Lifeboat Foundation

Intelligent sensing and tele-presence robotic technology, enabling health practitioners to remotely assess a person’s physical and cognitive health from anywhere in the world, is being pioneered in research co-led at the University of Strathclyde.

The technology could aid cost-effective diagnosis, more regular monitoring and health assessments alongside assistance, especially for people living with conditions such as Alzheimer’s disease and other cognitive impairments.

The system was demonstrated for the first time to the UK Government Minister, Iain Stewart during a visit to the construction site of the National Robotarium, hosted at Heriot-Watt University, which is co-leading the research with Strathclyde.

Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules [1]. This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.

This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.

Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.

“We have no guarantee that these antibodies that are out there will continue being effective against any new variants that occur,” Georgiev said.

According to a release from Vanderbilt, “Georgiev and his colleagues describe the isolation of a monoclonal antibody from a patient who had recovered from COVID-19 that ‘shows potent neutralization’ against SARS-CoV-2. It also is effective against variants of the virus that are slowing efforts to control the pandemic.”

VUMC said researchers can also use the technology to screen antibodies against any current variant of COVID-19, and researchers hope even other viruses that have not yet caused human disease but have the potential of doing so.

Cardiovascular fat deposition, found to be higher in postmenopausal women compared with premenopausal women, is a novel risk factor for cardiovascular disease. It is also believed to affect cognitive function through neuropathological pathways by changing the secretion of inflammatory cytokines and adipokines. The quality of cardiovascular fat is characterized by its radiodensity.

Summary: Greater radiodensity of perivascular adipose tissue in women during midlife was associated with decreased working memory performance later in life.

Source: NAMS

Continue reading “The Quality, Not Quantity, of Cardiovascular Fat Can Interfere With Memory Later in Life” »

“We show that focusing on genes whose expression patterns are evolutionarily conserved across species enhances our ability to learn and predict ‘genes of importance’ to growth performance for staple crops, as well as disease outcomes in animals,” explained Gloria Coruzzi, Carroll & Milton Petrie Professor in NYU’s Department of Biology and Center for Genomics and Systems Biology and the paper’s senior author.

Machine learning can pinpoint “genes of importance” that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture.

Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure—which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.

Continue reading “Machine learning uncovers ‘genes of importance’ in agriculture and medicine” »

CRISPR is the genius behind innovations that seemed impossible a decade ago. Could you grow tomatoes with the kick of hot sauce or ferment wine that doesn’t cause a hangover? That’s just two of the things scientists are looking into.

Circa 2018

Researchers at the University of Michigan just created the world’s smallest computer (again). Their previous micro-computer, the Michigan Micro Mote, measured 2x2x4mm. It was a complete, functioning system powered by solar cell batteries. But in March this year, IBM announced a new, smaller computer, which measured 1×1 mm, and was smaller than a grain of salt. It “raised a few eyebrows at the University of Michigan.”

After all, it’s unclear if the IBM computer even count as an actual microcomputer. The IBM device lost all its programming and data as soon as it turns off, unlike the Michigan Micro Mote, which retained its programming even when it wasn’t externally powered. “It’s more of a matter of opinion whether they have the minimum functionality required,” said David Blaauw, a professor of electrical and computer engineering at University of Michigan who helped develop the University of Michigan’s newest tiny device. If the IBM machine constituted a computer, then University of Michigan would work to gain back their title: their latest microdevice measures 0.3mm per side (1/10th the size of IBM’s computer), and is smaller than a grain of rice.

Continue reading “The Smallest Computer in the World Fits On a Grain of Rice” »

DNA has the capacity to store large amounts of information for very long durations. This protocol describes encoding of digital files as DNA and the error-free retrieval of the stored data from the sequenced data.

Know Labs’ glucose monitors are both powered by its Body-Radio Frequency Identification, or Bio-RFID, technology. The Bio-RFID sensors emit radio waves to measure specific molecular signatures in the blood through the skin, calculated using spectroscopy.

“We know that not all people with diabetes are looking for a wearable continuous glucose monitoring device to manage their diabetes. Some simply want to replace the painful, inconvenient and expensive fingersticks they currently rely on,” said CEO Phil Bosua, who invented the Bio-RFID technology. “The Bio-RFID sensor we currently use for our internal product testing fits in your pocket and is ready for final use, so we decided to create the KnowU as a portable, affordable and convenient alternative requiring no disposable items, such as test strips and lancets.”

In vitro tests have found that the radiofrequency sensor technology was able to measure glucose levels with accuracy comparable to that of Abbott’s Freestyle Libre continuous glucose monitor, which uses a sensor attached to the back of the arm for up to two weeks at a time. According to a 2018 study (PDF) comparing the two, 97% of the UBand’s readings were within 15% of the values calculated by Abbott’s device.