FoodPoisoning
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After Math: They're on the move
With the president's made up migrant caravan crisis having mysteriously vanished now that the midterms are over, it's time to take a look at the other movers and shakers from the industry this week. Volkswagen announced the development of a $23k Tesla rival, China has developed security cameras can now ID people by their gait, and Google's built a computer model to guess which restaurants will give you the runs.
Researchers genetically engineer Salmonella to eat brain tumors
Salmonella has earned its bad reputation. It is responsible for more than a million cases of food poisoning every year, of which nearly 400 people die. But a team of researchers from Duke University have recently engineered the bacteria to not attack the human gastrointestinal tract, but rather the most aggressive form of brain cancer known to man.
Scientists use lasers to sniff out E. coli on food
Scientists have developed a better sniff test for that plate of leftovers that's been hanging out in your fridge. Researchers from the Korea Advanced Institutes of Science and Technology are using lasers (the jury is out on whether they're of the "friggin'" variety or not) to determine just how much bacteria is crawling all over your stored food. MIT Technology Review says that by shooting a red, coherent laser at chicken breast coated with E. coli and Bacillus cereus, the Korean scientists were able to detect changes in laser refractions, indicating the bacteria's presence.
nEmesis system machine reads tweets, tells you which burrito joint to avoid
We all know that customer reviews can be prone to, shall we say, a little positive engineering. What if you could gather genuine opinions about a restaurant, or product before you commit your cash? Well, a new system developed at the University of Rochester might be able to offer just that. The "nEmesis" engine uses machine learning, and starts to listen when a user tweets from a geotagged location that matches a restaurant. It then follows the user's tweets for 72 hours, and captures any information about them feeling ill. While the system isn't able to determine that any resulting affliction is directly connected to their restaurant visit, results over a four-month period (a total of 3.8-million analysed tweets) in New York City found 480 reports of food poisoning. It's claimed these data match "fairly well" with that gathered by the local health department. The system's creators admit it's not the whole picture, but could be used alongside other datasets to spot potential problems more quickly. The only question is how long before we see "sabotage" tweets?
