scholarly journals Chipper: Open-source software for semi-automated segmentation and analysis of birdsong and other natural sounds

2019 ◽  
Author(s):  
Abigail M. Searfoss ◽  
James C. Pino ◽  
Nicole Creanza
Keyword(s):  
Open Source ◽  
Large Scale ◽  
List Type ◽  
Natural Sounds ◽  
Science Data ◽  
Field Recordings ◽  
Multiple User ◽  
Cross Platform ◽  
Multiple Species ◽  
User Friendly

AbstractAudio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high-throughput methods of analysis for researchers. Specifically, there is a need in the community for open-source methods that are tailored to recordings of varying qualities and from multiple species collected in nature.We developed Chipper, a Python-based software to semi-automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper’s accuracy, repeatability, and reproducibility.Chipper provides an effective way to quickly generate reproducible estimates of birdsong features. The cross-platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analyzing field recordings.Chipper streamlines the processing of audio recordings with multiple user-friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen-science data and increases the feasibility of large-scale multi-species birdsong studies.

scholarly journals QuickStitch for seamless stitching of confocal mosaics through high-pass filtering and recursive normalization

2016 ◽  
Author(s):  
Pavel A. Brodskiy ◽  
Paulina M. Eberts ◽  
Cody Narciso ◽  
Jochen Kursawe ◽  
Alexander Fletcher ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Imaging System ◽  
Tissue Level ◽  
Cellular Level ◽  
Specific Information ◽  
Open Source Tool ◽  
The Individual ◽  
User Friendly ◽  
High Pass

ABSTRACTFluorescence micrographs naturally exhibit darkening around their edges (vignetting), which makes seamless stitching challenging. If vignetting is not corrected for, a stitched image will have visible seams where the individual images (tiles) overlap, introducing a systematic error into any quantitative analysis of the image. Although multiple vignetting correction methods exist, there remains no open-source tool that robustly handles large 2D immunofluorescence-based mosaic images. Here, we develop and validate QuickStitch, a tool that applies a recursive normalization algorithm to stitch large-scale immunofluorescence-based mosaics without incurring vignetting seams. We demonstrate how the tool works successfully for tissues of differing size, morphology, and fluorescence intensity. QuickStitch requires no specific information about the imaging system. It is provided as an open-source tool that is both user friendly and extensible, allowing straightforward incorporation into existing image processing pipelines. This enables studies that require accurate segmentation and analysis of high-resolution datasets when parameters of interest include both cellular-level phenomena and larger tissue-level regions of interest.

scholarly journals BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

2019 ◽  
Author(s):  
Manoj Kumar ◽  
Cameron Thomas Ellis ◽  
Qihong Lu ◽  
Hejia Zhang ◽  
Mihai Capota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Open Source ◽  
Programming Languages ◽  
High Performance ◽  
Large Scale ◽  
Markov Models ◽  
Matrix Analysis ◽  
Fmri Analysis ◽  
User Friendly

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

scholarly journals PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

2021 ◽  
Vol 15 ◽  
Author(s):  
Babak Zandi ◽  
Moritz Lode ◽  
Alexander Herzog ◽  
Georgios Sakas ◽  
Tran Quoc Khanh
Keyword(s):  
Open Source ◽  
Ganglion Cells ◽  
Detection Accuracy ◽  
Retinal Ganglion ◽  
Detection Algorithms ◽  
Pupil Detection ◽  
Cross Platform ◽  
Measurement Validation ◽  
Pupil Tracking ◽  
User Friendly

The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT.

scholarly journals NeuroPM toolbox: integrating Molecular, Neuroimaging and Clinical data for Characterizing Neuropathological Progression and Individual Therapeutic Needs

2020 ◽  
Author(s):  
Yasser Iturria-Medina ◽  
Felix Carbonell ◽  
Atoussa Assadi ◽  
Quadri Adewale ◽  
Ahmed F. Khan ◽  
...  
Keyword(s):  
Open Access ◽  
High Performance ◽  
Large Scale ◽  
Synergistic Interactions ◽  
Academic Researchers ◽  
Cross Platform ◽  
Therapeutic Needs ◽  
User Friendly ◽  
Performance Computing

There is a critical need for a better multiscale and multifactorial understanding of neurological disorders, covering from genes to neuroimaging to clinical factors and treatments effects. Here we present NeuroPM-box, a cross-platform, user-friendly and open-access software for characterizing multiscale and multifactorial brain pathological mechanisms and identifying individual therapeutic needs. The implemented methods have been extensively tested and validated in the neurodegenerative context, but there is not restriction in the kind of disorders that can be analyzed. By using advanced analytic modeling of molecular, neuroimaging and/or cognitive/behavioral data, this framework allows multiple applications, including characterization of: (i) the series of sequential states (e.g. transcriptomic, imaging or clinical alterations) covering decades of disease progression, (ii) intra-brain spreading of pathological factors (e.g. amyloid and tau misfolded proteins), (iii) synergistic interactions between multiple brain biological factors (e.g. direct tau effects on vascular and structural properties), and (iv) biologically-defined patients stratification based on therapeutic needs (i.e. optimum treatments for each patient). All models outputs are biologically interpretable. A 4D-viewer allows visualization of spatiotemporal brain (dis)organization. Originally implemented in MATLAB, NeuroPM-box is compiled as standalone application for Windows, Linux and Mac environments: neuropm-lab.com/software. In a regular workstation, it can analyze over 150 subjects per day, reducing the need for using clusters or High-Performance Computing (HPC) for large-scale datasets. This open-access tool for academic researchers may significantly contribute to a better understanding of complex brain processes and to accelerating the implementation of Precision Medicine (PM) in neurology.

scholarly journals Phylo-Node: a molecular phylogenetic toolkit using Node.js

2016 ◽  
Author(s):  
Damien M. O’Halloran
Keyword(s):  
Open Source ◽  
Phylogenetic Analyses ◽  
Phylogeny Reconstruction ◽  
Evolutionary Modeling ◽  
Multiple Sequence ◽  
Server Side ◽  
Molecular Phylogenetic ◽  
Cross Platform ◽  
User Friendly ◽  
Simple Integration

ABSTRACTBackground: Node.js is an open-source and cross-platform environment that provides a JavaScript codebase for back-end server-side applications. JavaScript has been used to develop very fast and user-friendly front-end tools for bioinformatic and phylogenetic analyses. However, no such toolkits are available using Node.js to conduct comprehensive molecular phylogenetic analysis.Results: To address this problem, I have developed, phylo-node, which was developed using Node.js and provides a stable and scalable toolkit that allows the user to perform diverse molecular and phylogenetic tasks. phylo-node can execute the analysis and process the resulting outputs from a suite of software options that provides tools for read processing and genome alignment, sequence retrieval, multiple sequence alignment, primer design, evolutionary modeling, and phylogeny reconstruction. Furthermore, phylo-node enables the user to deploy server dependent applications, and also provides simple integration and interoperation with other Node modules and languages using Node inheritance patterns, and a customized piping module to support the production of diverse pipelines.Conclusions: phylo-node is open-source and freely available to all users without sign-up or login requirements. All source code and user guidelines are openly available at the GitHub repository: https://github.com/dohalloran/phylo-node

scholarly journals Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

2021 ◽  
Author(s):  
Mick A Phillips ◽  
David Miguel Susano Pinto ◽  
Nicholas Hall ◽  
Julio Mateos-Langerak ◽  
Richard M Parton ◽  
...  
Keyword(s):  
Open Source ◽  
Adaptive Optics ◽  
Medical Science ◽  
Machine Learning Algorithms ◽  
Spatial Light Modulators ◽  
List Type ◽  
Structured Illumination ◽  
Wide Range ◽  
Automated Microscopy ◽  
User Friendly

AbstractWe have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based GUI environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near-instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.HighlightsUser-friendly setup and use for simple to complex bespoke microscopes.Facilitates collaborations between biomedical scientists and microscope technologists.Touchscreen for near-instantaneous navigation of specimen landscape.Uses Python-Microscope, for abstracted open source hardware device control.Well-suited for user training of AI-algorithms for automated microscopy.

scholarly journals AN OPEN-SOURCE CANOPY CLASSIFICATION SYSTEM USING MACHINE-LEARNING TECHNIQUES WITHIN A PYTHON FRAMEWORK

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 175-182
Author(s):  
O. Smith ◽  
H. Cho
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Classification System ◽  
Large Scale ◽  
Tree Canopy ◽  
Machine Learning Techniques ◽  
Remotely Sensed Data ◽  
High Resolution Imagery ◽  
Learning Techniques ◽  
Cross Platform

Abstract. Studying deforestation has been an important topic in forestry research. Especially, canopy classification using remotely sensed data plays an essential role in monitoring tree canopy on a large scale. As remote sensing technologies advance, the quality and resolution of satellite imagery have significantly improved. Oftentimes, leveraging high-resolution imagery such as the National Agriculture Imagery Program (NAIP) imagery requires proprietary software. However, the lack of insight into the inner workings of such software and the inability of modifying its code lead many researchers towards open-source solutions. In this research, we introduce CanoClass, an open-source cross-platform canopy classification system written in Python. CanoClass utilizes the Random Forest and Extra Trees algorithms provided by scikit-learn to classify canopy using remote sensing imagery. Based on our benchmark tests, this new canopy classification system was 283 % to 464 % faster than commercial Feature Analyst, but it produced comparable results with a similarity of 87.56 % to 87.62 %.

scholarly journals MaGenDB: a functional genomics hub for Malvaceae plants

2019 ◽  
Author(s):  
Dehe Wang ◽  
Weiliang Fan ◽  
Xiaolong Guo ◽  
Kai Wu ◽  
Siyu Zhou ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Large Scale ◽  
Expression Profiles ◽  
Individual Species ◽  
Omics Data ◽  
Comparison System ◽  
Dynamic Expression ◽  
Biological Discovery ◽  
Multiple Species ◽  
User Friendly

Abstract Malvaceae is a family of flowering plants containing many economically important plant species including cotton, cacao and durian. Recently, the genomes of several Malvaceae species have been decoded, and many omics data were generated for individual species. However, no integrative database of multiple species, enabling users to jointly compare and analyse relevant data, is available for Malvaceae. Thus, we developed a user-friendly database named MaGenDB (http://magen.whu.edu.cn) as a functional genomics hub for the plant community. We collected the genomes of 13 Malvaceae species, and comprehensively annotated genes from different perspectives including functional RNA/protein element, gene ontology, KEGG orthology, and gene family. We processed 374 sets of diverse omics data with the ENCODE pipelines and integrated them into a customised genome browser, and designed multiple dynamic charts to present gene/RNA/protein-level knowledge such as dynamic expression profiles and functional elements. We also implemented a smart search system for efficiently mining genes. In addition, we constructed a functional comparison system to help comparative analysis between genes on multiple features in one species or across closely related species. This database and associated tools will allow users to quickly retrieve large-scale functional information for biological discovery.

scholarly journals abmR: an R package for agent-based model analysis of large-scale movements across taxa

2021 ◽  
Author(s):  
Benjamin Gochanour ◽  
Javier Fernández-López ◽  
Andrea Contina
Keyword(s):  
Open Source ◽  
Disease Ecology ◽  
Large Scale ◽  
Animal Movement ◽  
R Package ◽  
Theoretical Background ◽  
Agent Based Modeling ◽  
List Type ◽  
Agent Based ◽  
Continental Scale

AbstractAgent-based modeling (ABM) shows promise for animal movement studies. However, a robust, open-source, and spatially explicit ABM coding platform is currently lacking.We present abmR, an R package for conducting continental-scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein-Uhlenbeck (OU) model.The theoretical background for abmR is discussed and the main functionalities are illustrated using two example populations.Potential future additions to this open-source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with other R movement modeling packages.

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