Friday, April 26, 2019


-->
This is the first-ever simulation of an entire gene

Largest simulation an entire gene of DNA, LANL image.

Given just how important genes are, it’s somewhat surprising that we have very little direct imagery of them functioning. The problem is that genes are so small and work so quickly that taking any photos or videos of them is nearly impossible. That’s why a group of researchers at Los Alamos National Laboratory turned to a computer simulation as the next best thing.

Using the Trinity supercomputer at Los Alamos, the researchers created a simulation of a single nanosecond of a gene. If one nanosecond sounds short to you—that’s about a billionth of a second—remember that the simulation contains over a billion atoms. For the researchers to simulate this gene, they have to not only simulate those individual atoms but also the electrical and chemical interactions between each pair of them. That’s an enormous amount of calculation. (Full story)


LANL researchers simulate billion-atom biomolecule  HPCwire

Detail of the billion atom DNA model, LANL image.

Researchers from Los Alamos National Laboratory, RIKEN Center for Computational Science in Japan, the New Mexico Consortium, and New York University have successfully created the first billion-atom simulation of an entire gene using a new approach they devised that reduces computational costs for such large simulations.

“It is important to understand DNA at this level of detail because we want to understand precisely how genes turn on and off,” said Karissa Sanbonmatsu, a structural biologist at Los Alamos and author of the paper. “Knowing how this happens could unlock the secrets to how many diseases occur.” It’s worth noting there is enough DNA in the human body to wrap around the earth 2.5 million times, which means it is compacted in a very precise and organized way. (Full story)

See the video



The hidden seismic symphony in earthquake signals


Many of the recent headline-grabbing developments in machine learning hinge on an approach called deep neural networks. Yet a simpler and more transparent form of machine learning called decision trees is unlocking impressive new scientific discoveries. In the case of our earthquake research at Los Alamos National Laboratory, a machine-learning process involving decision trees has revealed previously unsuspected physics principles that a deep neural network would have obscured and humans poring over data sets probably never would have noticed. To our surprise—and delight—this approach has led to a breakthrough in probing the mechanics of earthquakes, which will certainly advance our pursuit of the holy grail of geoscience: earthquake forecasting. (Full story)


Could machine learning be the key to earthquake prediction?

Earthquakes of magnitude 7.0 or higher between
1900 and 2013, USGS image.

Five years ago, Paul Johnson wouldn’t have thought predicting earthquakes would ever be possible. Now, he isn’t so certain. “I can’t say we will, but I’m much more hopeful we’re going to make a lot of progress within decades,” the Los Alamos National Laboratory seismologist says. “I’m more hopeful now than I’ve ever been.”

The main reason for that new hope is a technology Johnson started looking into about four years ago: machine learning. Many of the sounds and small movements along tectonic fault lines where earthquakes occur have long been thought to be meaningless. But machine learning—training computer algorithms to analyze large amounts of data to look for patterns or signals—suggests that some of the small seismic signals might matter after all. (Full story)

 
Seeing the quantum

Light generated by spontaneous parametric
down-conversion, from Aeon.

I spent a lot of time in the dark in graduate school. Not just because I was learning the field of quantum optics – where we usually deal with one particle of light or photon at a time – but because my research used my own eyes as a measurement tool. I was studying how humans perceive the smallest amounts of light, and I was the first test subject every time. Author Rebecca Holmes is a physicist and staff scientist at Los Alamos National Laboratory. (Full story)


SuperCam developed in Los Alamos to be used on rover in 2020 mission to Mars 
SuperCam undergoing final preperations.

On Monday, a camera developed here in New Mexico makes its first stop on its mission to Mars. Researchers at the Los Alamos National Laboratory say the SuperCam will be a key feature on the 2020 Mars rover. It will be attached to the rover currently at NASA's Jet Propulsion Laboratory in Pasadena, Calif. before heading to space next year.

"SuperCam is like a geological observatory on Mars," said Roger Wiens, principal investigator on the SuperCam at Los Alamos National Laboratory. The future of space exploration is in the works -- and it's happening right here in New Mexico with the development of the SuperCam. (Full story)


In the Lab: Building the next generation of experts

John Kramer, LANL photo.

In a woodsy part of the Los Alamos National Laboratory where elk linger outside his building, John Kramer is guiding the next generations of high explosives experts.

The lab’s esteemed explosives enclave has been Kramer’s turf since he was 19, mopping up water in big bays and growing accustomed to the shaking, rumbling world around him. Now, 37 years later, Kramer is a revered R&D engineer who holds two patents and keeps the lab’s detonator powder production plant humming to meet growing demands. (Full story)