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Researchers find potential way to combat MERS virus

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New York: Researchers, including one of Indian-origin, have found molecules that shut down the activity of an enzyme essential to the Middle East Respiratory Syndrome (MERS) virus replication.

The virus is in the international spotlight again as South Korea faces the largest MERS outbreak outside the Middle East.

More than 2,800 people have been quarantined during the outbreak. The World Health Organisation (WHO) reported 27 deaths and 172 confirmed cases in its most recent update on June 22.

Professors Arun Ghosh and Andrew Mesecar from Purdue University in the US have been studying the virus and creating and testing molecular compounds that could lead to potential treatments since shortly after MERS was discovered.

“The team identified molecules that inhibit an enzyme essential to MERS virus replication and discovered a characteristic of the enzyme that is very different from other coronaviruses, the family of viruses to which MERS-CoV belongs,” Mesecar said. He added, “This enzyme is a prime target – an Achilles’ heel of the virus.”

The team was targeting an enzyme within the MERS virus called 3C-like protease, without which the virus cannot create more viruses to further an infection.

“We captured the protease’s atomic structure through this work, which provides the map to design potent new drugs to fight MERS,” said Mesecar.

The MERS virus emerged in 2012 and was mostly confined to the Middle East until 2014 when cases were identified in the US, Britain, France and Italy. Till date, 25 countries have reported cases, according to the WHO.

“It is a threat to public health and there currently is no treatment or vaccine. We continue to study the virus to improve our understanding of how it works and ways to prevent its spread.”

The study was published in the Journal of Biological Chemistry.

(IANS)

 

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Researchers Teaching Artificial Intelligence to Connect Senses Like Vision and Touch

The new AI-based system can create realistic tactile signals from visual inputs

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Artificial intelligence, road infrastructure
The fully-automated system is based on AI-powered object detection to identify street signs in the freely available images. Pixabay

A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI) that can learn to see by touching and to feel by seeing.

While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals.

Robots, however, that have been programmed to see or feel can’t use these signals quite as interchangeably.

The new AI-based system can create realistic tactile signals from visual inputs, and predict which object and what part is being touched directly from those tactile inputs.

Teaching, Artificial Intelligence, Researchers
) A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI). Pixabay

In the future, this could help with a more harmonious relationship between vision and robotics, especially for object recognition, grasping, better scene understanding and helping with seamless human-robot integration in an assistive or manufacturing setting.

“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge”, said Yunzhu Li, PhD student and lead author from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

“By blindly touching around, our model can predict the interaction with the environment purely from tactile feelings,” Li added.

The team used a KUKA robot arm with a special tactile sensor called GelSight, designed by another group at MIT.

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Using a simple web camera, the team recorded nearly 200 objects, such as tools, household products, fabrics, and more, being touched more than 12,000 times.

Breaking those 12,000 video clips down into static frames, the team compiled “VisGel,” a dataset of more than three million visual/tactile-paired images.

“Bringing these two senses (vision and touch) together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects,” said Li.

The current dataset only has examples of interactions in a controlled environment.

Teaching, Artificial Intelligence, Researchers
While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals. Pixabay

The team hopes to improve this by collecting data in more unstructured areas, or by using a new MIT-designed tactile glove, to better increase the size and diversity of the dataset.

“This is the first method that can convincingly translate between visual and touch signals”, said Andrew Owens, a post-doc at the University of California at Berkeley.

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The team is set to present the findings next week at the “Conference on Computer Vision and Pattern Recognition” in Long Beach, California. (IANS)