LAB REPORT

For the first time, the planet's top scientists said in a monumental report released Monday that they have definitively linked greenhouse gas emissions to the type of disasters driven by a warmer climate that have touched every corner of the globe this year: extreme rainfall in Germany and China, brutal droughts in the western U.S., a record cyclone in the Philippines and compound events like the wildfires and heat waves from the Pacific Northwest to Siberia to Greece and Turkey.

This is the world as it exists today, with an atmosphere 1.1 degrees Celsius hotter than it was in the pre-industrial era thanks largely to burning fossil fuels such as coal, oil and natural gas. Even grimmer: There is no scenario in the new analysis by the United Nations' Intergovernmental Panel on Climate Change in which the world avoids breaching the threshold of 1.5 degrees Celsius that the U.S., EU and several other countries have set as a target. Even the weaker 2-degree target that major polluters China and India have set as guideposts will be eclipsed unless greenhouse gas emissions peak by mid-century.

Advanced computing improvements have enabled those types of precision forecasts, said Paul Durack, a research scientist at Lawrence Livermore National Laboratory and an IPCC author on the science methods chapter. Scientists can now run powerful simulations accounting for numerous variables that are getting ever more complex, such as modeling what happens with greenhouse gas emissions from land-use change like deforestation or the thawing of methane-holding Arctic permafrost.

Some of those advances clarified that the world had already warmed 0.1 degree Celsius more than previously thought, the report said.

Mammals' immune systems are amazingly specialized. Among their myriad functions, they churn out proteins called antibodies in response to certain foreign matter, including bioagents. An antibody generated from a particular bioagent will bind to it and nothing else — a phenomenon biochemical detectors can exploit.

Antibody tests have the added advantage of being able to detect both microorganisms and biological toxins, which carry no DNA. At their simplest, such systems resemble a home pregnancy test: antibodies are fixed to a strip of cellulose on a plastic backing. The sample is applied, and the reaction between antibody and bioagent causes two colored lines to appear on the strip, meaning the bioagent has been detected. The appearance of one line, in contrast, means no agent is present.

A sophisticated test, being developed at Lawrence Livermore National Laboratory, uses fluorescent antibodies to bind to bacterial cells. The sample passes through a portable flow cytometer, a common laboratory device that counts cells by measuring their fluorescence or other properties as they move in a liquid. The Livermore cytometer is gated to detect particles that are both fluorescent and of the same size as the bacteria.

While advances in machine learning over the past decade have made significant impacts in applications such as image classification, natural language processing and pattern recognition, scientific endeavors have only just begun to leverage this technology. This is most notable in processing large quantities of data from experiments. 

Research conducted at Lawrence Livermore is the first to apply neural networks to the study of high-intensity short-pulse laser-plasma. Plasma is one of the four fundamental states of matter, along with solid, liquid and gas. It is an ionized gas consisting of positive ions and free electrons. While in most instances of neural networks they are used primarily for studying datasets, in this work the team uses them to explore sparsely sampled parameter space as a surrogate for a full simulation or experiment.

“The work primarily serves as a simple demonstration of how we can use machine learning techniques such as neural networks to augment the tools we already have,” LLNL postdoc Blagoje Djordjević said. “Computationally expensive simulations such as particle-in-cell codes will remain a necessary aspect of our work, but with even a simple network we are able to train a surrogate model that can reliably fill out interesting swaths of phase space.”

Through an engagement with Purdue University’s The Data Mine learning community, Lawrence Livermore and Purdue are partnering to speed up drug design using computational tools under the Accelerating Therapeutic Opportunities in Medicine (ATOM) project.

Over two recent semesters (fall 2020 and spring 2021), LLNL bioinformatics scientist and ATOM researcher Jonathan Allen mentored a cohort of 20 undergraduate and graduate students and two teaching assistants, introducing them to computationally driven drug discovery and designing predictive models for drug candidates.

The students met on a twice-weekly basis — with Allen on one day and with Data Mine Program Director Mark Ward on another — and worked in teams to evaluate virtual compounds for drug-like potential using ATOM-developed open source software.

Scientists at Lawrence Livermore have collaborated with Princeton Plasma Physics Laboratory to design a novel X-ray crystal spectrometer to provide high-resolution measurements of a challenging feature of high energy density matter produced by National Ignition Facility (NIF) experiments.

Laser-produced high energy density plasmas, similar to those found in stars, nuclear explosions and the core of giant planets, may be the most extreme state of matter created on Earth.

Princeton previously built a spectrometer for NIF that was quite successful. The spectrometer, delivered in 2017, provides high-resolution measurements of the temperature and density of NIF extreme plasmas for inertial confinement fusion experiments, and the data obtained was presented in invited talks and peer-reviewed publications.

The instruments measure profiles of key parameters such as the ion and electron temperatures in large volumes of hot plasmas that are magnetically confined in doughnut-shaped tokamak fusion devices to facilitate fusion reactions. By contrast, NIF laser-produced HED plasmas are tiny, point-like substances that require differently designed spectrometers for high-resolution studies.