MIT News

MIT engineers have spun polyethylene into fibers and yarns designed to wick away moisture. They wove the yarns into silky, lightweight fabrics that absorb and evaporate water more quickly than common textiles such as cotton, nylon, and polyester.

Read the article: https://news.mit.edu/2021/plastic-bags-recycle-fabrics-0315

A new study co-authored by MIT scholars suggests that healthier women are more likely to follow age-based guidelines, leaving room for better-targeted testing. Women who start getting mammograms at age 40 may be healthier than the population of 40-year-old women as a whole — and they have a lower incidence of breast cancer than those who do not start getting tested at that age. Therefore, simply changing age recommendations is not, by itself, an optimal way to make breast-cancer screening policy.

Read the article: https://news.mit.edu/2020/mammogram-age-guidance-1217

At a time when hostilities between countries increasingly occur online a new paper, co-authored by an MIT professor, suggests why countries that retaliate too much against online attacks make things worse for themselves.

Read the article: https://news.mit.edu/2020/cybersecurity-deterrence-retaliation-1210

This recording is a product of a glissando of sound waves that a team of MIT physicists sent through a carefully controlled gas of elementary particles known as fermions. The pitches that can be heard are the particular frequencies at which the gas resonates, like a plucked string. The way that sound travels through this fluid can be used to calculate the sound, and “quantum friction,” in neutron stars and other perfect fluids.

Read the related story: https://news.mit.edu/2020/sound-perfect-fluid-1203

More than 75 percent of the the quality engineering startup Ultranauts' employees are on the autism spectrum, allowing the company to tap into the unique strengths of each team member as it helps large enterprises and mature startups improve the quality of their data, analytics, and software.

Read the article: https://news.mit.edu/2020/ultranauts-neurodiversity-1201

A new MIT-developed system called RoboGrammar makes it possible to simulate and determine which robot design, out of thousands of possibilities, will work best based on what parts you have laying around your shop and what terrain it needs to traverse.

Read the article: https://news.mit.edu/2020/computer-aided-robot-design-1130

Since the coronavirus pandemic began in the spring, many people have only seen their close friends and loved ones during video calls, if at all. A new study from MIT finds that the longings we feel during this kind of social isolation share a neural basis with the food cravings we feel when hungry.

Read the article: https://news.mit.edu/2020/hunger-social-cravings-neuroscience-1123

For more information read the article: https://news.mit.edu/2020/covid-19-cough-cellphone-detection-1029

TRANSCRIPT:

[AUDIO RECORDING OF A PERSON COUGHING]

NARRATOR: Asymptomatic people who are infected with Covid-19 exhibit, by definition, no discernible physical symptoms of the disease. But it seems those who are asymptomatic may not be entirely free of changes wrought by the virus. The differences between a cough of an asymptomatic patient and a healthy individual are not decipherable to the human ear, but it turns out that they can be picked up by artificial intelligence. 

NARRATOR: For example, here is a cough of a healthy individual:[AUDIO RECORDING OF A HEALTHY INDIVIDUAL]

NARRATOR: And now here is a cough of an asymptomatic person with Covid-19: [AUDIO RECORDING OF AN ASYMPTOMATIC COVID-19 POSITIVE PATIENT]

NARRATOR: To make things even more challenging, listen to a person who has symptoms and is Covid-19 positive: [AUDIO RECORDING OF A COVID-19 POSITIVE PATIENT WITH SYMPTOMS]

NARRATOR: It is very hard, frankly almost impossible, for a person to distinguish these three cough’s, even after you’ve listened to them multiple times. But a team of MIT researchers report they have developed an AI model that can distinguish asymptomatic people with Covid-19 from healthy individuals without the disease through forced-cough recordings. 

NARRATOR: To develop their model, the researchers used tens of thousands of samples of coughs submitted by people voluntarily though web browsers and devices such as cellphones and laptops. When they fed the model new cough recordings, it accurately identified 98.5 percent of coughs from people who were confirmed to have Covid-19, including 100 percent of coughs from the asymptomatic, who reported they did not have symptoms but had tested positive for the virus.

NARRATOR: When the AI model is fed the cough of a Covid asymptomatic person [AUDIO RECORDING OF AN ASYMPTOMATIC COVID-19 PATIENT] they found it was able to pick up patterns in the four biomarkers — vocal cord strength, sentiment, lung and respiratory performance, and muscular degradation — that are specific to Covid-19.

NARRATOR: When the model is fed the cough of a covid-positive individual who IS exhibiting symptoms [AUDIO OF A SYMPTOMATIC COVID-19 POSITIVE PATIENT] it is actually harder for artificial intelligence to discriminate. The researchers think it is because there are many conditions that create symptoms, such as the flu or asthma and therefore the results are confounded. For this reason, they stress that their AI model is not meant to diagnose symptomatic people, OR determine whether their symptoms are due to Covid-19 or other conditions. The tool’s strength lies in its ability to discern asymptomatic coughs from healthy ones.

 

NARRATOR: The team is now working on incorporating the model into a user-friendly app which if FDA-approved and adopted on a large scale could potentially be a free, convenient, noninvasive prescreening tool to identify people who are likely to be asymptomatic for Covid-19. A user could log in daily, cough into their phone and instantly get information on whether they might be infected and therefore should confirm with a formal test.

In elections, every vote counts. Or should count. But a new study by an MIT professor indicates that in the 2016 U.S. general election, 4 percent of all mail-in ballots were not counted — about 1.4 million votes, or 1 percent of all votes cast, signaling a significant problem that could grow in 2020.

Read the article: http://news.mit.edu/2020/odds-mail-vote-not-count-1019

A study co-authored by MIT political scientist, Charles Stewart, quantifies the "blue shift" effect by state, analyzes its causes, and shows why the 2020 election might indeed be decided after Nov. 3.

Read the article: http://news.mit.edu/2020/votes-counted-after-election-1015