scholarly journals An open source device for operant licking in rats

2016 ◽  
Author(s):  
Matthew Longley ◽  
Ethan L Willis ◽  
Cindy X Tay ◽  
Hao Chen
Keyword(s):  
Open Source ◽  
Radio Frequency Identification ◽  
Credit Card ◽  
Fixed Ratio ◽  
Progressive Ratio ◽  
Complex Data ◽  
Motion Sensors ◽  
3D Design ◽  
Behavioral Studies ◽  
Complex Data Sets

Increasingly complex data sets are needed to fully understand the complexity in behavior. Credit card sized single-board computers with multi-core CPUs are an attractive platform for designing devices capable of collecting multi-dimensional behavioral data. To demonstrate this idea, we created an easy to-use device for operant licking experiments and another device that records environmental variables. These systems collect data obtained from multiple input devices (e.g., radio frequency identification tag readers, touch and motion sensors, environmental sensors) and activate output devices (e.g., LED lights, syringe pumps) as needed. Data gathered from these devices can be automatically transferred to a remote server via a wireless network. We tested the operant device by training rats to obtain either sucrose or water under the control of a fixed ratio, a variable ratio, or a progressive ratio reinforcement schedule. The lick data demonstrated that the device has sufficient precision and time resolution to record the fast licking behavior of rats. Data from the environment monitoring device also showed reliable measurements. By providing the code and 3D design under an open source license, we believe these examples will stimulate innovation in behavioral studies. http://github.com/chen42/openbehavior.

scholarly journals An open source device for operant licking in rats

2016 ◽  
Author(s):  
Matthew Longley ◽  
Ethan L Willis ◽  
Cindy X Tay ◽  
Hao Chen
Keyword(s):  
Open Source ◽  
Radio Frequency Identification ◽  
Credit Card ◽  
Fixed Ratio ◽  
Progressive Ratio ◽  
Complex Data ◽  
Motion Sensors ◽  
3D Design ◽  
Behavioral Studies ◽  
Complex Data Sets

Increasingly complex data sets are needed to fully understand the complexity in behavior. Credit card sized single-board computers with multi-core CPUs are an attractive platform for designing devices capable of collecting multi-dimensional behavioral data. To demonstrate this idea, we created an easy to-use device for operant licking experiments and another device that records environmental variables. These systems collect data obtained from multiple input devices (e.g., radio frequency identification tag readers, touch and motion sensors, environmental sensors) and activate output devices (e.g., LED lights, syringe pumps) as needed. Data gathered from these devices can be automatically transferred to a remote server via a wireless network. We tested the operant device by training rats to obtain either sucrose or water under the control of a fixed ratio, a variable ratio, or a progressive ratio reinforcement schedule. The lick data demonstrated that the device has sufficient precision and time resolution to record the fast licking behavior of rats. Data from the environment monitoring device also showed reliable measurements. By providing the code and 3D design under an open source license, we believe these examples will stimulate innovation in behavioral studies. http://github.com/chen42/openbehavior.

scholarly journals An open source device for operant licking in rats

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2981 ◽  
Author(s):  
Matthew Longley ◽  
Ethan L. Willis ◽  
Cindy X. Tay ◽  
Hao Chen
Keyword(s):  
Open Source ◽  
Radio Frequency Identification ◽  
Source Code ◽  
Fixed Ratio ◽  
Progressive Ratio ◽  
Raspberry Pi ◽  
Motion Sensors ◽  
Environmental Sensors ◽  
3D Design ◽  
Behavioral Studies

We created an easy-to-use device for operant licking experiments and another device that records environmental variables. Both devices use the Raspberry Pi computer to obtain data from multiple input devices (e.g., radio frequency identification tag readers, touch and motion sensors, environmental sensors) and activate output devices (e.g., LED lights, syringe pumps) as needed. Data gathered from these devices are stored locally on the computer but can be automatically transferred to a remote server via a wireless network. We tested the operant device by training rats to obtain either sucrose or water under the control of a fixed ratio, a variable ratio, or a progressive ratio reinforcement schedule. The lick data demonstrated that the device has sufficient precision and time resolution to record the fast licking behavior of rats. Data from the environment monitoring device also showed reliable measurements. By providing the source code and 3D design under an open source license, we believe these examples will stimulate innovation in behavioral studies. The source code can be found at http://github.com/chen42/openbehavior.

Facies classification using machine learning

2016 ◽  
Vol 35 (10) ◽  
pp. 906-909 ◽  
Author(s):  
Brendon Hall
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Data Sets ◽  
Complex Data ◽  
Facies Classification ◽  
Large Complex ◽  
Complex Data Sets ◽  
Software Platforms ◽  
Tools And Techniques

There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist's toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

scholarly journals Interactive web-based visualization of phylogenetic trees using Phylogeny.IO

2016 ◽  
Author(s):  
Nikola Jovanovic ◽  
Alexander S Mikheyev
Keyword(s):  
Open Source ◽  
Phylogenetic Trees ◽  
Data Driven ◽  
Data Sets ◽  
Complex Data ◽  
Web Page ◽  
Web Based ◽  
Interactive Display ◽  
Complex Data Sets

Traditional static publication formats make visualization, exploration and sharing of massive phylogenetic trees difficult. Web-based technologies, such as the Data Driven Document (D3) JavaScript library, exist to overcome such challenges by allowing interactive display of complex data sets. We here we an open-source web-based application that applies the power of D3 to the visualization of phylogenetic trees. Phylogeny.IO (http://phyloeny.io) displays trees together with a range of static (e.g., such as shapes and colors) and dynamic (e.g., pop-up text and images) annotations. Annotated trees can be shared as IFrame HTML objects easily embeddable in any web page.

scholarly journals Early Detection of Apathetic Phenotypes in Huntington’s Disease Knock-in Mice Using Open Source Tools

2017 ◽  
Author(s):  
Shawn Minnig ◽  
Robert M. Bragg ◽  
Hardeep S. Tiwana ◽  
Wes T. Solem ◽  
William S. Hovander ◽  
...  
Keyword(s):  
Early Detection ◽  
Huntington's Disease ◽  
Open Source ◽  
Huntington’S Disease ◽  
Fixed Ratio ◽  
Noisy Data ◽  
Progressive Ratio ◽  
Phenotypic Rescue ◽  
Preclinical Work ◽  
Normal Performance

AbstractApathy is one of the most prevalent and progressive psychiatric symptom in Huntington’s disease (HD) patients. However, preclinical work in HD mouse models tend to focus on molecular and motor, rather than affective, phenotypes. Measuring behavior in mice often produces noisy data and requires large cohorts to detect phenotypic rescue with appropriate power. The operant equipment necessary for measuring affective phenotypes is typically expensive, proprietary to commercial entities, and bulky which can render adequately sized mouse cohorts as cost-prohibitive. Thus, we describe here a home-built open-source alternative to commercial hardware that is reliable, scalable, and reproducible. Using off-the-shelf hardware, we adapted and built several of the rodent operant buckets (ROBucket) designed to test HttQ111/+ mice for attention deficits in fixed ratio (FR) and progressive ratio (PR) tasks. We find that, despite normal performance in reward attainment in the FR task, HttQ111/+ mice exhibit reduced PR performance at 9-11 months of age, suggesting motivational deficits. We replicated this in two independent cohorts, which demonstrates the reliability and utility of both the apathetic phenotype, and these ROBuckets, for preclinical HD studies.

scholarly journals Interactive web-based visualization of phylogenetic trees using Phylogeny.IO

2016 ◽  
Author(s):  
Nikola Jovanovic ◽  
Alexander S Mikheyev
Keyword(s):  
Open Source ◽  
Phylogenetic Trees ◽  
Data Driven ◽  
Data Sets ◽  
Complex Data ◽  
Web Page ◽  
Web Based ◽  
Interactive Display ◽  
Complex Data Sets

Traditional static publication formats make visualization, exploration and sharing of massive phylogenetic trees difficult. Web-based technologies, such as the Data Driven Document (D3) JavaScript library, exist to overcome such challenges by allowing interactive display of complex data sets. We here we an open-source web-based application that applies the power of D3 to the visualization of phylogenetic trees. Phylogeny.IO (http://phyloeny.io) displays trees together with a range of static (e.g., such as shapes and colors) and dynamic (e.g., pop-up text and images) annotations. Annotated trees can be shared as IFrame HTML objects easily embeddable in any web page.

scholarly journals Open-Source’s Inspirations for Computational Social Science: Lessons from a Failed Analysis

2020 ◽  
Vol 8 (3) ◽  
pp. 231-238 ◽  
Author(s):  
Nathaniel Poor
Keyword(s):  
Social Science ◽  
Open Source ◽  
Computational Social Science ◽  
Digital Data ◽  
Institutional Review Boards ◽  
Data Sets ◽  
Complex Data ◽  
Institutional Review ◽  
Complex Data Sets ◽  
Complex Statistical Analysis

The questions we can ask currently, building on decades of research, call for advanced methods and understanding. We now have large, complex data sets that require more than complex statistical analysis to yield human answers. Yet as some researchers have pointed out, we also have challenges, especially in computational social science. In a recent project I faced several such challenges and eventually realized that the relevant issues were familiar to users of free and open-source software. I needed a team with diverse skills and knowledge to tackle methods, theories, and topics. We needed to iterate over the entire project: from the initial theories to the data to the methods to the results. We had to understand how to work when some data was freely available but other data that might benefit the research was not. More broadly, computational social scientists may need creative solutions to slippery problems, such as restrictions imposed by terms of service for sites from which we wish to gather data. Are these terms legal, are they enforced, or do our institutional review boards care? Lastly—perhaps most importantly and dauntingly—we may need to challenge laws relating to digital data and access, although so far this conflict has been rare. Can we succeed as open-source advocates have?

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

scholarly journals Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

2021 ◽  
Vol 22 (5) ◽  
pp. 2659
Author(s):  
Gianluca Costamagna ◽  
Giacomo Pietro Comi ◽  
Stefania Corti
Keyword(s):  
Drug Discovery ◽  
Drug Screening ◽  
Neural Development ◽  
Neurological Disorders ◽  
Large Scale ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Level ◽  
Complex Data ◽  
Complex Data Sets

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

scholarly journals Ethanol-Related Behaviors in Mouse Lines Selectively Bred for Drinking to Intoxication

2021 ◽  
Vol 11 (2) ◽  
pp. 189
Author(s):  
Bryan E. Jensen ◽  
Kayla G. Townsley ◽  
Kolter B. Grigsby ◽  
Pamela Metten ◽  
Meher Chand ◽  
...  
Keyword(s):  
Drinking Behavior ◽  
Effective Means ◽  
Fixed Ratio ◽  
Progressive Ratio ◽  
Blood Alcohol ◽  
Drinking Patterns ◽  
Self Administration ◽  
Blood Alcohol Levels ◽  
Drinking In The Dark ◽  
Mouse Lines

Alcohol use disorder (AUD) is a devastating psychiatric disorder that has significant wide-reaching effects on individuals and society. Selectively bred mouse lines are an effective means of exploring the genetic and neuronal mechanisms underlying AUD and such studies are translationally important for identifying treatment options. Here, we report on behavioral characterization of two replicate lines of mice that drink to intoxication, the High Drinking in the Dark (HDID)-1 and -2 mice, which have been selectively bred (20+ generations) for the primary phenotype of reaching high blood alcohol levels (BALs) during the drinking in the dark (DID) task, a binge-like drinking assay. Along with their genetically heterogenous progenitor line, Hs/Npt, we tested these mice on: DID and drinking in the light (DIL); temporal drinking patterns; ethanol sensitivity, through loss of righting reflex (LORR); and operant self-administration, including fixed ratio (FR1), fixed ratio 3:1 (FR3), extinction/reinstatement, and progressive ratio (PR). All mice consumed more ethanol during the dark than the light and both HDID lines consumed more ethanol than Hs/Npt during DIL and DID. In the dark, we found that the HDID lines achieved high blood alcohol levels early into a drinking session, suggesting that they exhibit front loading like drinking behavior in the absence of the chronicity usually required for such behavior. Surprisingly, HDID-1 (female and male) and HDID-2 (male) mice were more sensitive to the intoxicating effects of ethanol during the dark (as determined by LORR), while Hs/Npt (female and male) and HDID-2 (female) mice appeared less sensitive. We observed lower HDID-1 ethanol intake compared to either HDID-2 or Hs/Npt during operant ethanol self-administration. There were no genotype differences for either progressive ratio responding, or cue-induced ethanol reinstatement, though the latter is complicated by a lack of extinguished responding behavior. Taken together, these findings suggest that genes affecting one AUD-related behavior do not necessarily affect other AUD-related behaviors. Moreover, these findings highlight that alcohol-related behaviors can also differ between lines selectively bred for the same phenotype, and even between sexes within those same line.

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