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The Quality, Not Quantity, of Cardiovascular Fat Can Interfere With Memory Later in Life

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

A worsening cardiovascular profile after menopause may contribute to the fact that women are disproportionately affected by dementia. A new study identified a link between cardiovascular fat volume and radiodensity and cognitive function, as well as racial differences in this association.

Study results will be presented during The North American Menopause Society (NAMS) Annual Meeting in Washington, DC, September 22–25, 2021.

Machine learning uncovers ‘genes of importance’ in agriculture and medicine

“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 to predict outcomes in agriculture and medicine is both a promise and challenge for . 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.

In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.

Samsung in talks with Tesla to make next-gen self-driving chips —Korea Economic Daily

The logo of Samsung Electronics is seen at its office building in Seoul, South Korea, August 25 2017. REUTERS/Kim Hong-Ji/File Photo.

SEOUL, Sept 23 (Reuters) — Samsung Electronics (005930.KS) is in talks with Tesla (TSLA.O) to make Tesla’s next-generation self-driving chips based on Samsung’s 7-nanometre chip production process, a South Korean newspaper reported on Thursday.

Since the beginning of this year, Tesla and Samsung have discussed chip design multiple times and exchanged chip prototypes for Tesla’s upcoming Hardware 4 self-driving computer, the Korea Economic Daily reported, citing sources with direct knowledge of the matter.

How multi-skilled AI could chart a path to human-like intelligence

A more comprehensible concept could be “multi-skilled AI.”

Multi-skilled AI is an approach to improving technologies by expanding their senses. In a similar way to how kids learn through perception and talking, multi-skilled AI systems combine senses and language to broaden their understanding of the world.

“It goes beyond image or language recognition and allows multiple tasks to be done,” Elizabeth Bramson-Boudreau, the CEO and publisher of MIT Technology Review, tells TNW.