Mac Operating Systems Research Articles

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data

MotivationThe analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns. ResultsWe introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data. Data and Code Availability and ImplementationLBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

PICKAPEP: An application for parameter calculation and visualization of cyclized and modified peptidomimetics.

The interest in peptides and especially in peptidomimetic structures has risen enormously in the past few years. Novel modification strategies including nonnatural amino acids, sophisticated cyclization strategies, and side chain modifications to improve the pharmacokinetic properties of peptides are continuously arising. However, a calculator tool accompanying the current development in peptide sciences towards modified peptides is missing. Herein, we present the application PICKAPEP, enabling the virtual construction and visualization of peptidomimetics ranging from well-known cyclized and modified peptides such as ciclosporin A up to fully self-designed peptide-based structures with custom amino acids. Calculated parameters include the molecular weight, the water-octanol partition coefficient, the topological polar surface area, the number of rotatable bonds, and the peptide SMILES code. To our knowledge, PICKAPEP is the first tool allowing users to add custom amino acids as building blocks and also the only tool giving the possibility to process large peptide libraries and calculate parameters for multiple peptides at once. We believe that PICKAPEP will support peptide researchers in their work and will find wide application in current as well as future peptide drug development processes. PICKAPEP is available open source for Windows and Mac operating systems (https://www.research-collection.ethz.ch/handle/20.500.11850/681174).

Digital Competences in Cybersecurity of Teachers in Training

The authors have developed a psychometric instrument with the purpose of assessing the digital competencies of teachers in aspects of cybersecurity. From the collection of data, different secondary objectives were carried out: (1) identify if there were significant differences regarding gender; (2) understand if there are significant differences based on various actions (use of different operating systems, two-step authentication, use of different passwords for applications and profiles), for each gender; (3) identify if the frequency of updating of passwords in LMS, emails, RRSS and apps correlate with the digital competencies of teachers in training, for each gender. For this purpose, an ex post facto ad hoc design was used based on a sample of 220 students enrolled in a Primary Education and Early Childhood Education course at the University of Malaga (Spain). The results show that the digital skills of these teachers of the future are at a low level, and that they find it hard to navigate their way in this new world. It is also concluded that there are significant differences with respect to gender in terms of various cybersecurity actions (use of the Mac operating system; two-step authentication). In addition, it was found that the frequency of updating passwords in LMS, emails, RRSS and apps affects the competences of the teachers in training in security and prevention, in both genders.

MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing.

In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation.

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research.

Nociceptors are a class of primary afferent neurons that signal potentially harmful noxious stimuli. An increase in nociceptor excitability occurs in acute and chronic pain conditions. This produces abnormal ongoing activity or reduced activation thresholds to noxious stimuli. Identifying the cause of this increased excitability is required for the development and validation of mechanism-based treatments. Single-neuron electrical threshold tracking can quantify nociceptor excitability. Therefore, we have developed an application to allow such measurements and demonstrate its use in humans and rodents. APTrack provides real-time data visualization and action potential identification using a temporal raster plot. Algorithms detect action potentials by threshold crossing and monitor their latency after electrical stimulation. The plugin then modulates the electrical stimulation amplitude using an up-down method to estimate the electrical threshold of the nociceptors. The software was built upon the Open Ephys system (V0.54) and coded in C++ using the JUCE framework. It runs on Windows, Linux, and Mac operating systems. The open-source code is available (https://github.com/Microneurography/APTrack). The electrophysiological recordings were taken from nociceptors in both a mouse skin-nerve preparation using the teased fiber method in the saphenous nerve and in healthy human volunteers using microneurography in the superficial peroneal nerve. Nociceptors were classified by their response to thermal and mechanical stimuli, as well as by monitoring the activity-dependent slowing of the conduction velocity. The software facilitated the experiment by simplifying the action potential identification through the temporal raster plot. We demonstrate real-time closed-loop electrical threshold tracking of single-neuron action potentials duringin vivohuman microneurography, for the first time, and duringex vivomouse electrophysiological recordings of C-fibers and Aδ-fibers. We establish proof of principle by showing that the electrical threshold of a human heat-sensitive C-fiber nociceptor is reduced by heating the receptive field. This plugin enables the electrical threshold tracking of single-neuron action potentials and allows the quantification of changes in nociceptor excitability.

SiREn (simulation of receptors and enzymes): An interactive simulation of enzyme kinetics and pharmacodynamic models

In an era of distance learning and the transition to virtual classrooms, there is a need for strategies to supplement online lectures in an interactive, dynamic way. At California Northstate University, there was a student-observed need to enhance distance learning during the COVID-19 pandemic. In this project, we developed a computer simulation program SiREn (Simulation of Receptors and Enzymes) as one attempt to enhance student learning and retention of key concepts in Enzyme Kinetics and Pharmacodynamics. SiREn is a student-driven idea, and an interprofessional collaboration between members of the College of Health Sciences, College of Graduate Studies, and College of Medicine at California Northstate University. This software was implemented in LabVIEW© language (National Instruments, Inc) and runs on both Windows and Mac Operating Systems. SiREn includes interactive simulations of the Michaelis-Menten, Lineweaver-Burk, Dose-Response, Therapeutic-Index models. All the relevant parameters for the simulation such as substrate concentration, Km, Vmax, and Kd can easily be modified by the user. Two versions of the software were developed: SiREn1 with a teaching emphasis for the College of Graduate Studies and SiREn2 customized for the College of Health Sciences. The latter has already been used in a Pharmacology class in the Spring 2022 semester. Feedback was collected from 16 pharmacology students. Overall, 63% of the users felt that SiREn2 improved their understanding of the Therapeutic Index concept, and 68.8% felt that SiREn2 facilitated self-paced, interactive learning by allowing a dynamic visualization of the formulas under different scenarios. These preliminary results suggest that integrating simulation for complex models is a useful addition to distance learning. This is the first stage of an ongoing project, and SiREn1 is currently being implemented in the Fall 2022 Pharmacology Course with added student suggestions for improvement.

TNT version 1.6, with a graphical interface for MacOS and Linux, including new routines in parallel.

A new graphical user interface (GUI) for the parsimony program TNT is presented that works under the Linux and Mac operating systems, as well as the Cygwin environment (which runs under Windows). The new interface is based on the GIMP Tool Kit, GTK (version 3). Formerly, only Windows versions of TNT had a GUI. The new interface improves upon the existing Windows GUI in several respects. These changes, together with several additions to the program since the publication of version 1.5, warrant a change in minor version, thus moving from version 1.5 to 1.6. Among the most notable improvements are the possibility to access graphical user dialogs by means of simple commands, to easily save trees in SVG format ("Scalable Vector Graphics") directly from any tree-diagram being displayed, and to manage analyses in parallel (using multiple processors, by means of the PVM system or "Parallel Virtual Machine"), as well as a generally more stable and consistent behaviour. As the binaries for the new version are compiled as native 64-bit applications, this removes the limitations for accessing large amounts of memory in the previous GUI Windows interface (which is a 32-bit application).

Evaluation of Operating System Using Electric Multi Criteria Decision Making Method

Operating system are computer software a user and a computer is between Communication an operating system (OS) is a All other applications on the computer Programs and programs through the launch program After loading into the computer A program that manages to make Limited application interface Requests for services through (API). Mostly, Apple Mac OS, Microsoft Windows and The top five, including Google's Android OS The IT sector is focused on OSes. Linux Operating system and Apple iOS. Operating system is most people are using the very difficult situation solving the problem in computer. ELECTRE (Elimination Et Choice Translating Reality) analysis using the ELECTRE methods, performance comparisons combining an Operating system 1, Operating system 2, Operating system 3, Operating system 4 Alternative value and Memory management, Process management, Storage management, protection and security, Software Features, Database management Evaluation Parameters in value. Operating system 1 is got the first rank whereas is the Operating system 4 is having the Lowest rank. In this paper Operating system for ELECTRE in Operating system 1 is got the first rank whereas is the Operating system 4 is having the Lowest rank.

Academic Tracker: Software for tracking and reporting publications associated with authors and grants.

In recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, it's important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub (https://moseleybioinformaticslab.github.io/academic_tracker/) and is also available on the Python Package Index (https://pypi.org/project/academic_tracker) for easy installation.

PDBsum1: A standalone program for generating PDBsum analyses.

PDBsum1 is a standalone set of programs to perform the same structural analyses as provided by the PDBsum web server (https://www.ebi.ac.uk/pdbsum). The server has pages for every entry in the Protein Data Bank (PDB) and can also process user-uploaded PDB files, returning a password-protected set of pages that are retained for around 3months. The standalone version described here allows for in-house processing and indefinite retention of the results. All data files and images are pre-generated, rather than on-the-fly as in the web version, so can be easily accessed. The program runs on Linux, Windows, and mac operating systems and is freely available for academic use at https://www.ebi.ac.uk/thornton-srv/software/PDBsum1.

VISROC 2.0: Updated Software for the Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses

The Receiver Operating Characteristics (ROC) method is used to evaluate the diagnostic accuracy of binary quantitative tests in a broad spectrum of disciplines, including medicine, physics of complex systems, geophysics, meteorology, etc. The estimation of the significance of the examined prediction method is of high importance and it's usually approximated by Monte Carlo calculations. To simplify this problem, a FORTRAN code called VISROC was submitted to the CPC Program Library in 2014. VISROC evaluates the significance of binary diagnostic and prognostic tools for a family of k-ellipses which are based on confidence ellipses and cover the whole ROC space. Since that time, the code has been significantly improved and several new capabilities have been added. Most importantly, a Graphical User Interface (GUI) has been implemented, which can be invoked using either the R shiny web application or the Python application available for Windows, Mac, and Linux operating systems, both of which are described here. New version program summaryProgram Title: VISROC 2.0CPC Library link to program files:https://doi.org/10.17632/dktybrydvf.1Licensing provisions: AGPLv3Programming language: R and PythonJournal reference of previous version: N.V. Sarlis, S.-R. G. Christopoulos, Comput. Phys. Commun. 185 (2014) 1172.Does the new version supersede the previous version?: YesReasons for the new version: Overcome previous version's limitations and implement GUI.Summary of revisions: R and Python applications, GUI, ability to input files containing ROC data.Nature of problem: The Receiver Operating Characteristics (ROC) [1] is a method used to evaluate the diagnostic ability of binary tests and prediction methods in a broad spectrum of disciplines. Apart from the sensitivity (or True Positive rate, TPr) and specificity (which is complementary to False Positive rate, FPr, i.e., specificity=1-FPr) measures, the estimation of the statistical significance of the examined method is of high importance. VISROC evaluates the significance of binary diagnostic and prognostic tools for a family of k-ellipses which are based on confidence ellipses and cover the whole ROC space.Solution method: Using the statistics of random binary predictions for a given value of the predictor threshold ϵt, one can construct the corresponding confidence ellipses. The envelope of these confidence ellipses is estimated by varying ϵt in the interval [0,1] and one obtains a new family of ellipses, called k-ellipses [2]. They cover the whole ROC space and lead to a well defined Area Under the Curve (AUC). Mason and Graham [3] have shown that AUC follows the Mann-Whitney U-statistics [4] which can be used [5] to estimate the statistical significance of each k-ellipse. As the transformation is invertible, any point on the ROC plane corresponds to a unique value of k, hence it belongs to a unique k-ellipse that allows the estimation of the probability (p-value) to obtain this point by chance. The present GUI applications provide the p-value on the ROC plane as well as the k-ellipses corresponding to the (p=)10%, 5% and 1% significance levels using as input the number of the positive (P) and negative (Q) cases to be predicted.

Rapid and simple analysis of short and long sequencing reads using DuesselporeTM.

Transcriptome analysis experiments enable researchers to gain extensive insights into the molecular mechanisms underlying cell physiology and disease. Oxford Nanopore Technologies (ONT) has recently been developed as a fast, miniaturized, portable, and cost-effective alternative to next-generation sequencing (NGS). However, RNA-Seq data analysis software that exploits ONT portability and allows scientists to easily analyze ONT data everywhere without bioinformatics expertise is not widely available. We developed DuesselporeTM, an easy-to-follow deep sequencing workflow that runs as a local webserver and allows the analysis of ONT data everywhere without requiring additional bioinformatics tools or internet connection. DuesselporeTM output includes differentially expressed genes and further downstream analyses, such as variance heatmap, disease and gene ontology plots, gene concept network plots, and exports customized pathways for different cellular processes. We validated DuesselporeTM by analyzing the transcriptomic changes induced by PCB126, a dioxin-like PCB, and a potent aryl hydrocarbon receptor (AhR) agonist in human HaCaT keratinocytes, a well-characterized model system. DuesselporeTM was specifically developed to analyze ONT data, but we also implemented NGS data analysis. DuesselporeTM is compatible with Linux, Microsoft, and Mac operating systems and allows convenient, reliable, and cost-effective analysis of ONT and NGS data.

High performance multi-platform computing for large-scale image-based finite element modeling of bone

BackgroundImage-based finite element (FE) modeling of bone is a non-invasive method to estimate bone stiffness and strength. High-resolution imaging data as input allows for inclusion of bone microarchitecture but results in large amounts of data unsuitable for traditional FE solvers. Bone-specific mesh-free solvers have been developed over the past 20 years to improve on memory efficiency in simulated bone loading applications. The objective of this study was to provide linear performance benchmarking for a bone-specific, mesh-free solver (FAIM) using µCT and HR-pQCT image data on Mac, Linux, and Windows operating systems using both single- and multi-thread CPU and GPU processing. MethodsThe focus is on the linear gradient-descent solver using standardized uniaxial loading of bone models from µCT, and first- and second-generation HR-pQCT scans of the radius and tibia. Convergence, speedup, memory, and batch performance tests were completed using CPUs and GPUs on three laboratory-based systems with Windows, Linux, and Mac operating systems. ResultsAlthough varying by system and model size, time-per-iteration was as low as 0.03 s when an HR-pQCT-based radius model (6.45 million DOF) was solved with 3 GPUs. Strong scaling was achieved with GPU and CPU parallel processing, with strong parallel efficiencies when models were solved using 3 GPUs or ≤ 10 CPU threads. Errors in force, strain energy density, and Von Mises stress were as low as 0.1% when a convergence tolerance of 10−3 or smaller was used. ConclusionThe results of this study indicate that to maximize computational efficiency and minimize model solution times using FAIM software under the standardized tested conditions using µCT, XCT1 and XCT2 HR-pQCT image data, convergence tolerance set to 10−4, and 10 threads or 2 GPUs are sufficient for efficient solution times. Less strict convergence tolerances will improve solution times but will introduce more error in the outcome measures.

Fast and flexible processing of large FRET image stacks using the FRET-IBRA toolkit.

Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

Spiky: An ImageJ Plugin for Data Analysis of Functional Cardiac and Cardiomyocyte Studies

Introduction and objective: Nowadays, investigations of heart physiology and pathophysiology rely more and more upon image analysis, whether for the detection and characterization of events in single cells or for the mapping of events and their characteristics across an entire tissue. These investigations require extensive skills in image analysis and/or expensive software, and their reproducibility may be a concern. Our objective was to build a robust, reliable and open-source software tool to quantify excitation–contraction related experimental data at multiple scales, from single isolated cells to the whole heart. Methods and results: A free and open-source ImageJ plugin, Spiky, was developed to detect and analyze peaks in experimental data streams. It allows rapid and easy analysis of action potentials, intracellular calcium transient and contraction data from cardiac research experiments. As shown in the provided examples, both classical bi-dimensional data (XT signals) and video data obtained from confocal microscopy and optical mapping experiments (XYT signals) can be analyzed. Spiky was written in ImageJ Macro Language and JAVA, and works under Windows, Mac and Linux operating systems. Conclusion: Spiky provides a complete working interface to process and analyze cardiac physiology research data.

Understanding Usability Test of Operating Systems in University: Evidence from Indonesia

The operating system is the most important computer system software. Currently, there are several operating systems that are so popular, including Windows and macOS. Usability measurements were made between the Windows and macOS operating systems. This study aims to measure the comparison of usage between Windows and Mac operating systems in lectures at the University. This study used a questionnaire with 13 questions asked and 250 respondents received, then analyzed the data using a Guttman measuring scale. The results show that the Windows operating system is more widely used with a much higher percentage than MacOS. The results of this study also prefer the Windows operating system with 84.61% while MacOS with 11.38%. Because starting in terms of popularity among university students, many use Windows because it is considered easier and the applications provided for free are different from prepaid MacOS

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging.

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond its historical association with motor control. Structural and functional neuroimaging studies have further refined understanding of the functional neuroanatomy of the cerebellum beyond its anatomical divisions, highlighting the need for the examination of individual cerebellar subunits in healthy variability and neurological diseases. This paper presents a standardized pipeline for examining cerebellum grey matter morphometry that combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation (Automatic Cerebellum Anatomical Parcellation using U-Net Locally Constrained Optimization; ACAPULCO) and voxel-based registration of the cerebellum (Spatially Unbiased Infra-tentorial Template; SUIT) for volumetric quantification. The pipeline has broad applicability to a range of neurological diseases and is fully automated, with manual intervention only required for quality control of the outputs. The pipeline is freely available, with substantial accompanying documentation, and can be run on Mac, Windows, and Linux operating systems. The pipeline is applied in a cohort of individuals with Friedreich ataxia (FRDA), and representative results, as well as recommendations on group-level inferential statistical analyses, are provided. This pipeline could facilitate reliability and reproducibility across the field, ultimately providing a powerful methodological approach for characterizing and tracking cerebellar structural changes in neurological diseases.

Comparative Analysis of Power Consumption of the Linux and its Distribution Operating Systems vs Windows and Mac Operating Systems.

Comparative Analysis of Power Consumption of the Linux and its Distribution Operating Systems vs Windows and Mac Operating Systems.

A Correlational Study between Growth Mindset, Delay of Gratification and Subjective Well-being during COVID-19

The current study was conducted in order to determine whether there exists a relationship between Growth Mindset, Delay of Gratification and Subjective Well-Being (SWB) in the context of the COVID-19 pandemic. Data were collected from two hundred and forty-six (n=246) undergraduate students enrolled in educational institutions in the city of Mumbai, Maharashtra, India. Scales used to assess the aforementioned variables were the 3-item Growth Mindset Scale (Dweck, 1999, 2006), Delay of Gratification Inventory (DGI-10) (Hoerger. M., et al 2011) and Satisfaction with Life Scale (Diener. E, 1985) respectively – all of which were administered online in light of the COVID-19 pandemic. IBM SPSS Statistics for Mac Operating System (ver. 27.0.1.0). was used in order to compute a correlational analysis; and all, except DGI and SWB, were found to have no significant correlation. Moreover, the correlation found between DOG and SWB was low (r=0.254, p

EntropyHub: An open-source toolkit for entropic time series analysis.

An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website– www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.