In many ways, Gitanjali Rao is an ordinary 11-year-old, lively and chatty, yet her scientific spirit makes a huge difference. Last month, she ___________ the top prize at the Discovery Education 3M Young Scientist Challenge for her invention.

What ___________ Gitanjali’s work was that her city faced a water emergency with too much lead in its water. “The idea didn’t ___________ to me until I saw my parents try to do water tests. The test strips may show inaccurate results, which weren’t very ___________.”

She recalls. She then searched for suitable materials with continued efforts and ___________ found that carbon nanotube (纳米管)sensors can be used to sense chemicals. Gitanjali ___________ to build a small blue housing using the 3-D printer at her school with computer chips and a battery inside. A free app, which Gitanjali designed under ___________ from her computer science teacher, gives instant ___________ and sends the data to a linked phone through an attached device.

The process of designing her project wasn’t always ___________, though. Kathleen Shafer, a scientist paired with Gitanjali as her teacher in summer, ______________ along the way. In the award ceremony, Gitanjali also ______________ her parents with constant support and ______________ to try “crazy ideas”. Gitanjali received $25, 000 to further develop her program along with the great ______________ of winning the contest. According to Shafer, who ______________ Gitanjali, “she shows a lot of motivation to solve ______________ issues through science”.

Face shape lets AI spot rare disorders

People with genetic syndromes (综合征) sometimes have revealing facial features, but using them to make a quick and cheap diagnosis can be _______given there are hundreds of possible conditions they may have. A new neural (神经系统) network that analyses photographs of faces can help doctors _______the possibilities.

Yaron Gurovich at biotechnology firm FDNA in Boston and his team built a neural network to look at the overall impression of faces and _______ a list of the 10 genetic syndromes a person is most likely to have.

They _______ the neural network, called DeepGestalt, on 17,000 images correctly labeled to match more than 200 genetic syndromes.The team then asked AI to _______potential genetic disorders from a further 502 photos of people with such conditions. It included the correct answer among its list of 10 responses 91 per cent of the time.

Gurovich and his team also_______the neural network's ability to distinguish between the different genetic mutations (父异) that can lead to the same syndrome.They used photographs of people with Noonan syndrome, which can result from mutations in any one of five genes.DeepGestalt correctly identified the genetic source of the physical appearance 64 per cent of the time. It’s clearly not _______, but it’s still much better than humans are at trying to do this.

As the system makes its assessments, the facial regions that are most helpful in the determination are_______and made available for doctors to view. This helps them to understand the relationships between genetic make-up and physical appearance.

The fact that the diagnosis is based on a simple photograph raises questions about_______. If faces can reveal details about genetics, then employers and insurance providers could, in principle,__________ use such techniques to__________ against people who have a high probability of having certain disorders.__________. Gurovich says the tool will only be __________for use by clinicians.

This technique could bring significant__________ for those who have genetic syndromes. The real value here is that for some of these ultra-rare diseases, the process of diagnosis can be many, many years. This kind of technology can help narrow down the search space and then be confirmed through checking genetic markers. For some diseases, it will cut down the time to diagnosis dramatically. For others, it could perhaps add means of finding other people with the disease and, __________, help find new treatments or cures.

If you have a smartphone, you probably use its front-facing camera for selfies and the occasional video call. Perhaps, if you’re ________, you’ll shoot the next TikTok masterpiece. You might use your next smartphone’s ________ camera for the same things, but there’s a chance that camera won’t completely ________ once you’re done with it.

This week, chipmaker Qualcomm revealed its latest Snapdragon processor, which will _______ many of the high-end Android smartphones you’ll see in stores. And a new feature built into that chip could allow smartphone makers to keep those front-facing cameras on_________.

The idea of a camera that stays on as long as your phone does seems deeply ________, even in an age where people are convinced that smartphones are already eavesdropping (窃听) on our ________. So why is a company responsible for building the brains of our smartphones trying to make “always-on” cameras a(n) ________ feature?

In time, a phone using this new chip could practicably ________ itself when it sees your face, and automatically lock itself again when you’re not looking at it anymore. Or, if it sees someone else’s face next to yours, the phone could automatically ________ notifications so no one else can see what your ________ emails or Slack messages are about. The benefit of a phone that’s always looking for you, it seems, is that it knows to act different when you’re not________.

As it turns out, the Qualcomm tech that could make such features possible is conceptually pretty ________. “What’s happening here is detecting a binary (二进制): is there a face, or is there not a face,” Heape, Senior Director of Product Management of the Qualcomm, told me. “There is not a photograph taken. There is no ________ being recorded.” He also noted that none of camera data ________ the chip when it’s being analyzed for faces.