Dec. 21, 2018
Glendon Parker, a biochemist with Lawrence Livermore National Laboratory's Forensic Science Center, examines a 250-year-old archaeological hair sample that has been analyzed for human identification using protein markers from the hair. Photo: Julie Russell/LLNL.
DNA is the foundation of modern forensic science. From blood to saliva to semen to microscopic bits of DNA, criminals leave traces behind at crime scenes that have become the strongest evidence presented to juries over the last 30 years.
However, DNA has limitations. One such limitation is that the richest sources can be the most fragile and can be surprisingly hard to find enough of to really make a breakthrough. For instance, about half of all rape kits don’t yield enough genetic information to determine a profile of the perpetrator.
But imagine you had a hair from the offender in that kit. That single hair could provide proteins that are hardier than DNA—as well as a genetic sequence to help to identify the rapist.
One of the most futuristic new projects in DNA investigation is about how the fundamental blueprint of DNA is expressed — through proteins. Since the proteins that make up bone and teeth, hair and even microscopic skin cells are hardier, they are the subjects of a series of ongoing projects at Lawrence Livermore National Laboratory.
An agent-based model of innate immune response mechanistically simulates sepsis in 2D.
A deep learning approach originally designed to teach computers how to play video games better than humans could aid in developing personalized medical treatment for sepsis, a disease that causes about 300,000 deaths per year and for which there is no known cure.
Lawrence Livermore, in collaboration with researchers at the University of Vermont, is exploring how deep reinforcement learning can discover therapeutic drug strategies for sepsis by using a simulation of a patient's innate immune system as a platform for virtual experiments. Deep reinforcement learning is a state-of-the-art machine learning approach originally developed by Google DeepMind to teach a neural network how to play video games, given only pixels as input and the game's score as a learning signal. The algorithms often exceed human performance, despite not being given any knowledge about the mechanics of the game.
LLNL's deep learning approach treats the immune system simulation developed by their collaborators as a video game. Using outputs from the simulation, a "score" based on patient health and an optimization algorithm, the neural network learns how to manipulate 12 different cytokine mediators — immune system regulators — to drive the immune response to infection back down to normal levels.
An artist’s representation of a block copolymer vesicle with carbon nanotube porins embedded in its walls. Image by Ella Maru Studio.
Cellular membranes serve as an ideal example of a system that is multifunctional, tunable, precise and efficient.
Efforts to mimic these biological wonders haven’t always been successful. However, Lawrence Livermore National Laboratory scientists have created polymer-based membranes with 1.5-nanometer carbon nanotube pores that mimic the architecture of cellular membranes.
Carbon nanotubes have unique transport properties that can benefit several modern industrial, environmental and biomedical processes -- from large-scale water treatment and water desalination to kidney dialysis, sterile filtration and pharmaceutical manufacturing.
Taking inspiration from biology, researchers have pursued robust and scalable synthetic membranes that either incorporate or inherently emulate functional biological transport units. Recent studies demonstrated successful lipid bilayer incorporation of peptide-based nanopores, 3D membrane cages and large and even complex DNA origami nanopores.
However, LLNL scientists went one step further and combined robust synthetic bloc-copolymer membranes with another LLNL-developed technology: artificial membrane nanopores based on carbon nanotube porins, which are short segments of single-wall carbon nanotubes that form nanometer-scale pores with atomically smooth hydrophobic walls that can transport protons, water and macromolecules, and even DNA.
Microcapsules containing sodium carbonate solution (which contains baking soda) are suspended on a mesh during carbon dioxide absorption testing. The mesh allows many capsules to be tested at one time while keeping them separated, exposing more of their surface area.
A new microcapsule technology could make post-combustion carbon capture cheaper, safer and more efficient.
The approach developed by Lawrence Livermore researchers is very different than the traditional method of capturing carbon dioxide at a power plant. Instead of flowing a chemical solvent down a tower, they put the solvent into tiny microcapsules.
The microcapsules would be packed into a container and power plant exhaust gas would be flown through them.
Conventional designs also use an amine solvent that is expensive and can be dangerous to the environment. The microcapsule design created by LLNL and their collaborators at the University of Pittsburgh uses a solution that is made from a common household item – baking soda.
By using laser-generated, hologram-like 3D images flashed into photosensitive resin, researchers at Lawrence Livermore have discovered they can build complex 3D parts in a fraction of the time of traditional layer-by-layer printing.
There’s been a lot of buzz lately in the additive manufacturing realm about generative design, developing AM-friendly materials and shifting printer processing speeds into overdrive.
One example is a speedy printer with a novel design has been built at Lawrence Livermore National Laboratory. It flashes hologram-like 3D images generated by three lasers into a vat of photosensitive resin. Where the beams intersect, the light is most intense. The light stays on long enough to cure the part — about 10 seconds.
The researchers claim the process, called “volumetric 3D printing,” can build complex 3D parts in a fraction of the time needed with traditional layer-by-layer printing.
“It’s a demonstration of what the next generation of additive manufacturing may be,” said engineer Chris Spadaccini, who heads LLNL’s 3D printing effort.
The Livermore Lab Report will take a break for the holiday season. It will return Jan. 11.