scholarly journals Novel, User-friendly Experimental and Analysis Strategies for Fast Voltammetry: 1. The Analysis Kid for FSCV

2021 ◽  
Author(s):  
Sergio Mena ◽  
Solene Dietsch ◽  
Shane N. Berger ◽  
Colby E. Witt ◽  
Parastoo Hashemi
Keyword(s):  
Web Application ◽  
Area Under The Curve ◽  
Maximum Amplitude ◽  
Fast Scan Cyclic Voltammetry ◽  
Peak Finding ◽  
Runtime Environment ◽  
File Formats ◽  
Low Concentrations ◽  
Carbon Fiber Microelectrodes ◽  
User Friendly

Fast-scan cyclic voltammetry at carbon fiber microelectrodes measures low concentrations of analytes in biological systems. There are ongoing efforts to simplify FSCV analysis and several custom platforms are available for filtering and multi-modal analysis of FSCV signals but there is no single, easily accessible platform that has capacity for all these features. Here we present The Analysis Kid: a free, open-source cloud application that does not require a specialized runtime environment and is easily accessible via common browsers. We show that a user-friendly interface can analyze multi-platform file formats to provide multimodal visualization of FSCV color plots with digital background subtraction. We highlight key features that allow interactive calibration and parametric analysis via peak finding algorithms to automatically detect the maximum amplitude, area under the curve and clearance rate of the signal. Finally, The Analysis Kid enables semi-automatic fitting of data with Michaelis Menten kinetics with single or dual reuptake models. The Analysis Kid can be freely accessed at https://analysis-kid.herokuapp.com/. The web application code is found, under an MIT license, at https://github.com/sermeor/The-Analysis-Kid.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

2021 ◽  
pp. 193229682098557
Author(s):  
Alysha M. De Livera ◽  
Jonathan E. Shaw ◽  
Neale Cohen ◽  
Anne Reutens ◽  
Agus Salim
Keyword(s):  
Continuous Glucose Monitoring ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Epidemiological Studies ◽  
Interactive Software ◽  
Web Based ◽  
Software Applications ◽  
Visualization Of Data ◽  
User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

scholarly journals COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ye Emma Zohner ◽  
Jeffrey S. Morris
Keyword(s):  
Web Application ◽  
The United States ◽  
Mitigation Strategies ◽  
Time Lags ◽  
Policy Makers ◽  
Major Health ◽  
Dynamic Representation ◽  
Graphical Displays ◽  
The Usa ◽  
User Friendly

Abstract Background The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions. Findings COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe. Conclusion The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.

scholarly journals Modified Sawhorse Waveform for the Voltammetric Detection of Oxytocin

2022 ◽  
Author(s):  
Favian Liu ◽  
Negar Ghasem Ardabili ◽  
Izaiah Brown ◽  
Harmain Rafi ◽  
Clarice Cook ◽  
...  
Keyword(s):  
Brain Slices ◽  
Physiological Role ◽  
Peptide Hormone ◽  
Protective Effects ◽  
Fast Scan Cyclic Voltammetry ◽  
The Past ◽  
Voltammetric Detection ◽  
Carbon Fiber Microelectrodes

Abstract Carbon fiber microelectrodes (CFMEs) have been used to detect neurotransmitters and other biomolecules using fast-scan cyclic voltammetry (FSCV) for the past few decades. This technique measures neurotransmitters such as dopamine and, more recently, physiologically relevant neuropeptides. Oxytocin, a pleiotropic peptide hormone, is physiologically important for adaptation, development, reproduction, and social behavior. This neuropeptide functions as a stress-coping molecule, an anti-inflammatory agent, and serves as an antioxidant with protective effects especially during adversity or trauma. Here, we measure tyrosine using the Modified Sawhorse Waveform (MSW), enabling enhanced electrode sensitivity for the amino acid and oxytocin peptide. Applying the MSW, decreased surface fouling and enabled codetection with other monoamines. As oxytocin contains tyrosine, the MSW was also used to detect oxytocin. The sensitivity of oxytocin detection was found to be 3.99 ± 0.49 nA/µM, (n=5). Additionally, we demonstrate that applying the MSW on CFMEs allows for real time measurements of exogenously applied oxytocin on rat brain slices. These studies may serve as novel assays for oxytocin detection in a fast, sub-second timescale with possible implications for in vivo measurements and further understanding of the physiological role of oxytocin.

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2020 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Microbiome Research ◽  
Interactive Visualizations ◽  
User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

scholarly journals Remote Gait Type Classification System Using Markerless 2D Video

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1824
Author(s):  
Pedro Albuquerque ◽  
João Pedro Machado ◽  
Tanmay Tulsidas Verlekar ◽  
Paulo Lobato Correia ◽  
Luís Ducla Soares
Keyword(s):  
Gait Analysis ◽  
Learning Strategies ◽  
Web Application ◽  
State Of The Art ◽  
The State ◽  
Remote Healthcare ◽  
Visual Artifacts ◽  
Analysis System ◽  
Private Network ◽  
User Friendly

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.

scholarly journals Prediction of Caption and Emoji of an Image using Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3721-3724
Keyword(s):  
Deep Learning ◽  
Social Networking ◽  
Web Application ◽  
Automatic Generation ◽  
Application Framework ◽  
Challenging Problem ◽  
Artificial Intelligence Research ◽  
Learning Techniques ◽  
User Friendly ◽  
Visually Impaired Persons

With the invention of deep learning, there is a good progress in image classification. But automatic generation of captions for images is still a challenging problem and is in the initial stages of artificial intelligence research. Automatic description of images has applications in social networking and will be useful to visually impaired persons. This paper concentrates on designing a user-friendly web application framework which can predict the caption of an image using deep learning techniques. The verbs and objects present in the caption are used for forming the emoji and for predicting the major color of the image

Amine-Functionalized Carbon-Fiber Microelectrodes for Enhanced ATP Detection with Fast-Scan Cyclic Voltammetry

2021 ◽  
Author(s):  
Yuxin Li ◽  
Moriah E. Weese ◽  
Michael T. Cryan ◽  
Ashley E. Ross
Keyword(s):  
Cyclic Voltammetry ◽  
Carbon Fiber ◽  
Adenosine Triphosphate ◽  
Fiber Surface ◽  
Fast Scan Cyclic Voltammetry ◽  
Extracellular Signaling ◽  
Amine Functionalized ◽  
Carbon Fiber Surface ◽  
Carbon Fiber Microelectrodes ◽  
Atp Detection

Here, we provide evidence that functionalizing the carbon-fiber surface with amines significantly improves direct electrochemical adenosine triphosphate (ATP) detection with fast-scan cyclic voltammetry (FSCV). ATP is an important extracellular signaling...

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