IBM wants to protect our food by sequencing its supply chains

[company]IBM[/company] is teaming up with food conglomerate Mars to study, and hopefully protect, citizens from foodborne illnesses by sequencing the genes of the tiny organisms that populate our food chains. Consisting of just the two companies right now but expected to grow, the effort is being called, aptly, the Consortium for Sequencing the Food Supply Chain.

The companies will study the metagenomics — essentially, the metadata — of safe factories, farms, grocery stores and other areas in order to determine what’s normal, explained Jeff Wesler, the vice president and lab director IBM’s Almaden research center in San Jose, California. Eventually, the goal is to understand enough about normal, safe conditions that companies will be able to detect deviations early enough to prevent them from spurring an outbreak of salmonella, E. coli, or other dangerous bacteria or chemicals.

For example, he explained, although many places along the food supply chain know to test for salmonella, they might not know to test for, or have any reason to expect, contamination by other substances. Those could be anything from other, foreign bacteria to chemicals such as the melamine found in Chinese milk and infant formula in 2008. Understanding the metagenomics of the product being sold and the factories producing it means companies and regulatory agencies might be able to spot a problem in the microbial ecosystem and then get to work determining what’s causing it.

According to the Centers for Disease Control, foodborne illnesses sicken one in six Americans each year and kill about 3,000 people in the United States. A 2012 study published in the Journal of Food Prediction estimates the annual economic impact of of foodborne illness at nearly $78 billion.

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Right now, Wesler predicts it will be about three to five years before the results of this research might be deployed commercially. The companies will spend the first couple years getting to know the baselines microbiomes of various areas and things, understanding what they’re composed of and how they react to changes in environments or to other stuff. Initial research will focus on Mars facilities, which span a range of products including candy, pet food, packaged food and coffee.

Hopefully, they’ll be able to build up a database of connections between bacteria, chemicals, heavy metals and other substances, and their reactions in the presence of each other. After that, Wesler thinks they’ll be able to begin working on a set of tests that makes sense for particular industries and that can be implemented in a reasonably easy manner.

Wesler calls this the “quintessential big data problem” because it involves analyzing so much data and is only really possible to solve now because of advances in the required technology. In this case, that’s not just cheap data storage and new data-analysis tools, but also better genetic-sequencing technologies. “One of the reasons we even think this is feasible is because of the rise of next-generation sequencing,” he said.

A major uptick in the capabilities of any piece of the chain could speed up the research, he said, but the current state of the art should be capable to work within the predicted time frame.

Deep learning now tackling autism and matching monkeys’ vision

Two studies published this week provide even more evidence that deep learning models are very good at computer vision and might be able to tackle some difficult problems.

The study on computer vision, out of MIT and published in PLOS Computational Biology, shows that deep learning models can be as good as certain primates when it comes to recognizing images during a brief glance. The researchers even suggest that deep learning could help scientists better understand how primate vision systems work.

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Charts showing the relative performance of primates and deep learning models.

The genetic study, performed by a team of researchers from the Canadian Institute for Advanced Research and published in Science (available for a fee, but the University of Toronto has a relatively detailed article about the research), used deep learning to analyze the “code” involved in gene splicing. Focusing on mutated gene sequences in subjects with autism, the team was able to identify 39 additional genes that might be tied to autism spectrum disorder.

By now, the capabilities of deep learning in object recognition have been well established, and there is plenty of excitement among entrepreneurs and scientists about how it could apply in medicine. But these findings suggest that excitement has substance and the techniques can make meaningful impacts in areas have little or nothing to do with the web, from where many recent advances have emerged.

How federal money will spur a new breed of big data

By pumping hundreds of millions of dollars into big data research and development, the Obama administration thinks it can push the current state of the art well beyond what’s possible today, and into entirely new research areas. It’s a noble goal, but also a necessary one.