Matrix Gemini Updates Enhance Flexibility, Productivity
News Apr 28, 2016
Released at the end of Q2 2016, these improvements will be available to all current Matrix Gemini V5 users and, of course, to all new customers.
The latest version of software provides more flexibility for importing documents, pictures and files by allowing the file path to be defined beforehand to automate the process. This speeds up sample entry and other common tasks by reducing the number of clicks required to import common files.
Improvements to the bulk sample registration workflow enable the selection and/or manual creation of multiple batch codes in the same registration screen as well as enabling new substance test groups to be created during the registration process. Incorporated as a direct result of user feedback these enhancements increase flexibility when registering multiple samples and improve user efficiency in the Lab.
The entry of results where certain pre-defined options are needed can also be significantly speeded up thanks to further enhancements to Matrix Gemini’s ‘editable list box’ feature. It is now possible to display any contents of an editable list column as a combo box (pull-down list) instead of just text.
A number of useful display functions have also been added to Matrix Gemini with the ability to display dynamic images or button colours. This facility allows images on buttons or button colours to change based on result calculations or real time data changes on the screen. For example a button could be configured to display in green or red depending on an entered result in order to highlight a pass/fail result. Similarly, images on the buttons may be changed to reflect a particular characteristic of a sample such as solid or liquid, or perhaps the particular animal breed in a veterinary application.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.
Large-scale supercomputing is essential to solving complex questions about our world. But storage platforms essential for these advanced computer systems have been stuck in a rigid framework that required users to either choose between customization of features or high availability. Now, researchers have found a way to give high-performance computing data systems the flexibility to thrive with a framework called BespoKV, perhaps helping to one day achieve the HPC goal of performing at the exascale, or 1 billion billion calculations per second.READ MORE
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