Agilent Technologies Collaborates with Persistent Systems
News Sep 07, 2009
Agilent Technologies Inc. has expanded its commitment to open systems for chromatography data systems (CDS) by announcing a collaboration with Persistent Systems, and by joining the DriverCentral.net instrument driver portal.
Multivendor instrument connectivity is a core need in many laboratories because scientists often operate diverse instrumentation from multiple vendors in a single laboratory.
Control for multivendor instrumentation is a core functionality of Agilent’s EZChrom Elite CDS and OpenLAB enterprise platforms, with support for more than 20 vendors.
Led by Persistent, DriverCentral.net is the industry’s first independent device-driver portal for analytical instrument driver purchase. DriverCentral is seen as a breakthrough for purchasing and managing drivers for analytical instruments.
Agilent is collaborating with Persistent Systems in three main ways:
• Transferring many of its non-Agilent instrument drivers to Persistent Systems for further development and support;
• Providing documentation on its RC.NET technology to convert legacy drivers to the new standard and develop new drivers based on RC.NET; and
• Joining DriverCentral.net to make its own analytical instrument drivers available through the portal.
“Agilent supports open systems and open access to the RC.NET standard so that customers can choose instrumentation from other vendors, when necessary, while still maintaining their Agilent data system,” said Bruce von Herrmann, vice president and general manager of Agilent’s Software and Informatics Business Unit.
“We understand the need to maintain a familiar software user interface and workflow. Additionally, with this partnership we strengthen our commitment to open architecture and open systems, the foundation of our OpenLAB brand. This is the latest example of end-user benefits from Agilent’s 15-year relationship with Persistent.”
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.
Antarctic Worm and Machine Learning Help Identify Cerebral Palsy EarlierNews
A research team has released a study in the peer-reviewed journal BMC Bioinformatics showing that DNA methylation patterns in circulating blood cells can be used to help identify spastic cerebral palsy (CP) patients. The technique which makes use of machine learning, data science and even analysis of Antarctic worms, raises hopes for earlier targeted CP therapies.
Towards Personalized Medicine: One Type of Data is Not EnoughNews
To understand the biology of diseased organs researchers use different types of molecular data. One of the biggest computational challenges at the moment is integrating these multiple data types. A new computational method jointly analyses different types of molecular data and disentangles the sources of disease variability to guide personalized treatment.READ MORE