scholarly journals Open-source microscopic solution for classification of biological samples

2021 ◽  
Author(s):  
Robert Archibald ◽  
Graham M. Gibson ◽  
Samuel T. Westlake ◽  
Akhil Kallepalli
Keyword(s):  
Open Source ◽  
Biological Samples

scholarly journals PoreAnalyzer—An Open-Source Framework for the Analysis and Classification of Defects in Additive Manufacturing

2021 ◽  
Vol 11 (13) ◽  
pp. 6086
Author(s):  
Nils Ellendt ◽  
Fabian Fabricius ◽  
Anastasiya Toenjes
Keyword(s):  
Additive Manufacturing ◽  
Open Source ◽  
Total Porosity ◽  
Shape Accuracy ◽  
Process Maps ◽  
Defect Type ◽  
Open Source Framework ◽  
New Material ◽  
High Cooling Rates

Additive manufacturing processes offer high geometric flexibility and allow the use of new alloy concepts due to high cooling rates. For each new material, parameter studies have to be performed to find process parameters that minimize microstructural defects such as pores or cracks. In this paper, we present a system developed in Python for accelerated image analysis of optical microscopy images. Batch processing can be used to quickly analyze large image sets with respect to pore size distribution, defect type, contribution of defect type to total porosity, and shape accuracy of printed samples. The open-source software is independent of the microscope used and is freely available for use. This framework allows us to perform such an analysis on a circular area with a diameter of 5 mm within 10 s, allowing detailed process maps to be obtained for new materials within minutes after preparation.

scholarly journals Availability of MudPIT data for classification of biological samples

2013 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Dario Silvestre ◽  
Italo Zoppis ◽  
Francesca Brambilla ◽  
Valeria Bellettato ◽  
Giancarlo Mauri ◽  
...  
Keyword(s):  
Biological Samples

scholarly journals PredicTF: a tool to predict bacterial transcription factors in complex microbial communities

2021 ◽  
Author(s):  
Lummy Maria Oliveira Monteiro ◽  
Joao Saraiva ◽  
Rodolfo Brizola Toscan ◽  
Peter F Stadler ◽  
Rafael Silva-Rocha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Microbial Communities ◽  
Open Source ◽  
Genetic Information ◽  
Environmental Isolate ◽  
Ammonium Oxidation ◽  
Cellular Gene ◽  
Bacterial Transcription

AbstractTranscription Factors (TFs) are proteins that control the flow of genetic information by regulating cellular gene expression. Here we describe PredicTF, a first platform supporting the prediction and classification of novel bacterial TF in complex microbial communities. We evaluated PredicTF using a two-step approach. First, we tested PredictTF’s ability to predict TFs for the genome of an environmental isolate. In the second evaluation step, PredicTF was used to predict TFs in a metagenome and 11 metatranscriptomes recovered from a community performing anaerobic ammonium oxidation (anammox) in a bioreactor. PredicTF is open source pipeline available at https://github.com/mdsufz/PredicTF.

Automated classification of self-grooming in mice using open-source software

2017 ◽  
Vol 289 ◽  
pp. 48-56 ◽  
Author(s):  
Bastijn J.G. van den Boom ◽  
Pavlina Pavlidi ◽  
Casper J.H. Wolf ◽  
Adriana H. Mooij ◽  
Ingo Willuhn
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Automated Classification

scholarly journals <i>dh2loop</i> 1.0: an open-source Python library for automated processing and classification of geological logs

2021 ◽  
Vol 14 (11) ◽  
pp. 6711-6740
Author(s):  
Ranee Joshi ◽  
Kavitha Madaiah ◽  
Mark Jessell ◽  
Mark Lindsay ◽  
Guillaume Pirot
Keyword(s):  
Open Source ◽  
Mineral Exploration ◽  
String Matching ◽  
Drill Hole ◽  
Geological Survey ◽  
Database Structure ◽  
Automated Processing ◽  
Study Case ◽  
Textual Form

Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021​​​​​​​) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).

Open-source deep learning models for acoustic detection and classification of orcas

2021 ◽  
Vol 150 (4) ◽  
pp. A286-A286
Author(s):  
Sadman Sakib ◽  
Steven Bergner ◽  
Dave Campbell ◽  
Mike Dowd ◽  
Fabio Frazao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Acoustic Detection ◽  
Learning Models

How the FLOSS Research Community Uses Email Archives

2012 ◽  
Vol 4 (1) ◽  
pp. 37-59 ◽  
Author(s):  
Megan Squire
Keyword(s):  
Software Engineering ◽  
Open Source ◽  
Open Source Software ◽  
Development Process ◽  
Relevant Literature ◽  
Research Community ◽  
Email Message ◽  
Object Of Study ◽  
Broad Overview

Artifacts of the software development process, such as source code or emails between developers, are a frequent object of study in empirical software engineering literature. One of the hallmarks of free, libre, and open source software (FLOSS) projects is that the artifacts of the development process are publicly-accessible and therefore easily collected and studied. Thus, there is a long history in the FLOSS research community of using these artifacts to gain understanding about the phenomenon of open source software, which could then be compared to studies of software engineering more generally. This paper looks specifically at how the FLOSS research community has used email artifacts from free and open source projects. It provides a classification of the relevant literature using a publicly-available online repository of papers about FLOSS development using email. The outcome of this paper is to provide a broad overview for the software engineering and FLOSS research communities of how other researchers have used FLOSS email message artifacts in their work.

A Study on Generating Fake Images of Podocyte Cells Based on Acgans with Restriction of Learning

2020 ◽  
Vol 38 ◽  
pp. 76-82
Author(s):  
Yusuke Ono ◽  
Tsutomu Matsuura ◽  
Toshiyuki Matsuzaki ◽  
Keiju Hiromura ◽  
Takeo Aoki
Keyword(s):  
Machine Learning ◽  
Epithelial Cells ◽  
Conventional Method ◽  
Biological Samples ◽  
Previous Method ◽  
Glomerular Epithelial Cells ◽  
Passage Number

In general, we need a lot of data for improving the accuracy of machine learning. However, the number of biological samples what we can obtain are not enough for machine learning. This problem exists in the classification of glomerular epithelial cells with the progress of disease, and its accuracy is contrary to our intuitive impression. Therefore, we would like to improve the accuracy by generating a lot of fake images using Generative Adversarial Nets (GANs). About podocyte cells, it was difficult to obtain an arbitrary disease by previous method. In this paper, we propose the model with restriction of learning by shapes information based on ACGANs, and we investigate how much fake images generated by our method are similar to real images. According to the results, the passage number of fake images by our method is 17% higher than conventional method.

Sign in / Sign up

Export Citation Format

Share Document