Geppetto: a reusable modular open platform for exploring neuroscience data and models

Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications, including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for Drosophila melanogaster neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data. Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way. The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly customizable frontend. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

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An open Platform for High-resolution Light-based Control of Microscopic Collectives

10.22541/au.164191998.84940186/v1 ◽

2022 ◽

Author(s):

Ana Rubio Denniss ◽

Thomas E. Gorochowski ◽

Sabine Hauert

Keyword(s):

High Resolution ◽

Open Source ◽

De Novo ◽

Low Cost ◽

Local Interactions ◽

Open Platform ◽

The Individual ◽

And Control

Engineering microscopic collectives of cells or microrobots is challenging due to the often-limited capabilities of the individual agents, our inability to reliably program their motion and local interactions, and difficulties visualising their behaviours. Here, we present a low-cost, modular and open-source Dynamic Optical MicroEnvironment (DOME) and demonstrate its ability to augment microagent capabilities and control collective behaviours using light. The DOME offers an accessible means to study complex multicellular phenomena and implement de-novo microswarms with desired functionalities. Corresponding author(s) Email: [email protected] [email protected]

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An open platform for high-resolution light-based control of microscopic collectives

10.1101/2020.12.28.424547 ◽

2020 ◽

Author(s):

Ana Rubio Denniss ◽

Thomas E. Gorochowski ◽

Sabine Hauert

Keyword(s):

High Resolution ◽

Open Source ◽

De Novo ◽

Low Cost ◽

Local Interactions ◽

Open Platform ◽

The Individual ◽

And Control

AbstractEngineering microscopic collectives of cells or microrobots is challenging due to the often-limited capabilities of the individual agents, our inability to program their motion and local interactions, and difficulties visualising their behaviours. Here, we present a low-cost, modular and open-source Dynamic Optical MicroEnvironment (DOME) and demonstrate its ability to augment microagent capabilities and control collective behaviours using light. The DOME offers an accessible means to study complex multicellular phenomena and implement de-novo microswarms with desired functionalities.

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SynBiopython: an open-source software library for Synthetic Biology

Synthetic Biology ◽

10.1093/synbio/ysab001 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Jing Wui Yeoh ◽

Neil Swainston ◽

Peter Vegh ◽

Valentin Zulkower ◽

Pablo Carbonell ◽

...

Keyword(s):

Synthetic Biology ◽

Open Source ◽

Open Source Software ◽

Development Projects ◽

Software Library ◽

Current State ◽

Starting Point ◽

Common Problems ◽

Data Tracking ◽

Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

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Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Sensors ◽

10.3390/s21020572 ◽

2021 ◽

Vol 21 (2) ◽

pp. 572

Author(s):

Mads Jochumsen ◽

Taha Al Muhammadee Janjua ◽

Juan Carlos Arceo ◽

Jimmy Lauber ◽

Emilie Simoneau Buessinger ◽

...

Keyword(s):

Open Source ◽

Neural Plasticity ◽

Motor Evoked Potentials ◽

Low Cost ◽

True Positive Rate ◽

Positive Rate ◽

3D Printed ◽

Major Factors ◽

And Control ◽

Bci System

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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An open-source analytical platform for analysis of C. elegans swimming-induced paralysis

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2014.04.024 ◽

2014 ◽

Vol 232 ◽

pp. 58-62 ◽

Cited By ~ 5

Author(s):

J. Andrew Hardaway ◽

Jing Wang ◽

Paul A. Fleming ◽

Katherine A. Fleming ◽

Sarah M. Whitaker ◽

...

Keyword(s):

Open Source ◽

C Elegans

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QuakeMigrate: a Modular, Open-Source Python Package for Automatic Earthquake Detection and Location

10.1002/essoar.10505850.1 ◽

2021 ◽

Author(s):

Tom Winder ◽

Conor Bacon ◽

Jonathan Smith ◽

Thomas Hudson ◽

Tim Greenfield ◽

...

Keyword(s):

Open Source ◽

Earthquake Detection ◽

Python Package

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tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

BMC Bioinformatics ◽

10.1186/s12859-021-03967-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Charlie M. Carpenter ◽

Daniel N. Frank ◽

Kayla Williamson ◽

Jaron Arbet ◽

Brandie D. Wagner ◽

...

Keyword(s):

Microbial Communities ◽

Open Source ◽

Data Structures ◽

Negative Binomial ◽

Rocky Mountain ◽

R Package ◽

Microbiome Analysis ◽

External Data ◽

Data Tables ◽

Microbiome Data

Abstract Background The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. Results We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. Conclusions tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.

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Industry 4.0 Digital Technologies for data collection and control

10.33920/pro-2-2106-04 ◽

2021 ◽

pp. 43-58

Author(s):

S. S. Yudachev ◽

P. A. Monakhov ◽

N. A. Gordienko

Keyword(s):

Data Collection ◽

Open Source ◽

Software Defined Radio ◽

Block Diagram ◽

Data Access ◽

Gnu Radio ◽

Time Data ◽

Object A ◽

Data Propagation ◽

And Control

This article describes an attempt to create open source LabVIEW software, equivalent to data collection and control software. The proposed solution uses GNU Radio, OpenCV, Scilab, Xcos, and Comedi in Linux. GNU Radio provides a user-friendly graphical interface. Also, GNU Radio is a software-defined radio that conducts experiments in practice using software rather than the usual hardware implementation. Blocks for data propagation, code deletion with and without code tracking are created using the zero correlation zone code (ZCZ, a combination of ternary codes equal to 1, 0, and –1, which is specified in the program). Unlike MATLAB Simulink, GNU Radio is open source, i. e. free, and the concepts can be easily accessed by ordinary people without much programming experience using pre-written blocks. Calculations can be performed using OpenCV or Scilab and Xcos. Xcos is an application that is part of the Scilab mathematical modeling system, and it provides developers with the ability to design systems in the field of mechanics, hydraulics and electronics, as well as queuing systems. Xcos is a graphical interactive environment based on block modeling. The application is designed to solve problems of dynamic and situational modeling of systems, processes, devices, as well as testing and analyzing these systems. In this case, the modeled object (a system, device or process) is represented graphically by its functional parametric block diagram, which includes blocks of system elements and connections between them. The device drivers listed in Comedi are used for real-time data access. We also present an improved PyGTK-based graphical user interface for GNU Radio. English version of the article is available at URL: https://panor.ru/articles/industry-40-digital-technology-for-data-collection-and-management/65216.html

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