Two New LIMS Case Studies Published
News Nov 20, 2014
Two new case studies highlighting the versatility of the Matrix Laboratory Information Management System (LIMS) at Castrol’s Lubricant Technology Centre and Cavendish Nuclear’s environmental laboratory are now available for download from the website of leading LIMS developer, Autoscribe Ltd.
'Matrix Provides Paperless LIMS At Castrol’ (www.autoscribeinformatics.com/phocadownload/userupload/ccsv1%20-%20castrol%20case%20study.pdf) describes how product and development scientists at the Lubricant Technology Centre at Castrol, Pangbourne, Berkshire, UK, can request analytical tests and receive results by accessing Matrix from their own computers. Castrol, part of BP, has been a Matrix LIMS customer for more than 20 years.
The analytical laboratory at Pangbourne provides a comprehensive test facility for the lubricant development and product teams and is capable of investigating all aspects of the technical life cycle, from research and development in fuels and lubricants, to support and quality assurance, fluid analysis and performance testing.
Cavendish Nuclear’s environmental laboratory is an internationally recognized provider of specialist radiometric analysis for a wide range of environmental and personnel matrices, requiring low limit of detection techniques. 'UKAS Accredited Laboratory Manages Its Own Configurations Of Matrix Gemini LIMS’ (www.autoscribeinformatics.com/phocadownload/userupload/cncsv1%20-%20cavendish%20nuclear%20case%20study.pdf) shows how by working closely with Autoscribe on the upgrade of Matrix 4 to Matrix Gemini, staff at Cavendish Nuclear’s environmental laboratory have become so proficient in using Gemini’s one-time configuration tools that they manage and implement any updates and developments to their own system as their needs arise.
Autoscribe’s Managing Director, John Boother, commented: “We are delighted that so many of our customers are working with us to show how they are benefitting from using Matrix. Seeing the product in action in many different industries is a really powerful testament to the versatility that Matrix has to offer, and gives our prospective customers real confidence that it will be able to meet their needs.
An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
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