There has to be a mathematical explanation for how bad that tie is.
from the film, A Beautiful Mind (2001)
~John Forbes Nash Jr., American Mathematician and Economist
Born: June 13, 1929
May 9, 2013
April Flowers for redOrbit.com – Your Universe Online
Climate change impacts on the surface of the Greenland Ice Sheet have been widely studied. An understanding, however, of the key processes in iceberg production has eluded researchers for a long time. A new study, led by the Universite Libre de Bruxelles, presents a sophisticated computer model that provides fresh insight into the impact of climate change on the production of icebergs by Greenland glaciers. The model also demonstrates that the shape of the ground beneath the ice has a strong effect on its movement.
Ice-loss from the Greenland Ice Sheet has been accelerating over the past decade. This raises concerns with scientists about runaway losses and consequent sea-level rise. Research into the four major Greenland fast-flowing glaciers, however, has enabled researchers to demonstrate that while these glaciers may show several bursts of retreat and periods of high iceberg formation in the future, the rapid acceleration of recent years is unlikely to continue unchecked.
The research team says that this is a critical step in understanding how Greenland’s glaciers will add to sea-level rise in the future. The findings, published in the journal Nature, indicate how important a more detailed knowledge of such glaciers is.
The team initially investigated the current behavior of the four glaciers, finding that the rate at which they lose ice depends critically on the shape of the fjords in which they sit, and the topography of the rock below them. Then, they designed a computer model for fast-flowing outlet glaciers from their investigations. The model, developed within the EU Ice2Sea program, projected a sea-level rise contribution from the glaciers of 0.8 inches to 2 inches by the year 2200. This is lower than estimates based solely on the extrapolation of current trends.
“I am excited by the way we have managed to create a detailed picture of the workings of the glaciers. It turns out that if the fjord a glacier sits in is wide or narrow it really affects the way the glacier reacts. The important role of the terrain below the ice shows we need to get a much clearer picture of the rest of Greenland’s glaciers before we have the whole story,” said Dr. Faezeh Nick, of the Universite Libre de Bruxelles.
The four glaciers – Petermann, Kangerdlugssuaq, Helheim and Jakobshavn Isbræ – were chosen because together they drain around 20 percent of the Greenland Ice Sheet. The model predicts that as a group the glaciers will lose, on average, 30 to 47 Gigatons (Gt) per year over the 21st century. A gigaton is equivalent to 1 cubic kilometer of water. Lake Geneva contains about 90Gt of water, for comparison.
Professor David Vaughan, who works at the British Antarctic Survey in Cambridge and is head of the ice2sea program said, “We know that the breaking off of icebergs from glaciers is influenced by climate, but this is the first time we’ve been able make projections of how the most important glaciers in Greenland will be affected by future climate change. The ice2sea research led by Dr Nick shows how a truly international program can make it possible for scientists to work together across different institutions to make significant steps forward.”
Apr. 7, 2013 — Scientists have described technology that accelerates microalgae’s ability to produce many different types of renewable oils for fuels, chemicals, foods and personal-care products within days using standard industrial fermentation.
Similar brain patterns emerge when seeing an object and conjuring it during sleep
Web edition: April 4, 2013 | ScienceNews
A computer can decode the stuff of dreams. By comparing brain activity during sleep with activity patterns collected while study participants looked at certain objects, a computer learned to identify some contents of people’s unconscious reveries.
“It’s striking work,” says cognitive psychologist Frank Tong of Vanderbilt University in Nashville, who was not involved in the research. “It’s a demonstration that brain activity during dreaming is very similar to activity during wakefulness.”
The work, reported April 4 in Science by Japanese researchers led by Yukiyasu Kamitani of Advanced Telecommunications Research Institute International, adds to somewhat scant knowledge of how the brain constructs dreams, says Tong. The research could lead to a better understanding of what the brain does during different states of consciousness, such as those experienced by some coma patients.
Dreams are a bit of a black box and difficult to study. Experiments with mice have revealed aspects of sleep and dreaming, such as how the experiences contribute to forming memories. But a mouse can’t tell you what it dreamed about. And the sleep stage that’s richest in dreams — REM sleep — typically kicks in about 90 minutes after a person conks out, making it time consuming to gather data on dreams. The noisy fMRI brain scanning machine doesn’t help.
To skirt these experimental issues, the researchers recorded brain activity in three adult male volunteers during the early stages of sleep. After the subjects had dozed off, they were repeatedly awakened and asked for detailed reports on what they had seen while sleeping. In an example, one participant stated: “Well, there were persons, about three persons, inside some sort of hall. There was a male, a female and maybe like a child. Ah, it was like a boy, a girl and a mother. I don’t think that there was any color.”
After gathering at least 200 such reports from the three men, the researchers used a lexical database to group the dreamed objects in coarse categories, such as street, furniture and girl. Then the study participants looked at images of things in those categories, while their brains were again scanned. Computer algorithms sorted through these patterns of brain activity, linking particular patterns with objects.
When the computer went back to the brain scans taken during dreaming, it did a pretty good job of distinguishing some of the signals, such as whether a dream contained a book or a girl. On average, the computer could pick which of two objects had appeared in a dream 70 percent of the time, a rate that is much better than would be expected by chance.
“To be able to get enough data to do this kind of analysis is really impressive,” says Russell Poldrack, a neuroimaging expert at the University of Texas at Austin.
The study bolsters the notion that the vivid imagery of dreams, no matter how fantastic, is as real as waking life, Kamitani says, at least from the brain’s perspective. Further research may reveal if the same is true about other dreamed senses, such as experienced sounds or emotions.