Pods of Science

From wildfires and hurricanes to drought and rainstorms, atmospheric scientist Ruby Leung explores the climatic underpinnings of extreme weather in this episode. Learn how wildfires in the U.S. are changing, what we can expect in tomorrow's hurricanes, and what Ruby does when she isn't modeling human-Earth system interactions.

Host Nick Hennen sits down with Pacific Northwest National Lab's Ryan McClure. Ryan is involved in a NASA-funded project with PNNL to blast soil laden with bacteria to the International Space Station. The bacteria-infused soil is from Prosser, Wash. Researchers like McClure and Janet Jansson, a laboratory fellow at PNNL and the leader of the study, will look at what the bacteria do in a microgravity environment to learn more about how soil microbial communities function in space. That's the intelligence scientists need to grow food in space or on another celestial body. If you'd like to follow the mission's progress and launch, you can do so here: https://www.nasa.gov/mission_pages/station/structure/launch/spacex.html or https://www.nasa.gov/spacex.  

When wildfire season creates toxic air in your community, the creeping smoke can make its way into your home, creating hazards that aren't always easy to detect. This penetration of smoke can lead to high concentrations of tiny particles indoors when nearby wildfires are extreme. Exposure to this kind of air has been linked to unfortunate health results.   A PNNL research team, made up of Chrissi Antonopoulos, a Senior Analyst focusing on the advancement of energy- and carbon-neutral buildings, and Sam Rosenberg, a Data Research Scientist focusing on residential energy efficiency, indoor air quality, and building codes, studied this phenomenon. Together, they huddled inside and examined the air quality inside a nearly 100-year-old 2,600 square foot single-family dwelling in Portland, Oregon as wildfires raged nearby, creating toxic smoke throughout the city. Using low-cost sensors deployed in a new DOE Building America field study, they discovered some clear benefits in air quality from using portable air cleaners during this high smoke event.    Tune in as host Nick Hennen learns more from special guest, Chrissi Antonopoulos on this episode of SciVIBE.

We meet with PNNL physicist Emily Mace on this episode of SciVIBE to get to know her a bit, learn about her experience working in the Shallow Underground Lab and with highly sensitive radiation detectors—designed and built by Pacific Northwest National Laboratory scientists—to measure argon-39 activity in groundwater samples.

Arun Devaraj, a materials scientist, remains committed to improving the quality and performance of metals. He is in the midst of an ambitious project to explore how hydrogen, combined with stress and oxidation, leads to catastrophic failures of high-strength steels that are widely used in the nuclear and automotive industries. His research will have important implications for carbon-free energy sources and their storage. And his research will unfold at PNNL's Energy Sciences Center, a recently dedicated $90 million facility on the Richland, Washington, campus.

PNNL Earth scientist Brian O'Neill answers listener-submitted questions on climate change on this special, climate-focused episode, which coincides with the release of the latest from the IPCC report (Intergovernmental Panel on Climate Change). Tune in to hear your questions answered! 

Marine renewable energy stands to do a great deal of good. The ocean's wave, current, and tidal energy holds the promise of electricity that can help power the grid, strengthen scientific observations, and bring renewable power to coastal communities. But how do marine animals like sharks and whales coexist with marine energy devices? What are the potential impacts? PNNL oceanographer and senior research scientist Andrea Copping leads research that explores these important questions. Join us today as we dive deep into her findings and discuss not only what the science says, but how the investigations unfold.

Carl Imhoff is one of the nation's top grid experts, recognized for his long history of advancing grid modernization in the region and across the country. Today you'll learn all about this, as well as a little bit about the man behind the science.  Carl's expertise includes Grid Modernization, Smart Grid, Grid Cybersecurity, Transmission Systems, Distribution Systems, Grid Analytics, and Energy Storage. In this episode, Carl details just how vital electricity will be in helping our country meet its national goals -- both in terms of economic vitality, clean energy and addressing climate change challenges. Carl also offers deep insight on the phenomenal changes we've seen in all of these aspects over the last twenty years. Tune in as we talk about what all of this means for the nation's power grid, And moving forward, what is next? 

Pacific Northwest National Laboratory Remembers September 11th, 2001.   Host Nick Hennen interviews with staff at PNNL on Sept. 11, 2001, including Senior Public Affairs Advisor Greg Koller, former lab director and national security director, Mike Kluse and former DHS director Mike Mitchell. It also offers perspective on how National Security work at PNNL continues to have a significant impact on the security of the country with two STEM ambassador visitors to the SciVIBE podcast: Data Scientist, Kate Miller and Team Leader of Technical Security Solutions, Russ Haffner. They discuss Artificial Intelligence, Social Media Misinformation, Disinformation and the threats we face today as a nation, and how they've changed since 9/11.   

The natural world is rich with examples of give and take relationships. Bees pollinate flowers and receive pollen. Clown fish clean anemones, receiving protection in kind. But these relationships, or “systems,” are often more complex than they appear, some offering lessons that could help shape our future. What's at stake in the relationship between leafcutter ants and the fungal gardens they nurture deep in their subterranean burrows? In this episode of SciVIBE, scientist Kristin Burnum-Johnson explains what's up with these underground, invertebrate farmers, and what we stand to learn from studying their system.  

PNNL Chief Scientist and AI expert Svitlana Volkova harnesses open-source data to understand human behavior. She most recently helped organize an NAS workshop that addresses  technologies and methodologies that can be leveraged to inform real time public health decision making about infectious disease outbreaks, epidemics, and pandemics.  You can find information on the NAS virtual workshop here  https://www.nationalacademies.org/our-work/pivotal-interfaces-of-environmental-health-and-infectious-disease-research-to-inform-responses-to-outbreaks-epidemics-and-pandemics-a-workshop   ** note: this podcast was recorded prior to the NAS  workshop   More information on Watch Owl: https://www.pnnl.gov/news-media/pnnl-team-taps-twitter-explore-perspectives-covid-19-response  

A new study suggests that, in a multi-room building, rapid air exchanges can spread the virus rapidly from the source room into other rooms at high concentrations. Particle levels spike in adjacent rooms within 30 minutes and remain elevated for up to 90 minutes.   The findings, published in the journal Building and Environment, come from researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory.   SciVIBE talks to lead author, Leonard Pease and co-author Timothy Salsbury to learn more about this new study.

Computational chemist Samantha Johnson talks to SciVIBE about her life and work at PNNL and the search for combinations to bolster energy future. Johnson is among the PNNL scientists preparing to move into the Energy Sciences Center, the new $90 million, 140,000-square-foot facility that is expected to open in late 2021.

Materials Scientist Curtis Larimer talks about ElastiDry, a PNNL invention that can be applied to PPE in the fight against Covid_19 that emerged as the winner at the recent National Labs Accelerator Pitch Event.  

Year in Review: A look back at three fascinating stories featured in SciVIBE in 2020. PNNL advances the frontiers of knowledge, taking on some of the world’s greatest science and technology challenges. Distinctive strengths in chemistry, Earth sciences, and data analytics are the heart of our science mission, laying a foundation for innovations that improve America’s energy resiliency and enhance our national security. We are a national lab with Pacific Northwest roots and global reach. Whether our researchers are unlocking the mysteries of Earth’s climate, helping modernize the U.S. electric power grid, or safeguarding ports around the world from nuclear smuggling, we accept great challenges for one purpose: to create a world that is safer, cleaner, more prosperous, and more secure.

While trees are known to absorb greenhouse gases such as carbon dioxide, PNNL researchers have found that under certain circumstances, trees actually contribute the more powerful greenhouse gas methane to the atmosphere. It can happen when seawater floods nearby forests; the flooding acts like a switch, causing release of methane from soil and trees. In some cases, enough fuel is bound within a tree’s tissues that it can, when manually released, sustain a flame. Coastal scientist Nick Ward discusses the work he’s presenting at the Earth science conference AGU 2020 and how his colleagues expect the process to become more important as sea levels rise, causing more coastal forests to flood. Check out more on AGU 2020 here: https://www.pnnl.gov/news-media/agu-2020-connect-virtually-pnnl. Here's a link to Nate's work on YouTube: https://www.youtube.com/watch?v=Ft3rZdPIE5s&t=3s

Dust blowing onto high mountains in the western Himalayas is a bigger factor than previously thought in hastening the melting of snow there, researchers show in a study published Oct. 5 in Nature Climate Change. That’s because dust – lots of it in the Himalayas – absorbs sunlight, heating the snow that surrounds it. Qian and Chandan Sarangi, formerly a post-doctoral associate at PNNL and now at the Indian Institute of Technology Madras in India, are corresponding authors of the study. More than 700 million people in southeast Asia, as well as parts of China and India, depend on melting snow in the Himalayas for much of their freshwater needs in summer and early fall, driving the urgency of scientists ferreting out the factors that influence earlier snowmelt in the region. In a study funded by NASA, scientists analyzed some of the most detailed satellite images ever taken of the Himalayas to measure aerosols, elevation, and surface characteristics such as the presence of dust or pollution on snow.

Brent VanDevender, a physicist at the Department of Energy’s Pacific Northwest National Laboratory, hasn’t had this much fun doing science for a while. In this episode, he shares how a chance conversation with a colleague led to a better understanding of the limitations of quantum computing. The study, published in the journal Nature, shows that qubits need to be shielded from natural radiation, like cosmic rays. And the research links the vanishingly small quantum bits to the search for dark matter in space.

Ted Bowyer, a PNNL physicist, has spent much of his career developing new way to track a radioactive gas known as radioxenon. The capability helps scientists track nuclear explosions anywhere on Earth.

Pods of Science | Episode 7 | How to Stay Safe from Biothreats JW: Welcome. I’m your host, Jess Wisse. Today I want to share something a little different with you.Let me introduce you to my friend Nick Hennen. He’ll be co-hosting of Pods of Science for us today. This episode was recorded live at the 2020 AAAS meeting. Take it away, Nick.NH: I’m Nick Hennen, Media Relations Advisor for the Pacific Northwest National Laboratory. And I’m here today with Katrina Waters who represents the Biological Division of our laboratory and Kristin Omberg, representing Chemical and Biosignatures Science at PNNL. Today we’ll talk about how increasing globalization is fueling the spread of novel natural biological threats, and advances in biotechnology that could be used to engineer new threats are constantly emerging. Frameworks for assessing unknown biological agents can enable rapid risk profiling and mitigation. This includes applying novel data analysis methods to host-pathogen interaction data to help predict, at early exposure times, whether a patient can be expected to recover from a disease such as Ebola without major interventions.Please introduce yourself and describe what you do at PNNL and why you do it.KW: Hi. So I’m Katrina Waters. I’m a Biochemist and Laboratory Fellow at PNNL. I manage the basic science organization for biology at the lab and work as a researcher in the area of infectious disease and public health. So the reason that I do it is that I get to work with really awesome people who contribute in a lot of different ways and it’s just been a lot of fun. KO: I’m Kristin Omberg. I am the manager of the Chemical and Biological Signatures Group at PNNL which is in the National Security Directorate. I’m a chemist by training and in 1999 I was doing a post-doc at Los Alamos National Laboratory, which is a sister laboratory. And my post-doc didn’t go very well so I started looking for jobs and I got a couple of offers. One was in accelerator production of Tritium and one was in biothreats—so, looking at preparing a system to detect a biological threat in the future. And I talked to my father who happens to be a Nuclear Engineer at PNNL and he said, “I feel kind of good about that counter terrorism stuff.” So I took the job and I started the job in December of 2000. NH: Oh, wow. That’s wonderful. KO: And since then, the 2001 anthrax attacks on the United States, it’s just been a constant sprint. NH: Thanks, Dad.KO: Yeah, thanks, Dad. NH: Tell us briefly about the nature of biothreats and what does that word mean?KO: That is a really interesting question because the way we use biothreat has actually changed in all of our lifetimes. A lot of people don’t realize that up until 1969, the United States had a bioweapons program. So they weaponized Bacillus anthracis, Yersinia pestis and other human pathogens for use in war. The former Soviet Union also had a biological weapons program and they weaponized many of the same pathogens. So at that time biothreat was used to describe a deliberate act of war by a state program using a weaponized pathogen. In 1975, the Biological Weapons Convention came into force and we became less concerned about a state program, both the Soviet Union and the United States ratified that convention. In the 1990s though, we started becoming concerned about terrorist groups. We started worrying about terrorist groups overseas who started using biological agents, and then we saw that in 2001. So we started being concerned about the biothreat by a state actor, terrorist, or a lone actor. But since 2001, we’ve so many outbreaks of diseases that are zoonotic diseases that jump from animals to humans. We’ve had avian influenza, we’ve seen a couple of rounds of Ebola, SARS, MERS and the current coronavirus. And they’ve all demonstrated that they can really be equally devastating in a globalized world. So, in the last decade we’ve started to worry about emerging disease, as well as the health of other populations, so the populations of animals and plants that share our world. Because the health of those populations also impact human health. And we’ve also become more concerned about lab accidents. If we had an accident and if a pathogen were to escape from a laboratory, it could potentially become disruptive. So, in 2018, the U.S. government released the biodefense strategy and they define biological threats as natural, accidental, or deliberate outbreaks of disease, whether in the human, animal, or plant population. So it’s a much more broad definition that we have today that incorporates natural, accidental events and deliberate events, as well as all the populations of the world. NH: Thank you. So I want to talk about outbreaks. What makes an outbreak happen, anyway? Is there a typical timeline that happens with an outbreak? Do most pretty much die off eventually? Or are they here to stay?KW: Yeah, that’s a really great question. So predicting when an outbreak will emerge, or what will emerge is a really complicated task. There are a lot of different factors that play into that, dozens of factors as we heard at one of the sessions yesterday at AAAS on infectious disease forecasting. Pathogens adapt to their changing environment. So right now we have climate change, we have changes in farming practices, migrations of populations closer to animal species, migrations of species closer to humans. And all of this human to other species, or animal vector contact increases the chances for the emergence of a pathogen to jump from their host species to a new host species. So when an outbreak happens, typically what that means is that not only does a primary human host get infected, but that pathogen changes to become human to human transmissible. So as we’ve seen with the current coronavirus, once that happened it spread pretty rapidly, that human to human transmission. The timelines can really vary. So one of the things we saw in our session yesterday was the Dengue virus outbreaks that happen in Puerto Rico tend to be very seasonal based on weather changes, whereas Ebola outbreaks that came from local animal populations tend to have really irregular frequency but they’ve been increasing in frequency since the 1970s. So I think outbreaks will continue to happen but predicting them is really more of a challenge of us understanding where they come from, what is causing that transmission and then how quickly they die off is influenced by a lot of different factors. Some of those things could be the nature of the pathogen itself, how infectious it is or how lethal it is to the human host. How we as a community or response agencies respond to that in terms of medications available, or vaccines, quarantine efforts. And then finally, the other thing is really is the natural reservoir of the pathogen. Is it existing in a reservoir where people are going to continue to get reinfected no matter what we do? All of those things will influence the timeline for an outbreak. NH: That’s quite complex, isn’t it? Is there anything very different or surprising about the current outbreak compared to these past outbreaks?KO: The thing I’ve been surprised about, and there’s been a nice focus on it at this meeting, is that we’re getting data in a lot faster than what we typically see. AAAS pulled together a nice session at the beginning of the meeting where they discussed, in part, some of the genomic sequence data that’s been collected and disseminated. And they’ve done a nice analysis of what that tells us about animal to human vs human to human transmission. I don’t ever remember having this many sequences available in such a short time following the identification of a disease. It really speaks to how ubiquitous sequencing machines have become. They have sequencers that can actually plug into a laptop and we’re deploying those all over the world. So people can sequence more than they ever could before. The other thing that I’m seeing that’s really remarkable is really the international effort to control the spread has been a lot more proactive and thorough that we’ve seen in the past. I was astonished, although in a good way, for example when Starbucks shut down stores in China. One of the big things that drives person to person transmission is the number of contacts a person has throughout the day. So a place like a Starbucks, a place like a school that's why you often see these public spaces, like a market for example in the current one, that's why you often see these public spaces associated with epidemics. It's because that's where people get together and that's where they end up sharing germs. NH: What’s tricky about identifying patients quickly during an outbreak? Walk us through what typically happens.KW: One of the things that's really tricky about these, about many of these outbreaks, is that the symptoms that people come down with sound a lot like the flu. Or a common cold, right? So even during the Ebola outbreak, some of the initial symptoms were fever, fatigue, body aches, chills, things that would normally be seen during any kind of similar endemic infection that people are accustomed to. And so right now, for example with coronavirus outbreak in the middle flu season, the symptoms are very similar to influenza. And so it's hard for the doctors to really notice, or for even the patients themselves to notice there's something different going on with them from what they would see in a typical flu season. So in the case of a doctor’s office or emergency room, really what's going to happen is somebody's going to walk in, they're going to say I have these symptoms, I have breathing problems, and the doctors are really going to be looking at the symptoms to try to determine what is there, but it could take hours if not days for the results of the tests that they need to find out if they're infected with something, if they have asthma, do they have COPD? Do they have pneumonia? What is it really that you're trying to treat? And many times what happens is that in the middle of the identification process, they're still focusing on getting that patient to breathe. And that will often include the administration of bronchodilators, steroids, or in really severe cases, putting people on a ventilator. And all of those treatments can drive the viral infection much deeper into the lung and make it worse. And so really, the need is for rapid diagnosis that can provide clues to physicians about how to treat a patient based on their physiology and the symptoms. While in the process we're trying to identify what caused it, but really be able to treat them appropriately right out of the gate.NH: I want to talk about biological threats. Tell me a little bit, about I definitely want to talk about biological agents. Can you tell me a bit about how are emerging biological agents or potential threats identified today?KO: In the strict deliberate threat space, we identify them based on a list that currently stands at 67 agents that were defined back in 1997 originally as a list of things that we wanted to keep out of the hands of terrorists or lone actors.NH: So 67?KO: 67 today. It was actually 47 when they originally defined it, but there have been diseases that have been added to it like pandemic influenza. So we have that list of things and in many cases we identify it through either an environmental sample if we can get a sample of it or a clinical case. And the clinical cases are the same problem that Katrina described. I actually had a friend when I lived in New Mexico who got plague. He got it golfing. Plague is endemic in the southwest part of the United States. When he first came in, plague manifests as a respiratory problem, so he came in and they thought he had a bad cold. It wasn't flu season fortunately, but they sent him home to rest. And it progressively got worse, so then they started testing him for other respiratory pathogens. But it took about two weeks before they got around to using the tests for plague because plague looks a lot like a lot of other respiratory diseases. So in many cases what we do is we try our diagnostics to see if we can find something in a clinical case. If we can't identify what it is based on things we've already identified, we can do DNA sequencing and that is one of the things we're seeing coming out of the current outbreak is a lot of sequences of this corona virus. If we have sequences available we can also do something called real-time polymerase chain reaction or RT-PCR, which is a DNA matching technique. Unfortunately, whether you're sequencing or whether you're doing RT-PCR, both of those techniques rely on matching the nucleic acid sequence to a known database of sequences. So we are really primarily looking for things that we've already seen before. When we have something that hasn't been seen before, if it hasn't been entered into the database, we can say that it's close to something in many cases, but we can't say exactly what it is. And even when you can't identify that sequence the nucleic acid tells you what the microbe is capable of doing. But it doesn't always tell you what it is doing.Microbes adapt to their environment. I like to actually think of them like my cats. My cats have a lot of functions they could do, but they choose not to because they don't have to. And so microbes adapt and they use the functions that they need to survive and they turn off the ones that they don't. So when you have a sequence, it may not tell you, for example what antibiotics you would use to treat something, it won't tell you often times how lethal it might be. It only will have limited applicability to determining person-to-person transmissibility and usually you just tell that by how quickly it mutates. So for example, with the current outbreak of corona virus the first DNA sequences didn't tell us if it was transmitting from human to human. It was only when we got enough clinical cases and enough sequences that we were able to compare the similarity and say it was human to human transmission. And that's true of all new diseases. And in 2020 we have all these data sharing techniques, we have a lot more data gathering techniques but in a lot of cases we still just wait and watch a disease play out in a human population. NH: What are some of the tools you're using to address these challenges?KW: So one of the things we're trying to do at the Pacific Northwest National Laboratory is to focus more on the host response and the actual biological response of the pathogen when it gets into a human host. And we can use the gene sequencing and we can use the transcribed RNA that comes out of those systems to study them. But we're also focusing at PNNL on the development of advanced mass spectrometry measurement approaches so that we can look at the proteins and the small molecules and the lipids within a biological system and see how are they functionally responding, these pathogens in their new environment and how is the host responding to that pathogen so that we can get a better sense of what is the severity of that infection. What does the disease actually look like physiologically within the human? And even give us clues for how to treat that. If we know that there's a specific kind of metabolic shutdown is there a drug, or a specific kind of treatment that can focus on the treatment of that while we went through the process that Kristin just described for identifying, developing a vaccine—figuring out what antibiotics or antivirals might be effective—we can really focus more on that treatment of the physiological condition of the human.NH: That's great. Are such measurements brand new? Or have they been used before?KW: So they have been used before and at PNNL we've been developing these technologies for several decades, but it's really only been in the recent history that they've become more and more sensitive. Now we can identify more molecules in smaller samples with greater quantitative precision to know what is there, how much of it is there, and whether it's really specific for one condition or another. And so in addition to the mass spectrometry for the identification, we also are developing a lot of computational approaches to make sense of all of that data so that we know with what precision we've done the identification and made that quantitative measurement. And so really it's the advances in the past few years that have given us the ability to get much better precision. And one of the things we talked about at our AAAS session on Friday was how we've applied this to the Ebola outbreak from 2014 and we could identify a set of biomolecules that were really indicative of the survival of patients that came down with the infections. And we've applied similar approaches to study cancer from military populations to get a sense of early cancer diagnosis and treatment, as well as to study the human microbiome and its influence on human health. NH: Is this as simple as measuring one or two things in a patient's blood?KW: That would be great, but it's really unlikely. So the reality is that there are hundreds of factors that contribute to disease and they might vary from person to person. So the genetic makeup of our population is so diverse that our individual susceptibilities to things is very different, and how they express physiologically. So we're working to get the needed measurements down to just a handful so that it could be used realistically in a clinical diagnostic. And at the same time, allow for an accurate prediction of somebody's risk or how you would want to treat them. And one of the really key tools that we have that we're applying to this is machine learning. So machine learning really helps us apply that to the big data problem, the complexity of the data that we collect and the huge amounts of data that we accumulate to use machine learning to help us identify those factors that are the most predictive in combination to be used in a clinical assay.NH: And what would you say is the biggest need right now in our response to an emerging bio threat? KO: I think there are two needs right now and one is an immediate need and one's a longer-term need. Right now, we need clear information and we need it as early as possible. That was one of the subjects that was brought up at the AAAS meeting in the session on coronavirus was the difficulty of getting clear, high-quality information out. Particularly when there is a lot of information coming out that is sensationalist, or only partially true. But in the longer run, I think what we really need is better science. And we're working on that science but it's going to take a little while to get there.What we really need is we need to apply the science techniques that we have developed over the last ten years, like the ones that Katrina talked about, like artificial intelligence, to try to figure out if we can do something while we're waiting around to identify a disease. So instead of asking, as was the case with my friend with plague, what does he have and then how will we treat it can we really get the right treatment first while we're figuring out what someone has. So can we understand what's going on with the host? Can we understand more about the pathogen without having to be able to match the DNA sequence? I think we can. I think it's not very far away right now. I think we couldn't have done it back in the 1970s when we stopped our biological weapons programs, but I think that we have the science now that in the next couple of decades we will be able to really be proactive in treating the disease as well as identifying it.NH: It's exciting. What can people listening to this podcast right now do to protect themselves from bio threats? I mean or do they even need to protect themselves? Should they be concerned? KW: So I think for the general population they don't need to be concerned about bio threats from a deliberate release. But when we think about these natural emerging agents that will continue to come up, the single most important thing people can do on a day-to-day basis is washing their hands. It is the most effective way of preventing people catching, as well as spreading, infections of any kind. The second thing people can do is get vaccines when they are available. People have forgotten that at the turn of the century 6,000 people died every year from measles. And vaccines really are a miracle of modern medicine as they’ve been applied, and childhood mortality rates have dropped from greater than 20% when before childhood vaccines were available in the 60s, to less than 5% today. And deaths attributed to childhood diseases have dropped by 99%. And so what people can do is get those vaccines that are available, including the influenza vaccine because even if they get the strains wrong it will often result in a less severe infection for the people who've had the vaccine. And particularly for the young, or for the older and compromised immune systems, getting influenza can be very, very serious or deadly. And so getting the vaccines where available is very important. KO: I think there's an interesting risk perception issue when we have something spectacular in the news, and there's an interesting risk perception for issue with the biological threat. While I personally believe that those are all very important, I believe that we lose perspective on the fact that between thirty and forty thousand people every single year die of flu in the United States. I was very proud of that recently, I tell my daughter that every single year when I'm trying to convince her that the flu vaccine is worth it and when some of her friends got upset the other day about coronavirus in her class my daughter's spouted up with, “Every year 30 to 40 thousand people in the U.S. die from the flu!”NH: She remembered it! KO: So proud. But we tend to think that these things or measles because they're more commonplace are not as serious, but they are. And in many cases the things that we use to protect ourselves against influenza, like handwashing, and really good sanitation, and hygiene practices. And I always tell my family to make sure you get a good night's sleep and you're eating your vegetables and you have a generally good health system.NH: Yeah and that's definitely an important thing to take care of your body, to eat right, wash your hands. It’s a defense against everything. Right well this has been really interesting. Thank you both so much for being a part of Pods of Science!KW: You're very welcome.MusicJW:Thanks for listening to Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening. 

Welcome. I’m your host, Jess Wisse. On today’s episode we’ll talking about how scientists are taking a new approach to better understand and fight cancer. Stay tuned to learn more.   JW: New research published in Cellshows a never-before look the steps that happen when a woman develops endometrial cancer. This type of cancer affects the uterine lining and it can be deadly. PNNL researchers are using their expertise in mass spectrometry and cancer biology to better target this disease. Meet one of them: Karin RodlandKR: I’m Karin Rodland. I am a PNNL laboratory fellow and I'm one of the lead cancer biologists at PNNL. The main thing that I do is provide expertise about cancer biology to the mass spectrometry group that does proteomics and metabolomics measurements of lots of different tumors.As I got into my 30s and I started really doing this, it's like got my PhD and I thought “Where am I going to postdoc? And what am I going to do in my own research lab?” The number of people I knew who were friends who had cancer—it was just mind-boggling. And I would go to Lions Clubs and Rotary Clubs to do this kind of lay outreach and I would start by saying, “If you or someone you know has had cancer raise your hand.” And every single arm in the room would go up. That's why I do this.JW: Karin has studied cancer biology since the 80s and she’s one of the top experts in the field. KR: KR: I ended up at Oregon Health & Science University as an assistant professor in the mid 80s and I was there for 17 years. And I earned the right to go on sabbatical and I came to Pacific Northwest National Lab to learn proteomics because they were the world's best at proteomics and I thought I was going to need that technology for my research. And I came on sabbatical for a year and I really enjoyed the team research philosophy and culture that they do at PNNL. I was totally impressed with the mass spectrometry technologies and the computational biology expertise and I saw a great opportunity to apply all these capabilities to biomedical research and particularly to cancer.JW: For years doctors and scientists have known that cancer is a genetic disease. Our genes control much of what happens in our bodies, including the way our cells function, grow, and divide. Because of this, cancer researchers have spent a lot of time studying the DNA and RNA of cancer cells. But Karin and her team looked one step closer. They studied the proteins synthesized by these cells. KR: What we call the central dogma of molecular biology, is you have the genes and they're the blueprint. And they send out kind of a Xerox copy and that's the messenger RNA. And then the message gets made into proteins and the proteins actually do the work. And the way that they do the work is by being modified with phosphorylation which turns them on and off, or by acetylation which opens them up or closes them down. So it's easy to measure DNA and it's easy to measure RNA. The technology has been very well developed, it's inexpensive, and it's easy. So scientists and doctors measure what is easy and convenient to measure. So we can measure genes and that's the only tool that most doctors have for doing precision medicine. It's the genes and maybe they can do the RNA. And so all these models have been built up trying to predict disease outcome based on the genes and the RNA. What we found is that when you add the proteins you get much, much more information. And sometimes the information from the RNA is a little bit misleading. It's a little bit different than the information that you get from the proteins, but if we correlate the proteins with the known clinical features we find that the protein modifications are more powerful in this study.JW: In this most recent study, Karin and her team studied nearly 150 uterine tissue samples. And they did so by using a tool called a mass spectrometer. This is an incredibly sensitive instrument that can measure the smallest parts of a sample. Using the mass spectrometer, the team took many different types of measurements—so many, that they actually took more than 12 million measurements – the most ever taken of proteins for cancer research. They tried to measure everything that they possibly could. KR: For cancer research, the type of research that we do at PNNL with our great mass spec is discovery research. We're not trying to test the hypothesis. We're trying to study what it is and describe it in in great detail. And then we hand that information off to the basic scientists at OHSU say, and they tease out parts of it and they do a very specific experiment to see what the relationship is. And so that's how the science grows and grows and grows. JW: This type of research was only possible with an amazing team of collaborators. Like much of the research done at PNNL, this research was done by a multidisciplinary team where each team member is an expert in something unique. KR: We have a great team at PNNL Tao Lu runs the mass spec and he runs it very well. He knows how to design experiments to make things work on the mass spec. And he just knows how to get people to work together and work well in everything. There's a very large mass spec team that's very great. There's Paul Piehowski who works on the sample processing. And Marina Gritsenko who solves the problems in sample processing. She's a very prominent author on this paper because doing the sample processing was so important. There's Ron Moore who keeps the instruments running well. Then there are the people who help us interpret the data. Jason McDermott and his team, they take all that mass spec data and they start to make sense out of it and build the pieces of the mass spec machinery. Sam Payne is on our team he was at PNNL he's now at Brigham Young University. Bing Zhang is the lead of the Baylor team and he's been a collaborator with us for ten years. And then the consortium, the CPTAC consortium has brought together a number of high-power labs that do nothing but genomic analysis. PNNL is not as strong in genomic analysis as we are in proteomic analysis, so we've been teaming with the folks at Washington University in the lab of Li Ding and the folks at New York University in the lab of David Fenyo.JW: Obviously, it’s bad to get cancer. But there are different types of cancer. And the type of cancer a patient has will determine how aggressively is spreads. Karin describes the differences as “bad actors” vs “good actors.” By doing their in-depth protein analysis, her team can now better identify if a cancer is a bad actor or good actor.KR: When somebody has a tumor the first things that the doctor does is to sample the tumor by a biopsy or removing the tumor if it's small and localized. And then you give it to a specialized kind of doctor called a pathologist who looks at it under the microscope. And for over a hundred years we have a lot of observational data about if the tumor looks like this it's going to behave bad. If it looks like this it's likely to behave well, but we don't know why. We just know that there's an association between what it looks like and how it behaves. And then there are tumors that we know behave badly when they look like they should be tame tumors. Okay, so there's a type of appearance that we call serous endometrial cancer and it's a bad actor because it doesn't look like a well-developed uterus. And there's a type of cancer that we call endometrioid endometrial cancer and it's a good actor. It's pretty much doing what it's supposed to do—it’s just growing faster than it's supposed to and you can whip it into shape pretty easily. But there's a small percentage of those endometrioid endometrial cancers that become bad actors and that metastasize and kill the woman. And you can't tell it by looking under the microscope. And you can't really tell it by looking at the DNA. And so what we found was the protein behaviors in those bad actors that look like the proteins in the serous type that we know are going to be bad actors. So, we can look for the common features that define a tumor that's going to be aggressive and nasty and a bad actor. So not only does that allow us to make a prediction about, you know, “You can rest assured you can be comfortable the surgery is going to cure you.” But then if you're not in that nice reassuring category, we can start to do a better job of attacking the problem, of developing targeted therapeutics that are going to attack precisely what is broken in those tumors that are the bad actors. No matter what they look like, it's whether proteins are good or bad.JW: Not only did the team find protein data to be so rich, they were able to use this information to learn more about immune cells. Tumors attract immune cells. They are a big part of the problem when it comes to the spread of cancer because they can trick the body into thinking tumor cells aren’t dangerous. Think of an intruder wearing a disguise to mask their true identity. KR: When we look at the tumor, we're not just looking at the tumor cells themselves. We're also looking at the immune cells that have been attracted to the tumor. And immune cells, their job is to kill anything that's foreign. And a tumor cell is a foreign cell—it has changed and mutated. So, it should look foreign to your immune system and the immune system should attack it and kill it. But many tumors make immunosuppressive molecules that tone down the immune system. So we can actually measure how much of the immunosuppressive nature is there. So one of the hottest therapies in cancer these days is immunotherapy where we stimulate the immune cells to kill the cancer cell. We remove the suppressive factors and we stimulate the aggressive factors and they kill the tumor cells. So with the proteins we can identify how much tumor suppression is there and whether the immunotherapy will work. But even when we stimulate the tumor cells, the immune cells to be active—they have to have certain machinery that allows them to actually reach out and touch the tumor cell and recognize that it's a tumor cell. And so we can also tell whether the tumor cells that are there have enough of this machinery to actually do their job. So this is going to help us determine whether immunotherapy will work for that patient or not. Because immunotherapy right now is only working in 40 to 60 percent of people. We don't know why it works in some and not in others, but when it does work it's practically a cure. When it doesn't work it can also make you very sick it can stimulate your immune system to attack your healthy cells. So we don't want to tune up your immune system if it's not going to work against the cancer. So this allows us to be more precise in how we use immunotherapy. JW: There’s one huge benefit to learning more about the proteins of immune cells. With this knowledge, doctors might be able to spare patients unnecessary side effects. KR: Well most immune therapies make you feel like if you've had the worst flu you've ever had. The early days of immunotherapy used the same molecule that your immune cells make when you have the flu. And patients that I've worked with said we don't want you to research immunotherapy because it makes you feel really horrible and it's not working often enough to be worthwhile. So, we had to understand the biology enough that we could make immunotherapy successful and that we could also use different strategies that didn't make you quite so sick. Almost everybody who gets immunotherapy feels like they have a really crappy flu. But you know, I'll go through the flu if it will cure my cancer. Some people get what's called an immune storm and the whole immune system just flares up like a thunderstorm and it can attack the heart muscle and that's obviously very dangerous. JW: But Karin didn’t just find a better way to do immunotherapy. She and her team almost accidentally discovered something that could be a live-safer for patients in the future. With the protein data they were able to identify an alternative use for a pre-existing, FDA approved drug. This other use? Cancer treatment.KR: Going back to the genome data that was available: So we had a p53 mutant cancer. A lot of endometrial cancers have a mutation or a fault in the p53 gene. That is a gene that normally suppresses growth. So it's what we call a tumor suppressor, so if it's broken it doesn't work—the break is off and the cells grow. But there's no drug that treats p53. Because it does so many different things. It's just difficult to drug. So by using the studying the protein data instead of just the gene mutation we can see the proteins downstream of p53 that are activated. Their activity is increased when p53 is broken. Okay, so it's like a Rube Goldberg machine, you know. And if you drop the ball into the bucket the chute kicks the mouse. And so if we can't stop the ball from dropping in the bucket, maybe we can inhibit the shoe from kicking the mouse. So by doing the proteins, we can outline the whole Rube Goldberg machine. And so in this case we identified that a protein downstream of the p53 that was activated when p53 is broken is called cyclin dependent kinase 12. And there is a drug out for that that has been approved by the FDA. Now without our data you would never have thought of using that drug and endometrial cancer, but now that we can see the whole Rube Goldberg machine we can see that maybe the drug against cdk 12 will work in endometrial cancer.JW: This is exciting because this means that a clinical trial could begin soon. All of this is because of the advanced protein measurements done at PNNL.KR: To me the big advantage of doing the protein measurements and the phosphoprotein measurements is that we're actually able to track the flow of information in a cancer cell from the external environment that is supporting the growth of the tumor cell to the DNA in the nucleus. So that we're making more cancer cells, and more cancer cells, and more cancer cells so that whole pathway of information is very important. We can't get that from the gene mutations. We can only get that for measuring the proteins and the phosphoproteins so that's the big takeaway. The second big takeaway is that when you add in information about the phosphorylated proteins it really tells you not only what roads are there, but which roads have the most cars, which are having the most traffic, which is really driving the disease. And that's the information that you need to have to do the targeted therapies that people are working. JW: Now with improved insight into what the proteins are doing, how they are behaving, and changing over time, patients can receive life-saving medicine, that’s tailored to them, before it’s too late. KR: Nothing happens overnight, but you know we licked infectious diseases. Maybe we can lick cancer. You know as Brian Druker says, “We want cancer to be something you die with, not of.” JW: A big thanks to Karin and other researchers like her who are a part of the Precision Medicine Innovation Collaboratory led by PNNL and Oregon Health State University. And with that I’ll let Karin wrap up our latest episode of Pods of Science:KR: And we’re done! MusicJW:Thanks for listening to Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening.

Intro: Welcome. I’m your host, Jess Wisse. On today’s episode we’ll talking about how something that may surprise you. Here are a few hints: the holidays, online shopping, and parking. Stay tuned to learn more.   JW: If you’re anything like me, I’m sure you’ve been busy preparing for the holidays. This includes wrapping gifts, RSVPing to parties, and online shopping. LOTS of online shopping. But have you ever considered all of the hands who’ve touched your latest shipment before it arrives on your doorstep? PNNL user experience scientist, Lyndsey Franklin thinks about this daily.LF: Maybe I should set out, you know, Gatorade and some snack bars. No, forget giving milk and cookies to Santa. Give Gatorade and snack bars to your poor delivery drivers because they are hustling. It's insane!JW: Researchers at Pacific Northwest National Laboratory are using their expertise in artificial intelligence, machine learning, and app development to ease challenges with urban freight delivery, an experience especially difficult during the holidays.Meet one of the researchers at PNNL working on this. LF: I am a User Experience Research Scientist in the Visual Analytics Group at PNNL. I try to make computers better playmates for people. I kind of take the philosophy that if something goes wrong, it's the computers fault. It wasn't designed well. Or people didn't think about it well, and it's really not the person's fault. Somebody needs to make the machine play better.JW: Lyndsey is working on a project that’s funded by DOE’s Office of Energy Efficiency and Renewable Energy’s Vehicle Technologies Office. The project is led by the University of Washington’s Urban Freight Lab. Lyndsey’s goal: to develop, test, and improve technologies aimed at cutting time spent by delivery drivers at the curb. Think of your average UPS or FedEx delivery driver. Lyndsey’s job is to increase their productivity and reduce the time and fuel spent searching for available parking.LF: What if you could make something like parking in downtown Seattle smarter? The particular problem that they were trying to address was, ‘How do we make that delivery process more fuel efficient in crazy environments like Seattle?’ So, they came to us in two capacities. They were looking for some expertise in the modeling aspect. John Feo here at PNNL is leading up that part of the of the effort. The other thing that was important to them, and very insightful of them, was to realize that this wasn't going to be something they were designing for a typical desktop environment. This this wasn't build a model, have it run on a big heavy machine, and spit out an answer, and give it to drivers. Downtown Seattle and the parking in downtown Seattle is ever-changing and always chaotic. And so, there's this noisy busy environment and you're supposed to be giving information to drivers who aren't sitting at a computer. So, what is that whole experience that the drivers are going to have. What's that going to look like?JW: Finding parking can be a major headache for freight delivery drivers. Especially in cities like Seattle. Restaurants need a constant cycle of fresh produce. Retail stores depend on delivered products to maintain a steady flow of sales. People living in a city’s apartment buildings expect their Amazon purchases delivered on time, without fail. That’s why PNNL is working with the University of Washington to develop an app that helps drivers identify open parking closest to a delivery location. The Urban Freight Lab calls this sweet spot for a delivery the “final 50 feet”—where a delivery driver stops to deliver their freight.LF: So, the focus of the app is trying to help increase awareness for when parking might be available. In the case of newer drivers who maybe are seasonal, they've been added to routes to deliver Christmas packages and things like that. They're not as familiar with the area. They don't really have that internal map in their head of: ‘Well, if I can't park here, I can park here. Or I could park in the super-secret spot that, you know I discovered by accident one day.’ They just don't have that list in their head. So hopefully what the app can do then is bring more of an awareness of well, this is what's available to you.JW: But Lyndsey isn’t only trying to improve Seattle’s parking situation. Her primary objective is to create a tool that would actually help delivery drivers. Sounds simple enough, right? But to create an app that’s actually useful, an app developer must first understand the people the app will help.LF: The part that I get most passionate about is, you know, being able to help people realize that: no, you should question that. If you have to jump through all these crazy hoops to get your job done, there's somebody in that chain who built those tools that didn't finish the job. There’s more to be done. You don't have to accept that. You know technology is this weird unfriendly thing that's just kind of foisted upon you and like you're told, ‘Here. This is the tool you get to use. Use it or, I don't know, do it by hand on paper or something.’ So, I like seeing when people realize that, ‘Oh, hey, I am the master of this strange beast we call technology and it's supposed to work for me!’ So, I'm the people pleaser by heart I think.JW: She needed to walk a mile (or two) in their shoes, climb a few flights of stairs, and drive all  around downtown Seattle for a day. How did she do it? She went undercover.LF: I got to actually you know dress up like a UPS driver, hang out in the truck with them and that experience was just wild. So the user experience I have it is once you have the information, and assuming you have data and a model, how do you present that to a driver in a way that's going to actually help them and, you know, not interfere with a job that's already crazy busy? JW: We asked Lyndsey if she was surprised by anything during her ride along. Her response? LF: So much! So much surprised us! We did two sets of ride-alongs. One was with a produce delivery company and the other was with UPS. So everybody's pretty familiar with what UPS does, but I think maybe they are not as familiar with the absolute speed at which these drivers operate. They are flying. I cannot begin to describe just how fast they are sprinting from building to building particularly in Seattle.JW: And the most surprising part actually had very little to do with delivery routes. LF: For me one, of the surprising things was just how much building navigation there was, and some of it was really non-intuitive. I mean he would he would look at a box, read a label, and be like, ‘Okay, well, this is supposed to go to this this business. And you know, the address is this particular suite.’ But just based on having done this route for, you know, seven/eight years think he had been delivering. And so, one of the surprising things for me was, if you are a new driver in some of these environments you can't actually trust the label because they're technically correct, but they're also very wrong. And so for a new driver some, of the difficulty comes in just knowing where am I actually supposed to hand this box off to? These buildings that they're delivering to are just—they’re mazes! And that's on top of the parking issue. I mean there's not a lot of parking, there's construction happening everywhere, you have inconsiderate personal vehicles who will, you know, park because, ‘Oh they're just going to be in there for five minutes. And, you know, why shouldn't they get to use that parking space too?’ But it's like no, no, you've got people who really need those commercial loading zones. Stay out of the commercial loading zones!JW: Another thing that surprised Lyndsey? The people side of the equation. It can sometimes be easy to forget that an actual human is responsible for delivering your packages. LF: The produce folks had their own set of quirks. That one was amazing! They started at like three four o'clock in the morning to deliver to all of these commercial kitchens. And the gentleman that I was riding with had been on that particular route long enough. He knew everyone's name from, you know, the back door to the front door. We'd be delivering produce and he'd be asking about how somebody's mother enjoyed her vacation to Puerto Rico and they're having, you know, full friendly conversations because they just they see each other day in day out. There was an amazing amount of interpersonal skill required for these jobs that I'm sure most people probably don't even think about. But they are expert problem solvers. They are expert navigators, and they are sprinting all day.You know I run marathons. I consider myself to be in semi-decent shape, and I was wishing I had brought like water bottles and Gatorade to keep up with some of these folks because not only are they just kind of sprinting around up and down stairs (because elevators take too long), but they're carrying lots of packages while they do it. So, it's just—this it's not a job for the faint of heart.JW:  After getting a glimpse into what these drivers are up against on a daily basis, Lyndsey was inspired. Even more than before, she wanted to create an app that could truly be useful to delivery drivers. The invaluable insights she gained from her ride alongs opened her eyes to just how powerful this app could be. LF: Inspiration is probably a good word for it - because once you've done that process of actually kind of embedding yourself with them (I mean, like I said for UPS we had to actually wear the uniform) it's a very different job from the one that I have and there is no substitute for actually experiencing it like that like we did with the ride along. So you come away from that going, ‘oh, you know, every expectation I had about what this thing might look like what it might do—toss those aside.’ JW: It was only after her ride alongs that Lindsey could envision a practical design for the app. She quickly learned that drivers wouldn’t have time to enter information into the app, an assumption she held before her ride alongs. And so, she adapted. LF: They're not going to have time to stop and input, you know, what their next stop and their route is going to be. We had originally had some thoughts of well, maybe we'll recommend some parking spaces based on, you know, what their next stop is going to be—give them like a top three. They don't have time for that. Even something as simple as, you know, move to the next stop on my manifest because sometimes there is no parking available. And so rather than sitting and waiting they'll just skip and go to the next stop and come back to it later. And so, the inspiration that we kind of started tapping into is: this isn't going to be like getting directions from Google Maps. It's more video game-like, in that you you've got kind of this, you know, the whole world is at play at once and you've got a player who is trying to, you know, make as many stops as they can in a short amount of time as possible. So, how do you lay out that information so that they can start to optimize in their own heads? With this additional information, and see what's where's my biggest value going to be? Because they just they don't have time to input. It's much more real time. You know, they are really working that hard. So, think less Google Maps, more like a video game. But it's the it's the sort of inspiration that you only get from experiencing it. JW: Lyndsey also quickly learned that these drivers aren’t just master navigators. They are also excellent negotiators and teammates. All for the sake of getting the right stuff to the right people at the right time. LF: There are, you know, chefs in the Seattle area who are very particular about their produce. So now you got to find parking. Now you got to carry packages around. And now you may have a chef or two in the mix who is unhappy with the quality of their mushrooms. And so the gentleman that I was riding with, like I said, there was a tremendous amount of interpersonal skill. He actually knew when certain stalls in Pike Place Market were open, when staff were likely to be there. But well before the market was open, he even had strategies for where he could go to find the same item and a high enough quality that he could come back to this kitchen so that they can get up and running at four o'clock in the morning so that they can start serving customers. And, you know, I don't want to call it like an unofficial barter system, but there was there was a tremendous amount of negotiation that's just once they get out of there their vehicle. There's actually a surprising amount of camaraderie between drivers even of other companies and so what you find is the drivers actually start cooperating with each other to help protect the parking Our UPS driver would pull out into traffic in a certain way that it would block traffic so that this FedEx truck could come in and take the parking space right after him.JW: The app that will help reduce double parking, blocked traffic, and parking fines takes all of this information, and more, into account. But for now, Lyndsey and her team are taking the time to focus on the prototype’s backend to make it as fast as possible. LF: Not only do we have to present a lot of information, but it has to be fast. It really has to be fast. And so, we're taking the time our main developer Amelia is doing an amazing job of very carefully deciding what the best services are for uploading and shifting data around in this platform. People look at this this mobile app that just appears on their device and they think all the all of the hard work goes into what you see. And really what you see is a dumb pretty window on top of some really sophisticated algorithms and technology in the backend. So right now we're kind of working on making sure that back-end is feature complete and fast. And It may not look like much at the moment. That'll come later because the hard work is in just supporting that pretty dumb window in the front end.JW: So, while the app is still currently in the prototype phase, the Urban Freight Lab & PNNL have big plans for its future. Including installing sensors in an eight-block study area in downtown Seattle. These sensors will collect data about parking spots and occupancy. PNNL will process and analyze the data and then feed it to the algorithmic model. Historical data like truck size, type of delivery, and how long a vehicle stays in a location, combined with real-time data from the sensors will allow scientists to ‘train’ the model. All of this information combined will allow the app to tell delivery drivers when there’s a high probability that a parking space will open up. LF: One of the things that we would like to do in the future then, is start understanding when drivers park in a particular space how many times do they go in and out of their truck? Is this a stop where people are likely to stay a really long time? So, there are actually some more delivery-specific behaviors that we would like to maybe think about how do we how do we fit that in?JW: The need for packages to arrive quickly and reliably is only increasing; especially when we’re getting more and more accustomed to ordering everything online. That’s why PNNL is excited to do research like this. Research that will researchers that will increase efficiencies and reduce fuel consumption. Research that will make people’s lives less stressful. Research that will make people happier. LF: Hopefully we can make it less stressful for them to be, you know, bringing us the things that we've all gotten so spoiled that I can, you know, order with one click on Amazon and it's supposed to be here the next day. And if it's not, that's tremendously inconvenient to me but you know that's actually somebody's job who's hustling, and if we can make it so that, you know, not such an onerous grind to them then everybody's happy.JW: And with that we end this episode by wishing you a Happy Holidays, happy online shopping, and please, please do not forget to thank your local delivery drivers this holiday season.MusicJW: Thanks for listening to Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening. 

Pods of Science | Episode 4 | How to Predict Your Next Doctor’s Appointment Intro:Welcome. I’m your host, Jess Wisse. On today’s episode we’ll talking about how artificial intelligence could take your doctor’s care to the next level. Stay tuned to learn more.   MusicJW: PNNL scientists have found a way to improve the accuracy of patient diagnosis by up to 20 percent! How? By using artificial intelligence. A PNNL project, called DeepCare, looked at ways to use AI to improve medical outcomes for patients. Meet the project lead, Robert Rallo. RR: I joined the lab three years ago, coming from Barcelona. My background is chemistry, but I was a professor in computer science for more than 20 years before joining the lab. My main area of expertise is machine learning and applications of machine learning in different areas, one of them being computational toxicology. The team working on this is different computer science scientists from PNNL, Khushbu Agarwal and Sutanay Choudhury. They are two computer scientists in the data sciences group at PNNL. We have strong collaborations also with the University Virginia Tech and Stanford and some of the students who have been summer students here at PNNL have been involved also in this type of biomedical work.JW: We asked Robert why he got into the field of computer science. And here’s what he had to say:RR: The fact that a computer is able to learn by itself from data is something that is really interesting for me, really intriguing, and what triggered my interest in this for me.JW: Robert and his team at PNNL created a new embedding approach. The approach seeks to capture and re-create the types of connections physicians do naturally, in their heads, when they apply a lifetime of learning and knowledge to the patient standing before them in the exam room.What’s embedding? Basically, it’s translation for computers. Using embeddings, computer scientists can take a piece of information that only humans can understand and then transform it into something a computer can use. RR: Medical concepts is, for instance, when you have a specific diagnose this is a concept. You have fever, you have high blood pressure; these are concepts. And then, the way in which a machine learning algorithm or a computer can process these concepts requires them to be codified in a certain numerical way. So one of the ways in which we are making this coding is by developing a continuous numeric representation of these concepts that somehow captures the similarities, the relationships between each one of these individual concepts. So this idea of somehow transforming this textual set of concepts information into a representation which is suitable for machine learning is the embedding process. And what we want is that, this numeric representation will convey the same semantics, the same information, than the original concepts.JW: One of the hardest parts about using AI in the medical field is the inability to combine multiple types of data. Think of all the information that’s captured when you go to the doctor. Now think of all the different forms it comes in. Computer-friendly data like blood work numbers or diagnosis codes are easier than unstructured data like chart notes or images from X-rays and MRIs.RR: Well everybody knows that it’s a known fact that understanding hand-written doctors’ notes is like impossible. (laughing) And I say this because my sister is a medical doctor. But no, I'm joking now. But essentially if we are looking at different types of information, you have structural information in which everything is well classified, well cataloged, and it’s very easy to use. And then you have all this unstructured information in which you have maybe recordings of the patients in an interview for something related to mental health. You can have the notesof doctor that can be written in different narrative styles. You can have different types of imaging data from x-rays to MRI. And each one of these modalities of data is complex and hasits own complexities. And again, the challenge is understanding being able to process in the proper way all this data. But the important thing is finding the ways on how we can combine all this information in the proper way to develop our models.JW: OK. Doctors have been known to have less than ideal handwriting. But deciphering handwritten notes is not the end goal for computer scientists like Robert. The goal is to create models that can take multiple pieces of data, in many forms, and make connections between them. The models can even detect connections that a doctor may not consider.   RR: What we are trying to do, is we try to go beyond these single concepts and capture these in the context of binning. We have different concepts, different entities, and the relationship between all of them. And this representation is richer than the single concept because it contains the relationships between all the concepts that produce something. And the way in which we are extracting this from the clinical notes is by using natural language processing techniques and by identifying what are the elements in these clinical notes that correspond to specific concepts, and while which have the elements in these clinical notes that corresponds to a specific relationship. So, for instance, if in the clinical note we say we say that the patient has fever and I'm diagnosing six milligrams of Advil, whatever, for this patient. So, we are extracting all this information and saying, you know, this is one of the diagnostics for this patient, and we were we are administering this drug, we are giving this dose of drug, and all this process together is a medication event for a specific symptom. JW: All of this work is to create one thing: a knowledge graph. A Robert describes these knowledge graphs as what’s naturally happening inside your doctor’s head when they see patients. Their medical knowledge and experience allows doctors to quickly make connections between symptoms and diseases. RR: This knowledge graphs is what medical doctors have in their mind when they are diagnosing you, right? They have all these relationships and based on years of experience and training they are able to make these connections because they have this mental model of the relationships between symptoms, diseases, and everything. So this is what we are capturing with these knowledge graphs and what we are feeding to the machine learning algorithm together with the dataJW: But artificial intelligence will never replace a doctor. Robert and his team envision new AI models, like the one developed at PNNL, will be a powerful tools for physicians. RR: We are trying to help the doctors with tools that can provide more information to them. Because these tools will have access to larger databases and larger amounts of information that the amount of information that we can store in our brain. And probably, these systems can provide clues of things that maybe a medical doctor, based on a given set of symptoms, could not initially consider but maybe is that the answer of what is happening. As the amount of information that we have on patients increases, not just in quantity but also in complexity, it will be more common to have all the omics analyses for the patients when they are treating a disease. All this information, and understanding the relationships between all this information, is going to be to become more and more complex, and this type of tools can help doctors in order to establish all these connections. JW: Having a tool like this could literally save lives. Doctors are humans. Which means they can make mistakes. Think of their heavy work loads, the high stress environments, and long hours—all of these things combined could cause your doctor to unintentionally miss something. You could chalk this up to having “off day.” RR: Professionals, they may make mistakes. And these mistakes can be for a number of reasons: stress at a given point in time, it could be because the information that they have been provided is not complete enough, maybe is because at a given point in time they are tired and they do not see something which could be evident. So hopefully these systems will help in this in this space. But probably the perception and the experience that a human, and the things that a human can sense when is interacting with a patient, perhaps cannot be captured properly by just, you know, some analytical methods or things like that. So what I am trying to say is maybe that the doctor can have some perception and see something in the patient that the machine learning, together with all the lab analysis or whatever we are doing, we cannot see. I think this is the power of this combination, right? Having this human/machine teaming in which we have these two components playing together is when we have this win-win situation. JW: This allows doctors to give even better care than they do now and has the potential to completely change the way we receive care as patients.   RR: Probably in the future this technology, as I said, the first step will be like medical assistance. Where in which the system will help the doctor in the reasoning process in order to establish a diagnostic and will provide indications, probably quantitative indications, in terms of probabilities of the different types of diagnostics and also recommendations regarding what is the best course of treatment. As this progresses the systems will be more and more intelligent and probably the systems will be able to forecast in the longer term what is going to happen for a patient. And maybe in the future, even with all these biometric devices that we have, maybe if this is connected with some medical system the medical system itself can alert the doctor for a patient saying, “you know these vitals for this patient are going in this direction, and we have seen in other patients with the same characteristics and maybe the same genetic profile that they are prone to have this type of this. And also it's good to be proactive.” And so this probably is the what is going to have more impact in the patient side right this moving from this medicine which is much more diagnostic to something which is much more predictive medicine in which we are looking at the longer term and trying to forecast what is going to happen to the patient before it happens instead of curing what is happening right now.JW: The benefits of using AI for medical care are far reaching. Not only will these models give doctors a look into a patient’s medical future, they could also gain insight into solving current medical mysteries.RR: I'm hopeful that by doing the analysis on the large database that the VA has for all theveterans and focusing on specific diseases we are going to gain an incredible amount of novel insight on some of these diseases. I'm sure that we are going to be discovering things aboutcardiovascular diseases or prostate cancer that we didn't know just fewyears ago . And this will open, for sure, windows to this combination of artificial intelligence and medicine and the applications for the health of the of the veterans and for mitigating some of the diseases which are specifically targeting this population. JW: What’s next for this research? A new dataset that’s part of a collaboration between the Department of Energy and the Veterans Administration. The VA-DOE Big Data Science Initiative includes investigating better ways of predicting suicide, cardiovascular disease and approaches to treatment of prostate cancer. These are important issues. Someday in the future we hope to use AI to increase people’s length and quality of life, all thanks to artificial intelligence. MusicJW: Thanks for listening to Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening.

Pods of Science | Episode 3 | How to Do Science Faster with Artificial Intelligence Intro: Welcome. I’m your host, Jess Wisse. On today’s episode we’ll talking about a new research center created by the U.S. Department of Energy. Stay tuned to learn more. Music JW: Pacific Northwest National Laboratory, Sandia National Laboratories. Georgia Institute of Technology. What do these have in common? They are three powerhouses in the realm of artificial intelligence, and now they are working together. Want to know who the man is at the helm of this new collaboration? Meet, Roberto. RG: My name is Roberto Gioiosa. I'm a senior computer scientist at PNNL in the high-performance computing group. My background is in hardware and software core design and mostly focus on the design of operating systems, runtimes, and programming model in particularly looking at emerging a future architecture both for processing, memory, and networking. I came to PNNL in 2012 after years of other experiences in both academia and industry. Ever since I joined PNNL, I've been trying to lead efforts on software and novel hardware for computational scientists to speed up the solution to their problem and therefore solve scientific challenges. JW: Artificial intelligence and machine learning seems to be cropping up everywhere these days. From self driving cars to your new smart phone, its everywhere. Even Alexa, Amazon’s voice assistant, is getting smarter with each passing day. Soon, she will be able to guess what you might be thinking with a new feature called Alexa Hunches. Originally called thinking machines in the 1950s, artificial intelligence is a sub-field of computer science where machines develop the ability to think and learn on their own. Artificial intelligence, also known as AI, allows computers to perform tasks that historically could only be done by humans; think of things like visual perception, speech recognition, language translation. And that’s just the beginning. RG: Artificial intelligence and machine learning is something that is helping us revolutionize the way we do research. Rather than starting from the top and using first principle to solve a problem, we are trying to see what the data tells us about the problem. This is something that you see every day— look at natural phenomenon and you're trying to find a correlation between what you observe and what are the reasons for that the causal relationships that are in there. In some cases, you know this naturally is complicated and it's not easy to go and have a complete understanding of what is happening without knowing anything about the entire process. What AI is doing for us is helping us do reverse engineering of natural phenomenon. You have probably seen tons of movies about AI and how that can help, but the fundamental thing is we are looking at the data and we are trying to infer the structure of the phenomenon from the data. JW: Roberto is the director of a new co-design center, known as the Center for Artificial Intelligence-focused Architectures and Algorithms, or (ARIAA). ARIAA is taking AI & machine learning to the next level. RG: ARIAA is essentially a tool, a means, in which we are trying to understand what are the requirements from our application domains. In this case, are power grid, cybersecurity, graph analytics, and chemistry, and how artificial intelligence and machine learning can support these domains to allow novel discoveries. JW: AARIA will explore how AI and machine learning can support four areas that touch virtually every American’s life. Whether we’re aware of it or not we encounter power grid, cybersecurity, graph analytics, and computational chemistry almost every day. These are the disciplines where new medicines are created, where the fate of our online identity lies, it’s how masses of information is analyzed, and where our lights magically turn on with a flip of the switch. RG: AI is revolutionizing our world. You see that from your mobile phone to self-driving cars and all of this. Under the umbrella of AI there is a lot— a lot of activities, a lot of different kind of AIs. I think the U.S. government recognized the importance of a coordinated strategy to solve these problems. JW: Earlier this year the Department of Energy made a commitment to accelerate AI. Programs like AARIA are in line with President Trump’s call for a national strategy to assure AI technologies are developed to positively impact the lives of the American public. RG: Artificial intelligence is also revolutionizing the way we do science and the way we tackle important problems being at the national security, chemistry level, and new materials. DOE and Secretary Perry have recognized the importance of this moment and they are putting together strategy. The DOE Artificial Intelligence Technology Office is the first step, but there are also other activities including ARIAA that will support DOE strategies. We are really looking into what would science look like in the future where we have infrastructure and tools that can use AI to help us. JW: ARIAA is centered around a concept known as “co-design.” This concept, of co-design, takes into account both the capabilities of computing hardware and software. Roberto and his team must determine what types of applications will run best on a given hardware set-up, while also considering the type of hardware that will be needed when new software is created. It’s a balancing act that is never resolved and requires Roberto to constantly imagine the future of computing. RG: If you go from the bottom up, at the hard level we are looking to understand what kind of hardware accelerator will be necessary in the future to support AI workloads, but also scientific workloads that leverage AI. At the software level, we are trying to understand without the obstruction that needs to be put in place so that the way scientists can use our software and hardware without having to be hardware experts. At the application level, we are trying to identify the opportunities for using AI models to either replace first principle computation or to support other computation that we have in place like the economic in a simulation producing some data in a AI framework next to it to try and understand what's happening and then having a closed loop where you can actually modify the next round of simulation. The crosscut research is how to put all of this together to make sure that the hardware we're building, the software we are building, and the application algorithm that we are developing all make sense and they are impactful to the DOE mission, PNNL, and the society at large. JW: Not only do all of these components have to work seamlessly together they also have to be portable, so that anybody in the world can use the design. RG: And so what this will allow us, is to solve current problems in a much faster way. But especially it will allow us to tackle problems that today cannot be solved because they are too complex. To do that is not just one part of the hardware/software stuff that you need to address – you need to go from the top down. What ARIAA will look at is identify these places in our application domains that may require AI machine learning support and develop the proper hardware that needs to be put in place so that this model can be accelerated and speed up. We would do that in a way that can be portable. And of course we plan to release our products, both software, hardware design, and application, as open source to the community, so any other person in the world can go and download our models, our software stack, or our architecture designs. JW: How is Roberto going to pull off such a huge task? He’s not going to do it alone. He’s confident in his team. RG: I think one of the strengths of this project is the people, the team we have together. Our partners in ARIAA are Sandia National Laboratories and Georgia Tech. I firmly believe that one of the strengths of this project and one of the reasons why it was selected is because of the team we have together. JW: And it’s not just the fact that Roberto has some of the brightest minds in computing working at ARIAA, he credits the ability to do this to those who came before him. RG: This is a very large problem that we are trying to solve, and it wouldn't be possible if we didn't have experience in doing similar form of activities, and in artificial intelligence in particular. A lot of the credit for having this project started that goes to the fact that PNNL, Sandia, and Georgia Tech and have invested in the past on developing infrastructures, people, and knowledge. JW: Thanks to Roberto’s tireless efforts and the newly developed ARIAA scientists across disciplines will have the ability to do carry out their research even faster. Roberto likes to call it the science of the future. Music JW: Thanks for listening to Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening.

Jess Wisse (JW): What's behind the science and inventions that impact our daily lives? Pacific Northwest National Laboratory’s Pods of Science are the stories of what happens before the breakthrough. Before a technology becomes a house-hold name, before the life-saving drug his pharmacy shelves, before the paper's published - see what happens when great minds meet great challenges. Music Welcome. I’m your host, Jess Wisse. On today’s episode we’ll talking about new technology that may give dogs a run for their money. Wondering what we’re talking about? Stay tuned to learn more. Music JW: Floppy-eared sniffer dogs made the news earlier this year as the latest change to security procedures in airports. But in the not-too-distant future, they could be replaced. The replacement is a technology that doesn’t require a scratch behind the ear and a treat to its job. Meet the man behind the tech: Robert Ewing. Robert Ewing (RE): I like discovering things. I like solving puzzles. I like doing things that I don't think are possible, or challenging. And learning. JW: Robert Ewing is a scientist at PNNL. And he’s made a discovery that could potentially save lives. RE: I'm Robert Ewing. I'm a research chemist at the Pacific Northwest National Lab. I've been here for about 13 years. I've studied various analytical techniques for detecting trace of stances explosives and drugs, or some of those compounds. Ionization chemistry as a part of that. The instrumentation that goes along with that. Those are some of the things that I do for fun. JW: So, what did Robert discover? He and his team at PNNL developed a technology that’s ultrasensitive. It detects explosive vapors, deadly chemicals, and drugs like methamphetamine and fentanyl with unparalleled accuracy. And it works in seconds. RE: The technology really stems from using the detector of a mass spectrometer. And that's a way to look at different molecules, to understand what mass is there. And from that you can sort of determine the analyte. Here you're looking at what we did, or one of the challenges, was is the ionization. So, for the mass spec to see a molecule you've got to put a charge on there so it can manipulate that charge, create an electric field, and separate it. And so the ionization process is a way of getting that charge, that electrical charge, onto an individual molecule. And that's really where I've spent a lot of my time—understanding the chemistry around how that ionization process works and how to improve upon it. With the commercial mass specs that are out there that are pretty sensitive (parts per billion range and stuff) work pretty well. What we did is, we discovered that if you increase the amount of time that the ionization process can occur you can increase the sensitivity. And so the mass spec has a pinhole bringing the ions in from outside. What we do is, we took the ionization source and moved that away from the mass spec, and instead of having a few milliseconds of reaction time we give it two or three seconds and that gave us several orders of magnitude increase in sensitivity. JW: This technology could be a game-changer for transportation hubs, mail facilities, and other safety and security screening applications, like the ones you see in airports. Thanks to Robert’s tireless efforts, the system can detect a whole slew of things. Including explosive vapors, like TNT, toxic chemicals similar to nerve agents, and even illicit drugs, like fentanyl, methamphetamine, and cocaine. The most surprising part? Robert thought this was a problem that was un-solvable. But he kept mulling on the idea, and that got him to think about none other than man’s best friend. RE: Probably one of the driving force—the ah-hah moments—I always thought that explosive vapor was a challenge that we probably wouldn't overcome. And yet, dogs go out and sniff explosives all the time. Well, I've always wondered what dogs really smell? You know, are they smelling the explosive? Are they smelling the other things that surround that are that are higher vapor pressure, more volatile? And I remember I went to a conference on explosives detection and they had a guy who had a detector dog and showed the detection of RDX. But he talked about his process of how he purified it, cleaned it, then trained his dog and showed it. And I watched it, you know, I watched the video. That dog has really seen. And I was like, “why can't we do that?” And so it was really about a month later when I was in the lab and said, “well, okay I got all the right tools.” I put it all together and on a Friday afternoon, I made it work. I mean so at some point it just happened. JW: So how did Robert train this technology to sniff out specific vapors? The answer: Selective ionization chemistry. RE: We talked about selective ionization chemistry. When you're detecting vapor at low parts per trillion parts per quadrillion levels there's a lot other things in the room. So we try to find selective ionization, so it's kind of like finding a needle in a haystack. You know how you do that? You use a magnet, right? The magnet is selective to the needle. It finds the metal and it ignores the straw. This is the same kind of thing. And that's where we spend a lot of our time is in that chemistry. Pick the right reactant ions, the ions that you want to has will analyze your analyte, and with that will analyze the materials we are looking for, and be kind of be invisible to the other species. So some examples there, you asked what chemicals we look at. You know explosives was one example. You want to see explosives, but maybe you don't want to see the various hydrocarbons and diesel fuel or gasoline or perfumes or colognes—things like that. And so that's the selectivity part. JW: But for the longest time, detection of certain explosive vapors, like TNT, wasn’t possible. The instrumentation just wasn’t sensitive enough. Typical instrumentation can see chemical levels in the parts per million and billion range. But many of the explosives out there in the world have very low vapor pressures—they’re more like in the low parts per trillion or below. RE: So, an air you've got molecules and stuff floating around. They're always bumping into each other. I mean there's like 10 to the 11th collisions per second for a molecule, and air that's occurring. And so to bring those two together, you've got lots of lots of collisions that are probably going to occur. And so more time just gives you a higher probability that interactions going to happen. JW: So now that we can detect things like explosives, what’s the future look like? RE: So, one of the aspects of this this equipment. By being able to see vapor detection, that allows you to have non-contact detection. So, when you go through an airport nowadays, they usually swipe your bag and run it through an instrument looking for explosive residue. This ability to see vapor kind of helps remove that contact, so it's a little less invasive. So, you can do maybe baggage screening or cargo screening, or you know even people when you walk through the imagers there to have a look for vapor at the same time. And so really, it's taking the next step of non-contact. Hopefully increasing screening speed, getting people through the airports quicker, and also maybe a little more thorough in your searches. It's sort of that vision what we hope to do is be able to take this instrument and work with a smaller mass spec so then you can you get it in a footprint that's similar to stuff that's in the airport. Or you can integrate it to x-ray machines, or so forth. So, that's kind of what our hope is—to you know, take this to the next level. Get it into a smaller more portable size, and be able to test it and evaluate it, and see what other challenges need to be fixed. JW: We asked Robert how it feels to know that he’s responsible for a technology that could potentially save lives. And he said, “it’s pretty cool.” RE: It's challenging too because what we do doesn't happen overnight. I mean there every now then you have really good days and there's sometimes months where you struggle to get make things. So, it's a mix of, it takes a lot of determination to find those good breakthroughs. JW: So, while it may not be in your local airport yet, PNNL’s vapor detection technology has a bright future. And that’s exciting. [Music] JW: Thanks for listening to Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening. [Music]

Jess Wisse: What's behind the science and inventions that impact our daily lives? Pacific Northwest National Laboratories Pods of Science are the stories of what happens before the breakthrough. Before a technology becomes a house-hold name, before the life-saving drug his pharmacy shelves, before the paper's published. See what happens when great minds meet great challenges. Music Welcome, I'm your host Jess Wisse. On today's episode we'll be unveiling new research coming out of PNNL’s Data Sciences and Analytics Group, but before we unpack that here's a bit more information from our co-host Jessica Bernsen. Music Jessica Bernsen: Humans are social animals. Nowhere is that more apparent in today's modern world than on social media. Log in to your favorite social media platforms and you'll find a slew of conversations, debates, news, and more. PNNL researchers took all of this data and built a quantitative framework to better understand communication patterns and how information spreads online. Meet Svitlana. Svitlana Volkova: My name is Svitlana Volkova and I am a senior scientist at PNNL. I've been here for three years, and my work involves machine learning, deep learning, natural language processing, and computational social science. I work a lot with social data. Jess Wisse: Findings gathered by Svitlana and her colleagues Maria Glensky and Emily Saldana shed led light on to how cryptocurrency discussions spread and they also could inform artificial intelligence applications used to forecast things like cryptocurrency prices. Svitlana Volkova: So in this paper we analyzed almost three years worth of data; a lot of discussions, millions of posts, and comments and that’s what makes this research interesting that like we have access to this vast amount of data that we have the techniques and methodology to analyze really fast and draw some insights and scientific conclusions from this data that can in turn inform machine learning and deep learning models to predict the future. Jessica Bernsen: Nobody really looked into how information about cryptocurrency spreads on reddit specifically and that's what Svitlana and her team did. Svitlana Volkova: The current data set included the historical rise of the Bitcoin price and we specifically wanted to look into social signals around this historical event when the price is increasing and then decreasing we wanted to see how social environments are reflecting this change. We found that across of three coins, the discussion spread is very different. We know that Bitcoin is the most popular coin, and that was reflected in our analysis. We found that comments on a Bitcoin post about was the fastest—on average people responded in 11 minutes to discussions about Bitcoin versus Monero and Ethereum. In Ethereum threads, it takes people at least 30 minutes to follow up on a on a post, but interestingly we found that Monero has really long, ongoing conversations compared to Bitcoin conversations that have a very short life time. They don’t live long. And Bitcoin conversation focus on a specific audience, which on average is between 2 and 6 people. Monero conversations involve more people, and more diverse audiences. And structurally the discussions are very different. The Monero discussions are like chains. They go deep. And at each level they have a specific size of the audience. Bitcoin discussions are more diverse, and they form trees, and they go more viral compared to Monero. Jess Wisse: Svitlana and her team looked in to reddit, but their research can also be applied to a variety of platforms. For example, the framework they designed for measuring information spread can be also be extended to measure the spread of other types of information. Such as images on Instagram, videos on YouTube, hashtags on Twitter. Svitlana Volkova: This analysis would be very helpful for a different predictive analytics so for example you can look how discussions spread around different cryptocurrencies and social platforms and more specifically across social platforms that involved that goes beyond Reddit and actually try to predict cryptocurrency prices. Music Jessica Bernsen: We share a lot of information on social media everyday this information can be used in a variety of ways not only to predict cryptocurrency prices. Svitlana Volkova: So this work is specifically focusing on one type of information - cryptocurrencies in one social environments, but you can think about more general applications and implications of this work. So for example you would like to know how this information and false narrative spread or one might want to measure how different discussions about software vulnerabilities spread. Other people might be interested in how negative language spreads across social environments so all of this is applicable. The evaluation framework and the measurements that we use that are targeting specific social phenomena. Ideally we would like to take any piece of information whether it's text or image or video and see how it spreads and measure how many people it reaches how my like what is the size of the audience how fast it spreads and what is the actual impact on it the society so this is the main goal of the whole project. Jess Wisse: So how did Svitlana even get into this line of work and why does it all matter? Svitlana Volkova: It's very interesting to understand people. I love measuring how people act and interact and respond in social environments that's what we do daily right we go to social media and we respond to posts we comment and we spread the information we are the people who are spreading the information and we actually influence the rate of spread and the impact right? So I think it's very fascinating to see what people can do and measure it actually not like talk about it and like and have a rate we can actually measure it quantitatively and maybe we can change something if we want to change something Jessica Bernsen: In a nutshell Svitlana Volkova: We build machine learning models that can predict the future. Jessica Bernsen: And that’s powerful. Jess Wisse: Thanks for listening to our first episode of Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening. Music.