Wednesday June 26, 2019
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Scientists Find New Ways of Tracking Objects by Combining DNA of Dust Particles

Clothing, medicine and other items in one’s environment all have genetic markers, or fingerprints, that provide clues to where they came from, according to scientists

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Scientists say they have new ways of tracking where clothing, medicines and other items are made, making it harder for unscrupulous businesses to sell items that don't work or violate laws. VOA

Clothing, medicine and other items in one’s environment all have genetic markers, or fingerprints, that provide clues to where they came from, according to scientists.

Researchers are analyzing the microorganisms in dust particles that land on surfaces and are using artificial intelligence to read and classify the unique genetic codes of the microbes that vary from place to place.

“It is the collection of bacteria, fungi, viruses, protozoa that are present in any environment,” said Jessica Green, microbial systems expert and co-founder of Phylagen, a company that is building a microbial map of the world. Phylagen is collecting dust from different places and turning it into data by studying the DNA of the microscopic organisms in the particles.

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This digitally colorized microscope image provided by the National Institute of Allergy and Infectious Diseases (NIAID) shows Staphylococcus aureus bacteria in yellow. Bacteria are part of the collection of microorganisms that tell scientists where an object has been. VOA

Exposing labor abuses

Phylagen says its findings will provide real world applications. The California-based company says one application involves companies that outsource the manufacturing of products, such as clothing.

According to Human Rights Watch, unauthorized subcontracting of facilities in the apparel industry occurs often, and it is in these places that some of the worse labor abuses happen.

Phylagen is digitizing the genome of different locations by working in more than 40 countries and sampling the dust in hundreds of factories. The goal is to create a database so the microbes on each product can be traced.

“We sample the DNA of the products, and then, we use machine learning algorithms to map what is on the product with the factory, and can therefore verify for brands that their goods are made by their trusted suppliers in factories where you have good labor conditions, good environmental conditions versus unauthorized facilities which can be really detrimental,” Green said.

Tracking diseases, ships

With a database of distinct microbial DNA, Green said other possible future uses could include predicting the outbreak of disease and helping law enforcement track the movement of ships, since shipping logs can be falsified. Even counterfeit medicines could be traced as the database of microbial information grows, she said.

ALSO READ: Electric Cars Can Help You Live Longer: Study

“We can sequence the DNA of seized counterfeit pills, cluster together pills that have similar microbial signatures and then use that to help both pharmaceutical companies and the government, the U.S. government, gain some intelligence about how many different sources of these manufacturing facilities are there,” Green said. (VOA)

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Do You Know What All Activities Your Smartwatch Can Sense? Read Here To Find Out!

Apps might alert users to typing habits that could lead to repetitive strain injury (RSI), or assess the onset of motor impairments such as those associated with Parkinson's disease.

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To reach this conclusion, Harrison and his team began their exploration of hand activity detection by recruiting 50 people to wear specially programmed smartwatches for almost 1,000 hours while going about their daily activities. Pixabay

Smartwatches, with a few tweaks, can detect a surprising number of things your hands are doing like helping your spouse with washing dishes, chopping vegetables or petting a dog, say researchers from Carnegie Mellon University.

By making a few changes to the smartwatch’s operating system, they were able to use its accelerometer to recognise hand motions and, in some cases, bio-acoustic sounds associated with 25 different hand activities at around 95 percent accuracy.

Those 25 activities (including typing on a keyboard, washing dishes, petting a dog, pouring from a pitcher or cutting with scissors) are just the beginning of what might be possible to detect, the researchers said.

“We envision smartwatches as a unique beachhead on the body for capturing rich, everyday activities,” said Chris Harrison, Assistant Professor in Human-Computer Interaction Institute (HCII) at Carnegie.

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Sensing hand activity also lends itself to health-related apps — monitoring activities such as brushing teeth, washing hands or smoking a cigarette.
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“A wide variety of apps could be made smarter and more context-sensitive if our devices knew the activity of our bodies and hands,” he added.

Just as smartphones now can block text messages while a user is driving, future devices that sense hand activity might learn not to interrupt someone while they are doing certain work with their hands.

Sensing hand activity also lends itself to health-related apps — monitoring activities such as brushing teeth, washing hands or smoking a cigarette.

“Hand-sensing also might be used by apps that provide feedback to users who are learning a new skill, such as playing a musical instrument, or undergoing physical rehabilitation,” the study noted.

Apps might alert users to typing habits that could lead to repetitive strain injury (RSI), or assess the onset of motor impairments such as those associated with Parkinson’s disease.

To reach this conclusion, Harrison and his team began their exploration of hand activity detection by recruiting 50 people to wear specially programmed smartwatches for almost 1,000 hours while going about their daily activities.

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Those 25 activities (including typing on a keyboard, washing dishes, petting a dog, pouring from a pitcher or cutting with scissors) are just the beginning of what might be possible to detect, the researchers said.
Pixabay

More than 80 hand activities were labeled in this way, providing a unique dataset.

For now, users must wear the smartwatch on their active arm, rather than the passive (non-dominant) arm where people typically wear wristwatches, for the system to work.

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Future experiments will explore what events can be detected using the passive arm.

Harrison and HCII PhD student Gierad Laput presented the findings at “CHI 2019”, the Association for Computing Machinery’s conference on human factors in computing systems in Glasgow, Scotland. (IANS)