Apple Inc. and Google unveiled a rare partnership to add technology to their smartphone platforms that will alert users if they have come into contact with a person with Covid-19. People must opt in to the system, but it has the potential to monitor about a third of the world’s population.
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.
Amid the coronavirus pandemic that has spread across the U.S., a number of states have extended their lockdown orders in an effort to combat the spread of the virus.
As of April 8, every U.S. state has reported confirmed coronavirus cases but the stay-at-home and shelter-in-place policies all came at different times. California was the first state to issue a stay-at-home order on March 19, while South Carolina was the most recent, issuing their statewide order on April 7. Some other states like Arkansas, Iowa, North Dakota and Nebraska have yet to issue statewide stay-at-home orders.
Just a few hours later, Trump tweeted it was his decision — not that of state governors — of when states should re-open.
Materials scientists at Duke University have shown the first clear example that a material’s transition into a magnet can control instabilities in its crystalline structure that cause it to change from a conductor to an insulator.
If researchers can learn to control this unique connection between physical properties identified in hexagonal iron sulfide, it could enable new technologies such as spintronic computing. The results appear April 13 in the journal Nature Physics.
Commonly known as troilite, hexagonal iron sulfide can be found natively on Earth but is more abundant in meteorites, particularly those originating from the Moon and Mars. Rarely encountered in the Earth’s crust, most troilite on Earth is believed to have originated from space.
The new viruses are not harmful to humans or closely related to SARS-CoV-2, the coronavirus that causes COVID-19.
The Ancient History of AI:
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Artificial intelligence has been a topic of growing prominence in the media and mainstream culture since 2015, as well as in the investment world, with start-ups that even mention the word in their business model, gaining massive amounts of funding.
Researchers at the Hebrew University of Jerusalem announced on Sunday that they have developed a new method of testing for COVID-19 which is not only 4–10 times faster than the tests most commonly used today, but also significantly cheaper, while supplying the same level of accuracy. Moreover, most of the materials required to perform the new test are already available in Israel, easing significantly both the country’s dire shortage of testing materials and its heavy economic dependence on foreign commercial markets. The method was developed in the labs of Prof. Nir Friedman of the Institute of Life Sciences and the School of Engineering and Computer Sciences and Dr. Naomi Haviv of Hebrew University’s Neuroscience Research Center, and is based on materials which are not affected by global shortages and can be recycled for repeated used on future tests. The method commonly used today for COVID-19 testing involves extracting RNA molecules from a patient’s sample to determine if the molecules produced have viral RNA within them, which confirms the presence of the coronavirus. The new test developed by the researchers performs the same action, but is made from more commonly attainable materials, that produce results at a much higher speed. Dr. Naomi Haviv said that “We have an efficient RNA extraction method, 4–10 times faster than the current method. It is based on magnetic beads and can be performed both robotically and manually.”
Other than the magnetic beads, all of the other materials needed to perform the tests are available for purchase in Israel. The beads themselves are recyclable and can be reused to perform future tests. “The robotic method has already undergone a series of tests at Hadassah Hospital, using hundreds of samples from patients — and is now becoming operational.”
The new test would supply results 4–10 times faster than the current method, and is made almost entirely from materials which are easily available for purchase within the country.
