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The new AggTag method allows researchers to see the previously undetectable but potentially disease-causing intermediate forms of proteins as they misfold. The method uses fluorescence to simultaneously detect two different proteins (red, green) within the cell (blue). Credit: Zhang lab, Penn State

New method uses fluorescence to identify disease-causing forms of proteins

A new method uses fluorescence to detect potentially disease-causing forms of proteins as they unravel due to stress or mutations. A team of researchers from Penn State and the University of Washington reengineered a fluorescent compound and developed a method to simultaneously light up two different proteins as they misfold and aggregate inside a living cell, highlighting forms that likely play a role in several neurodegenerative diseases including Alzheimer’s and Parkinson’s.

Troy Ott

Troy Ott to discuss "the improbable series of events that led to your birth"

At this month’s "Science on Tap" event, Huck Associate Director and professor of reproductive biology Troy Ott will discuss viviparity — the development of an embryo inside the body leading to the birth of a live offspring. Viviparity is thought to have evolved from egg-laying animals. Ott's talk will focus on one of the enigmas of live birth that relates to the mother’s immune system.

A new program developed by researchers from Penn State and Microsoft Azure automatically detects regions of interest within images, alleviating a serious bottleneck in processing photos for wildlife research. The new program successfully identified the region of interest—unique marks on giraffe torsos—in giraffe photos, even when the giraffe occupied a small region of the photo or when they were partially covered by vegetation. Credit: Penn State/Wild Nature Institute

Toward automated animal identification in wildlife research

A new automated method to prepare digital photos for analysis will help wildlife researchers who depend on photographs to identify individual animals by their unique markings. A wildlife biologist from Penn State teamed up with scientists from Microsoft Azure, a cloud computing service, using machine learning technology to improve how photographs are turned into usable data for wildlife research. A paper describing the new technique appears online in the journal Ecological Informatics.