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EU funds research oncryosocieties & the immune system

Thursday, 19 April 2018 by System Administrator

Canstockphoto17670389

Public Release: 

EU funds research on 'Cryosocieties' and the immune system

Scientists at Goethe University Frankfurt are awarded 2 ERC Advanced Grants; € 2.5 million each for 5 years

Goethe University Frankfurt

FRANKFURT. Two Advanced Grants of the European Research Council (ERC) have been awarded to researchers at Goethe University Frankfurt. The sociologist Professor Thomas Lemke is researching the social impacts of cryobiology, i.e. the freezing and long-term preservation of organic material. The biochemist Professor Robert Tampé wants to unravel the winding pathways of the immune system inside the cell.

"Cryosocieties" project explores "suspended life"

Cryobiology has seen an enormous upturn over the last decades. More and more types of tissues and cellular material can be frozen, stored and thawed again without any detectable loss of vitality. Today, cryobiological practices are not only an important infrastructural prerequisite for many medical applications and a significant driver for innovations in the life sciences but also represent important options for personal family planning decisions as well as for the preservation of global biodiversity.

"In our 'Cryosocieties' project, I want to investigate the impact of cryopreservation on our understanding of life, starting with the hypothesis that cryobiological practices produce a specific form of life that I call 'suspended life'. They keep many vital processes in a suspended state between life and death, in which biological substances are neither completely alive nor completely dead," explains Professor Thomas Lemke from the Institute of Sociology. The aim of the project, which lies at the interface between biology, sociology and technology, is to study how cryopreservation practices alter temporal and spatial relationships and configurations as well as our understanding of life and death, health and illness, (in)fertility and sustainability.

Fighting obesity at the cellular level

Thursday, 19 April 2018 by System Administrator

Canstockphoto13170435

Characterizing 'keyhole' is first step to fighting obesity at cellular level

Team also launches development of potential small-molecule therapeutics

Date:

April 18, 2018

Source:

Vanderbilt University

Summary:

Scientists have characterized for the first time a complex, little-understood cellular receptor type that, when activated, shuts off hunger.


Jens Meiler's team determined the first crystal structure for a neuropeptide Y receptor, deciphering the thousands of carbon, oxygen, nitrogen and other atoms involved with it and how they bind to one another.

Credit: Brian Bender/Vanderbilt University

An international team has uncovered the potential to beat obesity at the cellular level, characterizing for the first time a complex, little-understood receptor type that, when activated, shuts off hunger.

Jens Meiler, professor of chemistry and pharmacology at Vanderbilt University, said pharmaceutical companies long have attempted to develop a small-molecule drug that could do just that. But until now, nobody knew exactly what the receptor looked like, making it nearly impossible to design the key to activating it.

Deep Learning is about to transform Biomedical Science

Thursday, 19 April 2018 by System Administrator

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How deep learning is about to transform biomedical science

“In silico labeling” aims to decode the terabytes of data per day generated in bio research labs

April 18, 2018

Human induced pluripotent stem cell neurons imaged in phase contrast (gray pixels, left) — currently processed manually with fluorescent labels (color pixels) to make them visible. That’s about to radically change. (credit: Gladstone Institutes)

Researchers at Google, Harvard University, and Gladstone Institutes have developed and tested new deep-learning algorithms that can identify details in terabytes of bioimages, replacing slow, less-accurate manual labeling methods.

Deep learning is a type of machine learning that can analyze data, recognize patterns, and make predictions. A new deep-learning approach to biological images, which the researchers call “in silico labeling” (ISL), can automatically find and predict features in images of “unlabeled” cells (cells that have not been manually identified by using fluorescent chemicals).

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