Highly Successful Matrix LIMS User Group Meeting
News Oct 31, 2016
Presentations from users and Autoscribe technical specialists were interwoven with live link-ups to Autoscribe experts at a number of locations in the US to provide a varied perspective on current and future uses of Matrix.
The key theme that ran through the meeting was the flexibility provided by the Matrix Gemini configuration tools to allow users to organise their LIMS to their exact needs. There were some enthusiastic presentations from users in vastly different industries who were carrying out all of their own configuration.
Autoscribe specialists contributed a number of useful hints and tips on the use of configuration tools to build checklists, forms or task lists and the use of bulk sample registration. Another session focused on ways to reduce the number of steps required for users to interact with Matrix in order to decrease the number of decisions needed and minimise the chance of making mistakes.
Autoscribe Informatics President, John Boother, commented: “It was a very enjoyable day and very rewarding to see many of our customers so actively embracing the use of our configuration tools. All of the systems we saw today looked very different and yet all use exactly the same underlying code that forms Matrix. Because each individual system has the same code, Autoscribe can support all of our customers’ own configurations provided they have an annual support contract with us – they don’t have to rely on us to configure their system for them.”
“It was also significant that our very first customer for the Autoscribe Matrix V2 system was there to make a presentation,” he continued. “Having purchased Matrix twenty years ago, and having had a regular maintenance contract, they have benefitted by automatically receiving each new iteration of Matrix as it has evolved over the years without ever having to purchase a new system, and are now running the latest version, Matrix Gemini V5. That level of commitment from a customer is testament both to the continuing quality of the product and the importance we attach to customer satisfaction.”
Scientists have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish. Using 3D images of fluorescently labeled cells, the research team taught computers to find structures inside living cells without fluorescent labels, using only black and white images generated by an inexpensive technique known as brightfield microscopy.READ MORE