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A new deep-learning algorithm could provide advanced notice when systems — from satellites to data centers — are falling out of whack.

When you’re responsible for a multimillion-dollar satellite hurtling through space at thousands of miles per hour, you want to be sure it’s running smoothly. And time series can help.

A time series is simply a record of a measurement taken repeatedly over time. It can keep track of a system’s long-term trends and short-term blips. Examples include the infamous Covid-19 curve of new daily cases and the Keeling curve that has tracked atmospheric carbon dioxide concentrations since 1958. In the age of big data, “time series are collected all over the place, from satellites to turbines,” says Kalyan Veeramachaneni. “All that machinery has sensors that collect these time series about how they’re functioning.”

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Before the pandemic, the lab of Stanford University biochemist Peter S. Kim focused on developing vaccines for HIV, Ebola and pandemic influenza. But, within days of closing their campus lab space as part of COVID-19 precautions, they turned their attention to a vaccine for SARS-CoV-2, the virus that causes COVID-19. Although the coronavirus was outside the lab’s specific area of expertise, they and their collaborators have managed to construct and test a promising vaccine candidate.

“Our goal is to make a single-shot vaccine that does not require a cold-chain for storage or transport. If we’re successful at doing it well, it should be cheap too,” said Kim, who is the Virginia and D. K. Ludwig Professor of Biochemistry. “The target population for our vaccine is low-and middle-income countries.”

Their vaccine, detailed in a paper published in ACS Central Science (“A Single Immunization with Spike-Functionalized Ferritin Vaccines Elicits Neutralizing Antibody Responses against SARS-CoV-2 in Mice”), contains nanoparticles studded with the same proteins that comprise the virus’s distinctive surface spikes.

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The rover won’t land on the Red Planet until May.

China’s Tianwen-1 Mars orbiter and rover are speeding toward the Red Planet and preparing to arrive on Feb. 10, the China National Space Administration (CNSA) has said.

Tianwen-1 has been in space for nearly 24 weeks and was around 81 million miles (130 million kilometers) from Earth and 5.15 million miles (8.3 million km) from Mars on Jan. 3 Beijing time, according to CNSA.

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(Inside Science) — It took years of painstaking work for Carlos Souza and his colleagues to map out every road in the Brazilian Amazon biome. Official maps of the 4.2 million-square-kilometer region only show roads built by federal and local governments. But by carefully tracing lines on satellite images, the researchers concluded in 2016 that the true length of all the roads combined was nearly 13 times higher.

“When we don’t have a good understanding of how much roadless areas we have on the landscape, we probably will misguide any conservation plans for that territory,” said Souza, a geographer at a Brazil-based environmental nonprofit organization called Imazon.

Now, Imazon researchers have built an artificial intelligence algorithm to find such roads automatically. Currently, the algorithm is reaching about 70% accuracy, which rises to 87%-90% with some additional automated processing, said Souza. Analysts then confirm potential roads by examining the satellite images. Souza presented the research last month at a virtual meeting of the American Geophysical Union.

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