scholarly journals pyMCR: A Python Library for Multivariate Curve Resolution Analysis with Alternating Regression (MCR-AR)

2019 ◽  
Vol 124 ◽  
Author(s):  
Charles H. Camp
Keyword(s):  
Open Source ◽  
Relative Abundance ◽  
Open Source Software ◽  
A Priori ◽  
Multivariate Curve Resolution ◽  
Software Library ◽  
Chemometric Method ◽  
Curve Resolution ◽  
Error Functions ◽  
Resolution Analysis

pyMCR is a new open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating measurement signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of error functions for minimization, constraints, and regressors. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

A study of the photo-degradation kinetics of nifedipine by multivariate curve resolution analysis

2003 ◽  
Vol 31 (5) ◽  
pp. 1013-1019 ◽  
Author(s):  
Mojtaba Shamsipur ◽  
Bahram Hemmateenejad ◽  
Morteza Akhond ◽  
Katayoun Javidnia ◽  
Ramin Miri
Keyword(s):  
Degradation Kinetics ◽  
Multivariate Curve Resolution ◽  
Photo Degradation ◽  
Curve Resolution ◽  
Resolution Analysis ◽  
Kinetics Of

scholarly journals Development of framework for detecting smoking scene in video clips

2019 ◽  
Vol 13 (1) ◽  
pp. 22
Author(s):  
Poonam Ghuli ◽  
Shashank B N ◽  
Athri G Rao
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Open Source ◽  
Image Recognition ◽  
Open Source Software ◽  
Software Library ◽  
Accurate Model ◽  
Video Clips ◽  
Warning Messages ◽  
Warning Message

<p>According to Global Adult Tobacco Survey 2016-17, 61.9% of people quitting tobacco the reason was the warnings displayed on the product covers. The focus of this paper is to automatically display warning messages in video clips. This paper explains the development of a system to automatically detect the smoking scenes using image recognition approach in video clips and then add the warning message to the viewer.  The approach aims to detect the cigarette object using Tensorflow’s object detection API. Tensorflow is an open source software library for machine learning provided by Google which is broadly used in the field image recognition. At present, Faster R-CNN with Inception ResNet is theTensorflow’s slowest but most accurate model. Faster R-CNN with Inception Resnet v2 model is used to detect smoking scenes by training the model with cigarette as an object.</p><p><em><br /></em></p>

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