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Funding Update: NSF Grants Awarded to Arizona State; University of Illinois


Recent NSF Awards in Proteomics and Protein Research

Title: Plasmonic-Based Electrochemical Impedance Microscopy for Studying Molecular Binding and Cellular Processes
Principal Investigators: Nongjian Tao, Shaopeng Wang
Sponsor: Arizona State University
Start/End Date: Aug. 1, 2012 – July 31, 2015
Amount Awarded to Date: $651,205

Funds development of a plasmonic-based electrochemical impedance microscope that can monitor the binding of small molecules and proteins in microarray format. The researchers aim to integrate P-EIM with total internal reflection fluorescence and surface plasmon resonance microscopy for use in fields including proteomics.

Title: Evolutionary Genomics Collaborative: Origins, Evolution and Structure of Viral and Cellular Proteomes
Principal Investigator: Gustavo Caetano-Anolles
Sponsor: University of Illinois at Urbana-Champaign
Start/End Date: July 1, 2012 – June 30, 2014
Amount Awarded to Date: $49,721

Funds a project to characterize the origin, evolution, and structure of viral proteomes and compare them to proteomes of the cellular world in order to better integrate an understanding of viral structural and functional evolution into knowledge of cellular evolution.

The Scan

Study Links Genetic Risk for ADHD With Alzheimer's Disease

Higher polygenic risk scores for attention-deficit/hyperactivity disorder were also linked to cognitive decline and Alzheimer's disease risk, according to a new study in Molecular Psychiatry.

Study Offers Insights Into Role of Structural Variants in Cancer

A new study in Nature using cell lines shows that structural variants can enable oncogene activation.

Computer Model Uses Genetics, Health Data to Predict Mental Disorders

A new model in JAMA Psychiatry finds combining genetic and health record data can predict a mental disorder diagnosis before one is made clinically.

Study Tracks Off-Target Gene Edits Linked to Epigenetic Features

Using machine learning, researchers characterize in BMC Genomics the potential off-target effects of 19 computed or experimentally determined epigenetic features during CRISPR-Cas9 editing.