scholarly journals Innovative management of accelerometry, inertial, acoustic, and satellite data using netCDF and Postgres

2018 ◽  
Author(s):  
Alex Nunes ◽  
Damian Lidgard ◽  
Franziska Broell
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Large Scale ◽  
Cluster Computing ◽  
Movement Ecology ◽  
Application Programming Interface ◽  
Easy Access ◽  
Application Programming ◽  
Original Size ◽  
User Friendly

In 2015, as part of the Ocean Tracking Network’s bioprobe initiative, 20 grey seals (Halichoerus grypus) were tagged with a high-resolution (> 30 Hz) inertial tags (> 30 Hz), a depth-temperature satellite tag (0.1 Hz), and an acoustic transceiver on Sable Island for 6 months. Comparable to similar large-scale studies in movement ecology, the unprecedented size of the data (gigabytes for a single seal) collected by these instruments raises new challenges in efficient database management. Here we propose the utility of Postgres and netCDF for storing the biotelemetry data and associated metadata. While it was possible to write the lower-resolution (acoustic and satellite) data to a Postgres database, netCDF was chosen as the format for the high-resolution movement (acceleration and inertial) records. Even without access to cluster computing, data could be efficiently (CPU time) recorded, as 920 million records were written in < 3 hours. ERDDAP was used to access and link the different datastreams with a user-friendly Application Programming Interface. This approach compresses the data to a fifth of its original size, and storing the data in a tree-like structure enables easy access and visualization for the end user.

scholarly journals Innovative management of accelerometry, inertial, acoustic, and satellite data using netCDF and Postgres

2018 ◽  
Author(s):  
Alex Nunes ◽  
Damian Lidgard ◽  
Franziska Broell
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Large Scale ◽  
Cluster Computing ◽  
Movement Ecology ◽  
Application Programming Interface ◽  
Easy Access ◽  
Application Programming ◽  
Original Size ◽  
User Friendly

In 2015, as part of the Ocean Tracking Network’s bioprobe initiative, 20 grey seals (Halichoerus grypus) were tagged with a high-resolution (> 30 Hz) inertial tags (> 30 Hz), a depth-temperature satellite tag (0.1 Hz), and an acoustic transceiver on Sable Island for 6 months. Comparable to similar large-scale studies in movement ecology, the unprecedented size of the data (gigabytes for a single seal) collected by these instruments raises new challenges in efficient database management. Here we propose the utility of Postgres and netCDF for storing the biotelemetry data and associated metadata. While it was possible to write the lower-resolution (acoustic and satellite) data to a Postgres database, netCDF was chosen as the format for the high-resolution movement (acceleration and inertial) records. Even without access to cluster computing, data could be efficiently (CPU time) recorded, as 920 million records were written in < 3 hours. ERDDAP was used to access and link the different datastreams with a user-friendly Application Programming Interface. This approach compresses the data to a fifth of its original size, and storing the data in a tree-like structure enables easy access and visualization for the end user.

scholarly journals An IoT-Based Smart Home Automation System

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3784
Author(s):  
Cristina Stolojescu-Crisan ◽  
Calin Crisan ◽  
Bogdan-Petru Butunoi
Keyword(s):  
Application Programming Interface ◽  
Smartphone Application ◽  
Raspberry Pi ◽  
Automation System ◽  
Home Automation ◽  
Network Connection ◽  
Communication Scheme ◽  
Application Programming ◽  
Home Automation System ◽  
User Friendly

Home automation has achieved a lot of popularity in recent years, as day-to-day life is getting simpler due to the rapid growth of technology. Almost everything has become digitalized and automatic. In this paper, a system for interconnecting sensors, actuators, and other data sources with the purpose of multiple home automations is proposed. The system is called qToggle and works by leveraging the power of a flexible and powerful Application Programming Interface (API), which represents the foundation of a simple and common communication scheme. The devices used by qToggle are usually sensors or actuators with an upstream network connection implementing the qToggle API. Most devices used by qToggle are based on ESP8266/ESP8285 chips and/or on Raspberry Pi boards. A smartphone application has been developed that allows users to control a series of home appliances and sensors. The qToggle system is user friendly, flexible, and can be further developed by using different devices and add-ons.

scholarly journals A comparison of Galactic electron density models using PyGEDM

2021 ◽  
Vol 38 ◽  
Author(s):  
D. C. Price ◽  
C. Flynn ◽  
A. Deller
Keyword(s):  
Electron Density ◽  
Large Scale ◽  
Galactic Halo ◽  
Application Programming Interface ◽  
Dispersion Measure ◽  
Radio Pulsars ◽  
Distribution Models ◽  
Distance Measurements ◽  
Application Programming ◽  
The Impact

Abstract Galactic electron density distribution models are crucial tools for estimating the impact of the ionised interstellar medium on the impulsive signals from radio pulsars and fast radio bursts. The two prevailing Galactic electron density models (GEDMs) are YMW16 (Yao et al. 2017, ApJ, 835, 29) and NE2001 (Cordes & Lazio 2002, arXiv e-prints, pp astro–ph/0207156). Here, we introduce a software package PyGEDM which provides a unified application programming interface for these models and the YT20 (Yamasaki & Totani 2020, ApJ, 888, 105) model of the Galactic halo. We use PyGEDM to compute all-sky maps of Galactic dispersion measure (DM) for YMW16 and NE2001 and compare the large-scale differences between the two. In general, YMW16 predicts higher DM values towards the Galactic anticentre. YMW16 predicts higher DMs at low Galactic latitudes, but NE2001 predicts higher DMs in most other directions. We identify lines of sight for which the models are most discrepant, using pulsars with independent distance measurements. YMW16 performs better on average than NE2001, but both models show significant outliers. We suggest that future campaigns to determine pulsar distances should focus on targets where the models show large discrepancies, so future models can use those measurements to better estimate distances along those line of sight. We also suggest that the Galactic halo should be considered as a component in future GEDMs, to avoid overestimating the Galactic DM contribution for extragalactic sources such as FRBs.

emiRIT: A text-mining based resource for microRNA information

2020 ◽  
Author(s):  
Debarati Roychowdhury ◽  
Samir Gupta ◽  
Xihan Qin ◽  
Cecilia N. Arighi ◽  
K. Vijay-Shanker
Keyword(s):  
Text Mining ◽  
Information Needs ◽  
Large Scale ◽  
Biological Process ◽  
Essential Gene ◽  
Mirna Gene ◽  
Easy Access ◽  
Context Specific ◽  
Biological Entities ◽  
User Friendly

AbstractMotivationmicroRNAs (miRNAs) are essential gene regulators and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications, and developing new hypotheses built on previous knowledge. Here, we present emiRIT, a text mining-based resource, which presents miRNA information mined from the literature through a user-friendly interface.ResultsWe collected 149,233 miRNA-PubMed ID pairs from Medline between January 1997 to May 2020. emiRIT currently contains miRNA-gene regulation (60,491 relations); miRNA-disease (cancer) (12,300 relations); miRNA-biological process and pathways (23,390 relations); and circulatory miRNAs in extracellular locations (3,782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively.ConclusionWe provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large-scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, in the absence of gold standards, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of diseases. Database URL: https://research.bioinformatics.udel.edu/emirit/

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics

2016 ◽  
Vol 14 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Ruifeng Hu ◽  
Xiaobo Sun
Keyword(s):  
Free Energy ◽  
Nucleic Acid ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binding Free Energy ◽  
Target Prediction ◽  
Easy Access ◽  
Web Based ◽  
Size Limitation ◽  
User Friendly

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

scholarly journals The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

2018 ◽  
Author(s):  
Alberto Noronha ◽  
Jennifer Modamio ◽  
Yohan Jarosz ◽  
Nicolas Sompairac ◽  
German Preciat Gonzàlez ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Current Knowledge ◽  
Application Programming Interface ◽  
Data Interpretation ◽  
Human Metabolism ◽  
Human Genes ◽  
Application Programming ◽  
Underlying Mechanisms ◽  
User Friendly ◽  
Programming Interface

AbstractA multitude of factors contribute to complex diseases and can be measured with “omics” methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, http://vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources “Human metabolism”, “Gut microbiome”, “Disease”, “Nutrition”, and “ReconMaps”. The VMH captures 5,180 unique metabolites, 17,730 unique reactions, 3,288 human genes, 255 Mendelian diseases, 818 microbes, 632,685 microbial genes, and 8,790 food items. The VMH’s unique features are i) the hosting the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; ii) seven human metabolic maps for data visualization; iii) a nutrition designer; iv) a user-friendly webpage and application-programming interface to access its content; and v) user feedback option for community engagement. We demonstrate with four examples the VMH’s utility. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

scholarly journals Molint 1.0: An Application Programming Interface Framework for the Computation of Molecular Integrals and Their First Derivatives for the Density-Fitted Methods

2020 ◽  
Author(s):  
Uğur Bozkaya
Keyword(s):  
Tensor Decomposition ◽  
Application Programming Interface ◽  
Efficient Computation ◽  
Property Evaluation ◽  
Hybrid Code ◽  
Programming Techniques ◽  
Molecular Integrals ◽  
Application Programming ◽  
User Friendly ◽  
Programming Interface

The efficient computation of molecular integrals and their derivatives is a crucial step in molecular property evaluation in modern quantum chemistry. As an integral tensor decomposition technique, the density-fitting (DF) approach becomes a popular tool to reduce the memory and disk requirements for the electron repulsion integrals. In this study, an application programming interface (API) framework, denoted Molint (MFW), for the computation of molecular integrals and their first derivatives, over contracted Gaussian functions, for the density-fitted methods is reported. The MFW is free software and it includes overlap, dipole, kinetic, potential, metric, and 3-index integrals, and their first derivatives. Furthermore, the MFW provides a smooth approach to build the Fock matrix and evaluate analytic gradients for the density-fitted methods. The MFW is a C++/Fortran hybrid code, which can take advantage of shared-memory parallel programming techniques. Our results demonstrate that the MFW is an efficient and user-friendly API for the computation of molecular integrals and their first derivatives.

scholarly journals Comparing protein structures with RINspector automation in Cytoscape

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 563 ◽  
Author(s):  
Guillaume Brysbaert ◽  
Théo Mauri ◽  
Marc F. Lensink
Keyword(s):  
Protein Structures ◽  
Application Programming Interface ◽  
Easy Access ◽  
Network Centrality ◽  
Backbone Flexibility ◽  
Residue Interaction ◽  
Application Programming ◽  
Quaternary Structures ◽  
The Impact ◽  
Programming Interface

Residue interaction networks (RINs) have been shown to be relevant representations of the tertiary or quaternary structures of proteins, in particular thanks to network centrality analyses. We recently developed the RINspector 1.0.0 Cytoscape app, which couples centrality analyses with backbone flexibility predictions. This combined approach permits the identification of crucial residues for the folding or function of the protein that can constitute good targets for mutagenesis experiments. Here we present an application programming interface (API) for RINspector 1.1.0 that enables interplay between Cytoscape, RINspector and external languages, such as R or Python. This API provides easy access to batch centrality calculations and flexibility predictions, and allows for the easy comparison of results between different structures. These comparisons can lead to the identification of specific and conserved central residues, and show the impact of mutations to these and other residues on the flexibility of the proteins. We give two use cases to demonstrate the interest of these functionalities and provide the corresponding scripts: the first concerns NMR conformers, the second focuses on mutations in a structure.

scholarly journals GeNePy3D: a quantitative geometry python toolbox for large scale bioimaging

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1374 ◽  
Author(s):  
Minh-Son Phan ◽  
Anatole Chessel
Keyword(s):  
Large Scale ◽  
Application Programming Interface ◽  
Scale Space ◽  
Experimental Conditions ◽  
Computational Tools ◽  
3D Geometry ◽  
Application Programming ◽  
Reusable Containers ◽  
Microscopy Techniques ◽  
Programming Interface

The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells’ positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the  GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility.

The COVID-19 Infodemic (Preprint)

2021 ◽  
Author(s):  
TEJAS DESAI ◽  
Arvind Conjeevaram
Keyword(s):  
Healthcare Workers ◽  
Scientific Information ◽  
Healthcare Providers ◽  
Application Programming Interface ◽  
Easy Access ◽  
Publication Date ◽  
Time Period ◽  
Application Programming ◽  
Impossible Task ◽  
Visual Basic Application

BACKGROUND In Situation Report #13 and 39 days before declaring COVID-19 a pandemic, the WHO declared a “COVID-19 infodemic”. The volume of coronavirus tweets was far too great for one to find accurate or reliable information. Healthcare workers were flooded with “noise” which drowned the “signal” of valuable COVID-19 information. To combat the infodemic, physicians created healthcare-specific micro-communities to share scientific information with other providers. OBJECTIVE Our objective was to eliminate noise and elevate signal tweets related to COVID-19 and provide easy access to the most educational tweets for medical professionals who were searching for information. METHODS We analyzed the content of eight physician-created communities and categorized each message in one of five domains. We coded 1) an application programming interface to download tweets and their metadata in JavaScript Object Notation and 2) a reading algorithm using visual basic application in Excel to categorize the content. We superimposed the publication date of each tweet into a timeline of key pandemic events. Finally, we created NephTwitterArchive.com to help healthcare workers find COVID-19-related signal tweets when treating patients. RESULTS We collected 21071 tweets from the eight hashtags studied. Only 9051 tweets were considered signal: tweets categorized into both a domain and subdomain. There was a trend towards fewer signal tweets as the pandemic progressed, with a daily median of 22% (IQR 0-42%). The most popular subdomain in Prevention was PPE (2448 signal tweets). In Therapeutics, Hydroxychloroquine/chloroquine wwo Azithromycin and Mechanical Ventilation were the most popular subdomains. During the active Infodemic phase (Days 0 to 49), a total of 2021 searches were completed in NephTwitterArchive.com, which was a 26% increase from the same time period before the pandemic was declared (Days -50 to -1). CONCLUSIONS The COVID-19 Infodemic indicates that future endeavors must be undertaken to eliminate noise and elevate signal in all aspects of scientific discourse on Twitter. In the absence of any algorithm-based strategy, healthcare providers will be left with the nearly impossible task of manually finding high-quality tweets from amongst a tidal wave of noise. CLINICALTRIAL not applicable

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