the 1D (along the ray) steady-state, local radiation transport equation is repeatedly used to gradually build a synthetic spectrum for each ray.Restrictions: Steady-state approximation of the radiation transport equation is used. Scattering as a radiation source is not included. Given plasma conditions are considered fixed; potential feedback of computed radiation back into plasma temperature and equation-of-state is neglected. Doppler shifts are not modeled at this time. The quality of the computed results critically depends on the accuracy of external atomic databases used as input by FESTR. Polygon objects are assumed to be convex at present.

A Verified Generational Garbage Collector for CakeML

This paper presents the verification of a generational copying garbage collector for the CakeML runtime system. The proof is split into an algorithm proof and an implementation proof. The algorithm proof follows the structure of the informal intuition for the generational collector’s correctness, namely, a partial collection cycle in a generational collector is the same as running a full collection on part of the heap, if one views pointers to old data as non-pointers. We present a pragmatic way of dealing with ML-style mutable state, such as references and arrays, in the proofs. The development has been fully integrated into the in-logic bootstrapped CakeML compiler, which now includes command-line arguments that allow configuration of the generational collector. All proofs were carried out in the HOL4 theorem prover.

Lempel-Ziv Jaccard Distance, an effective alternative to ssdeep and sdhash

Recent work has proposed the Lempel-Ziv Jaccard Distance (LZJD) as a method to measure the similarity between binary byte sequences for malware classification. We propose and test LZJD's effectiveness as a similarity digest hash for digital forensics. To do so we develop a high performance Java implementation with the same command-line arguments as sdhash, making it easy to integrate into existing work-flows. Our testing shows that LZJD is effective for this task, and significantly outperforms sdhash and ssdeep in its ability to match related file fragments and files corrupted with random noise. In addition, LZJD is up to 60× faster than sdhash at comparison time.

Clarifications on the Construction and Use of the ManyBugs Benchmark

Automated repair techniques produce variant php interpreters, which should naturally serve as the tested interpreters. However, the answer to the question of what should serve as the testing interpreter is less obvious. php's default test harness configuration uses the same version of the interpreter for both the tested and testing interpreter. However, php may be configured via a command-line argument to use a different interpreter, such as the unmodified defective version, or a separate, manually-repaired version.

Jug: Software for Parallel Reproducible Computation in Python

As computational pipelines become a bigger part of science, it is important to ensure that the results are reproducible, a concern which has come to the fore in recent years. All developed software should be able to be run automatically without any user intervention. In addition to being valuable to the wider community, which may wish to reproduce or extend a published analysis, reproducible research practices allow for better control over the project by the original authors themselves. For example, keeping a non-executable record of parameters and command line arguments leads to error-prone analysis and opens up the possibility that, when the results are to be written up for publication, the researcher will no longer be able to even completely describe the process that led to them. For large projects, the use of multiple computational cores (either in a multi-core machine or distributed across a compute cluster) is necessary to obtain results in a useful time frame. Furthermore, it is often the case that, as the project evolves, it becomes necessary to save intermediate results while down-stream analyses are designed (or re-designed) and implemented. Under many frameworks, this causes having a single point of entry for the computation becomes increasingly difficult. Jug is a software framework which addresses these issues by caching intermediate results and distributing the computational work as tasks across a network. Jug is written in Python without the use of compiled modules, is completely cross-platform, and available as free software under the liberal MIT license. Jug is available from: http://github.com/luispedro/jug.

DBSR_HF: A B-spline Dirac–Hartree–Fock program

A B-spline version of a general Dirac–Hartree–Fock program is described. The usual differential equations are replaced by a set of generalized eigenvalue problems of the form (Ha−εaB)Pa=0, where Ha and B are the Hamiltonian and overlap matrices, respectively, and Pa is the two-component relativistic orbit in the B-spline basis. A default universal grid allows for flexible adjustment to different nuclear models. When two orthogonal orbitals are both varied, the energy must also be stationary with respect to orthonormal transformations. At such a stationary point the off-diagonal Lagrange multipliers may be eliminated through projection operators. The self-consistent field procedure exhibits excellent convergence. Several atomic states can be considered simultaneously, including some configuration-interaction calculations. The program provides several options for the treatment of Breit interaction and QED corrections. The information about atoms up to Z=104 is stored by the program. Along with a simple interface through command-line arguments, this information allows the user to run the program with minimal initial preparations. Program summaryProgram title: DBSR_HFCatalogue identifier: AEZK_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEZK_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 22643No. of bytes in distributed program, including test data, etc.: 354629Distribution format: tar.gzProgramming language: Fortran 95.Computer: No specific requirements to the computer.Operating system: Any system with a Fortran 95 compiler.Classification: 2.1.External routines: LAPACK (http://www.netlib.org/lapack/)Nature of problem:Relativistic Dirac–Hartree–Fock wavefunctions are determined for atoms in a bound state. These wavefunctions may be used to predict a variety of atomic properties.Solution method:The radial functions for large and small components of the one-electron spinor are expanded in B-spline bases. The variational principle applied to an energy functional that includes Lagrange multipliers for orthonormal constraints defines the Dirac–Hartree–Fock matrix for each orbital. Orthonormal transformations for a stationary solution were applied and Lagrange multipliers eliminated through projection operators.Restrictions:There is no restriction on calculations for the average or specific term energy of any atomic configuration with shells whose angular momenta are less than or equal to 9/2.Unusual features:The program allows the consideration of a few atomic states simultaneously. A simple interface through the command-line arguments allows the user to run the program with minimal initial preparations.Running time:From a few seconds to a few minutes depending on the atom under consideration.

Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy.

PurposeTo compare non-commercial DICOM toolkits for their de-identification ability in removing a patient's personal health information (PHI) from a DICOM header.Materials and MethodsTen DICOM toolkits were selected for de-identification tests. Tests were performed by using the system’s default de-identification profile and, subsequently, the tools' best adjusted settings. We aimed to eliminate fifty elements considered to contain identifying patient information. The tools were also examined for their respective methods of customization.ResultsOnly one tool was able to de-identify all required elements with the default setting. Not all of the toolkits provide a customizable de-identification profile. Six tools allowed changes by selecting the provided profiles, giving input through a graphical user interface (GUI) or configuration text file, or providing the appropriate command-line arguments. Using adjusted settings, four of those six toolkits were able to perform full de-identification.ConclusionOnly five tools could properly de-identify the defined DICOM elements, and in four cases, only after careful customization. Therefore, free DICOM toolkits should be used with extreme care to prevent the risk of disclosing PHI, especially when using the default configuration. In case optimal security is required, one of the five toolkits is proposed.Key Points• Free DICOM toolkits should be carefully used to prevent patient identity disclosure.• Each DICOM tool produces its own specific outcomes from the de-identification process.• In case optimal security is required, using one DICOM toolkit is proposed.

Towards fully automatic auto-tuning

Application auto-tuning has produced excellent results in a wide range of computing domains. Yet adapting an application to use this technology remains a predominately manual and labor intensive process. This paper explores first steps towards reducing adoption cost by focusing on two tasks: parameter identification and range selection. We show how these traditionally manual tasks can be automated in the context of Chapel, a parallel programming language developed by Cray Inc. Potential auto-tuning parameters may be inferred from existing Chapel applications by leveraging features unique to this language. After verification, these parameters may then be passed to an auto-tuner for an automatic search of the induced parameter space. To further automate adoption, we also present Tuna: an auto-tuning shell designed to tune applications by manipulating their command-line arguments. Finally, we demonstrate the immediate utility of this system by tuning two Chapel applications with little or no internal knowledge of the program source.

Accelerating genomic data publishing and sharing.

Elsevier Publishing is pleased to announce the launch of Genomics Data, an open access journal that considers articles on all aspects of genome-scale analysis. Over the past 20 years, genomics technology evolutions have drastically transformed biomedical researches. Powerful genomics tools have enabled whole-genome gene expression analysis, genotyping of millions of genetic markers in hundreds and thousands of individuals, and ultimately the complete genome sequencing of many organisms. Large amounts of genomics datasets have been generated and are being generated routinely. We see a huge need to publish genomics research results quickly and to allow the research community to openly share and exchange data. Toward this goal, Genomics Data aims to provide a useful format that links authored content with structured standard information about a genomic or functional genomic study, which shall maximize data searching, mining and reporting. Genomics Data publishes high quality and standardized reports summarizing results of microarray or sequencing studies in all types of organisms. The genomic and functional genomic data/results are intended to serve as points of record that are enriched with data generation strategies and methods, data QC metrics, data analysis process and algorithms, biological interpretation and conclusions. All articles will be peer-reviewed and verified by the editorial team. Authors may construct their reports with a downloadable genome report document template with the following or part of the following information: (1) Title of the paper; (2) Author name and affiliations; (3) Key words; (4) Platforms: microarray and/or next-gen sequencing (NGS), for example, genomic sequencing, RNA-Seq, Epi-Seq (e.g. Methylation, ChIP-Seq). The text can be organized into (1) Abstract; (2) Introduction; (3) Study Design; (4) Methods, SOP/protocol; (5) Results: i) raw data, to be submitted to GenBank, NCBI/NIH (SRA, GEO), and submission numbers should be cited in the manuscript; ii) data format, e.g. XML; iii) results (figures, tables); iv) data description and interpretation; (6) Conclusion and Discussion. In addition to original research articles, authors may also consider making contributions in the forms of (1) review, (2) method and/or SOP, (3) meeting report, and (4) commentary and perspective articles. Standard operating procedures (SOPs) should follow a general format of title, authors, institutions, overview and procedures used, and should categorize the type(s) of processes involved, revision versions, dates and any underlying dependencies. Genomics Data encourages submission of details such as command-line arguments or other run-time parameters, and operational thresholds. The sequential procedure of computational and manual operations for data capture and calculation should describe but not be limited to the following: i) assumptions involved; ii) steps for reproducibility; iii) points at which the quality of the process and its output may be evaluated. Genomics Data is user friendly, offering creative apps that allow authors to bring their data into the publishing environment. One example of this is a Genomics Data app that resides in the cloud. When the data sets are stored in the cloud, a simple to use app can help authors take the data into the review/publishing environment and thus add the credibility that can come only from the formal peer review process. This credibility is further enhanced by expert reviewers that have the additional means to review your work while using the same analytic tools that you used in the cloud. Data deposits and wide dissemination complete the user friendly functionality of the app. We intend to make articles and data published in Genomics Data indexed and searchable in major public databases such as PubMed Central.

The 2010 Rosetta Developers Meeting: Macromolecular Prediction and Design Meets Reproducible Publishing

The 2010 Rosetta Developers Meeting: Macromolecular Prediction and Design Meets Reproducible Publishing