Thursday August 16, 2018

AI Technology Could Help Protect Water Supplies

Moving forward, the goal is an AI system to continuously monitor water flowing through a microscope for a wide range of contaminants and microorganisms

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It's critical to have running water, even if we have to boil it, for basic hygiene. Pixabay
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Researchers have developed a novel artificial intelligence (AI)-based software that could make monitoring at water treatment plants cheaper and easier and help safeguard public health.

The new technology is capable of identifying and quantifying different kinds of cyanobacteria, or blue-green algae, as a threat to shut down water systems when it suddenly proliferates.

“We need to protect our water supplies. This tool will arm us with a sentinel system, a more rapid indication when they are threatened,” said Monica Emelko, Professor at the University of Waterloo in Ontario, Canada.

The operational AI system uses software in combination with a microscope to inexpensively and automatically analyse water samples for algae cells in about one to two hours, including confirmation of results by a human analyst.

water
The new technology is capable of identifying and quantifying different kinds of cyanobacteria, or blue-green algae, as a threat to shut down water systems when it suddenly proliferates. Pixabay

The AI system would provide an early warning of problems since testing could be done much more quickly and frequently than current existing methods, said Alexander Wong, Professor at the varsity.

Moving forward, the goal is an AI system to continuously monitor water flowing through a microscope for a wide range of contaminants and microorganisms.

Also Read: Car Tyres May Purify Wastewater in Future

The researchers estimate it may take two to three years to refine a fully commercial sample testing system for use in labs or in-house at treatment plants. The technology to provide continuous monitoring could be three to four years away.

“It’s critical to have running water, even if we have to boil it, for basic hygiene,” Emelko said. (IANS)

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Researchers Develop AI That Can Help Make Cancer Treatment Less Toxic

The new "self-learning" machine-learning technique could make the dosing regimen less toxic but still effective

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Cancer
Indian-American researchers unleash turmeric's power to fight cancer. Pixabay

MIT researchers, including one of Indian-origin, have developed novel machine-learning techniques to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for an aggressive form of brain cancer.

Glioblastoma is a malignant tumour that appears in the brain or spinal cord, and the prognosis for adults is no more than five years.

Patients are generally administered maximum safe drug doses to shrink the tumour as much as possible, but they still remain at risk of debilitating side effects.

The new “self-learning” machine-learning technique could make the dosing regimen less toxic but still effective.

It looks at the treatment regimen currently in use, and finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumour sizes to a degree comparable to that of traditional regimen, the researchers said.

“We kept the goal where we have to help patients by reducing tumour sizes but, at the same time, we want to make sure the quality of life — the dosing toxicity — doesn’t lead to overwhelming sickness and harmful side effects,” said Pratik Shah, principal investigator from the Massachusetts Institute of Technology (MIT) in Boston, US.

Cancer
Representational image. Pixabay

The findings will be presented at the 2018 Machine Learning for Healthcare conference at Stanford University in California, US.

In simulated trials of 50 patients, the model comprising of artificially intelligent “agents”, designed treatment cycles that reduced the potency to a quarter or half of nearly all the doses while maintaining the same tumour-shrinking potential.

Many times, it skipped doses altogether, scheduling administrations only twice a year instead of monthly.

You May Also Like to Read About The Relation of Cancer Cells With Immune System- Decoded: How Cancer Cells Cripple Immune System

However, the researchers also had to make sure the model does not just dish out a maximum number and potency of doses. Whenever the model chooses to administer all full doses, it gets penalized, so instead it chooses fewer, smaller doses.

“If all we want to do is reduce the mean tumour diameter, and let it take whatever actions it wants, it will administer drugs irresponsibly,” Shah said.

“Instead, we need to reduce the harmful actions it takes to get to that outcome.” (IANS)