Discovery Days returns!

On April 30 and May 1, thousands of elementary and middle school students from across Washington state will arrive on the 痳豆在线鈥檚 Seattle campus to explore more than . Hosted by the UW College of Engineering, Discovery Days gives students a chance to experience science and engineering concepts for themselves by building batteries, designing videogames, firing air vortex cannons and controlling plasma with their fingertips.听

This year, more than 9,000 students from 109 schools registered to attend.

For more information, contact William Poor at wpoor@uw.edu.

Even though he wanted to bike commute from his Capitol Hill home to the 痳豆在线, Jared Hwang often took transit because he struggled to find a good bike route. Apps like Google Maps and Strava might suggest hilly, busy streets simply because they have bike lanes. He even headed to Reddit to crowdsource ideas.听

鈥淚 was like, surely, this cannot be the best way to do things,鈥� said , a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. 鈥淭his data is out there. We know where bike lanes are, what the roads are like, what the speed limits are. We should be able to easily access all this information at once.鈥�

So Hwang and a team of UW researchers built , a demo web app that lets users find personalized bike routes in Seattle. Cyclists plug in their origin and destination 鈥� just like in other mapping apps 鈥� and can then create personalized routes by adjusting eight sliders.听聽

Researchers initially worked with four participants to understand how cyclists tend to plan their routes. Based on that, they built a prototype of BikeButler. For the basic street layout and other info, they pulled data from OpenStreetMap and government data sets. But those didn鈥檛 have information on more subjective qualities.听

For those, researchers turned to Google Street View. They used a visual language model, or VLM 鈥� a type of artificial intelligence 鈥� to analyze street images and rate subjective attributes like greenery and pavement quality. The team had the VLM rate the level of greenery on streets and then compared this with two researchers鈥� ratings. The humans agreed with each other about as much as they agreed with the VLM 鈥� about 60% of the time. Future research might try to gather individual users鈥� greenery preferences to offset this discrepancy.听

Once they鈥檇 mapped most of Seattle, the team tested the prototype with 16 participants.听

鈥淥verall the response was really positive,鈥� Hwang said. 鈥淲e found that people do, in fact, have contextual preferences. A cyclist riding for fun on a Saturday might want a safer, greener route compared with their fast work commute. People intuitively know this, but it hadn鈥檛 been established through research.鈥澛�

Researchers say future work might integrate feedback from the user study, such as the ability to drag routes to change them slightly and an option to take fewer turns. The team is currently studying how to quantify cyclists鈥� preferences around intersections and turns.

The researchers note that the quality of BikeButler鈥檚 recommendations is constrained by the recency and accuracy of the data it uses. For instance, a new bike lane might not yet appear on a map, or it could appear in OpenStreetMap but not Google Street View. Also, since the team planned this as a proof of concept, BikeButler is limited to Seattle, though it could be expanded to other areas.听

鈥淚鈥檓 a lifelong biker and bike commuter,鈥� said senior author , a UW professor in the Allen School. 鈥淲hat excites me most about Jared鈥檚 work is how it points to a future where we receive route choices individualized to our preferences. So whether I鈥檓 biking with my two young children, or riding for groceries, I can find a route for that context.鈥�

Co-authors include , a student at Issaquah High School and intern in the Allen School; , a UW doctoral student in urban design and planning; and , a UW student in the Allen School. This study was supported by the National Science Foundation.

For more information, contact Hwang at jaredhwa@cs.washington.edu.

As far as research subjects go, it鈥檚 not always easy to find common ground with a single-celled bacterium. Yet the more studies his model bacteria, , the more he sees surprising commonalities between their behavior and our own as humans.

鈥淚t was mortifying to be stumped for so long by what appeared to be completely counterintuitive behavior only to realize that I engage in exactly the same behavior everyday,鈥� said Wiggins, an associate professor of both physics and bioengineering at the 痳豆在线.听

Scientists in use experiments and modeling to understand the global principles that govern gene expression, and protein abundance in particular. In in Science Advances, Wiggins鈥� team discovered that A. baylyi cells amass huge surpluses of essential proteins, rather than taking the seemingly more efficient approach of making just enough to survive. UW News chatted with Wiggins to learn about the remarkably relatable reason for this puzzling behavior.

This work grew out of a mystery you and your team uncovered. Tell us about that mystery.

Paul Wiggins: Genes are the blueprints for proteins 鈥� we say they 鈥渃ode for proteins.鈥� A. baylyi has a number of genes that code for proteins that we know are essential for cell growth. But we didn’t know exactly what each of these proteins do. In 2016, we were attempting to uncover these proteins’ specific functions in collaboration with the . To do this we disrupted each gene so that the cells couldn’t make any more protein 鈥� they were left with a now dwindling supply of whatever they鈥檇 previously made. Then we would watch the cells under a microscope to determine when and how cellular processes would fail.听

As an example, we knocked out a gene that coded for a protein that we found was responsible for cell wall synthesis 鈥� it makes the protein-sugar chainmail that prevents the cells from rupturing, or lysing. And you can watch the video we recorded to see what happened: The cells grew and divided for a while, but then all of a sudden they inflated and just popped.

In that example, something strange happened. We would expect the cell walls to start to fail almost immediately after the disruption happened because every time the cells divide, the remaining protein is divided among the offspring cells, so pretty quickly there wouldn鈥檛 be enough to sustain the new cell walls. However, growth continued, one generation after another, before the cells finally failed after four rounds of division!

Why did it take so long? Gene after gene showed the same pattern. We realized that each cell must have made a ton of extra proteins 鈥� far more than it needed. So after we knocked out that essential gene, the cell was able to run on fumes for a while 鈥� and was even able to pass stores of that protein on to its offspring. That finding was initially a huge surprise. We all expected, naively, that if a cell only needed a few copies of a protein to function, it would only make a few 鈥� anything more would be a waste of resources and energy. It鈥檇 be like taking a seven-day trip and packing 30 pairs of socks. And yet, this behavior seemed to be common for lots of essential genes.听

What do you think is the cause of this protein overabundance?

PW: Baking is a good analogy. If you want to make an apple pie, you probably only buy as many apples as you need for that recipe. But you keep a large quantity of salt in your pantry. You might only need a teaspoon of salt to make any given meal, but none of us go to the store and buy salt a teaspoon at a time. Salt is so cheap and easy to store that, relative to the cost of other ingredients in your meal, it鈥檚 basically free to keep in large quantities. And critically, you don鈥檛 want to run out of salt when you鈥檙e cooking.听

We demonstrated that something analogous is happening in A. baylyi cells for most of the essential genes. Only about 30% of a cell鈥檚 essential genes code for proteins that are “expensive” in that the cells need these proteins in large numbers. It would be very costly to, say, double an already large number. These are the apples in our apple pie analogy 鈥� the cell makes just enough of those proteins to get by.听

The remaining 70% of essential genes, however, code for proteins that the cell does not need in large numbers. In fact, relative to that other 30%, the cell needs so few of these proteins that it鈥檚 basically free to produce a bunch of extras. Doubling the production of those proteins, say from 30 to 60 copies, is a drop in the bucket if the cell鈥檚 overall budget is three million proteins. So the cell says, 鈥淪crew it, it鈥檚 virtually free. Let鈥檚 make extra so we don鈥檛 run out.鈥� In some cases a cell might make 10 times more protein than it will ever need.

Why is this strategy useful for the cells?

PW: This overabundance strategy is important because otherwise a cell might fail to produce enough of something critical. Protein synthesis is an imprecise process 鈥� cells sometimes make a little more or a little less of things than they鈥檙e programmed to make. Some essential proteins are made at such low numbers that any deviation from the plan could leave a cell with zero copies of that protein. This is less of a problem for essential proteins that are made in much higher numbers.听

How do these findings support or challenge previous ideas about how cells function?

PW: Depending on who you talk to, this is either definitely wrong or completely obvious. On the one hand, it鈥檚 a really ingrained idea that organisms are always optimizing everything, which would naively suggest that cells should make exactly what they need 鈥� no more, no less. However, this is clearly not the case. Other studies have observed these kinds of protein surpluses in cells before, but it wasn鈥檛 appreciated quite how wide-spread this phenomenon was. Previously researchers proposed that overabundance might be a hedge against changing conditions 鈥� maybe cells are stockpiling proteins in case times get tough. We鈥檙e suggesting that it鈥檚 a hedge against the cells failing to make the right number of essential proteins.

Co-authors include , a UW postdoctoral researcher of physics; Teresa W. Lo and , former UW doctoral students of physics; , a UW graduate student of physics; and , a UW postdoctoral researcher of laboratory medicine and pathology.

This research was funded by the National Science Foundation and the National Institutes of Health.

For more information, contact Wiggins at pwiggins@uw.edu.听

痳豆在线 researchers developed the first system that incorporates tiny cameras in off-the-shelf wireless earbuds to allow users to talk with an AI model about the scene in front of them. For instance, a user might turn to a Korean food package and say, 鈥淗ey Vue, translate this for me.鈥� They鈥檇 then hear an AI voice say, 鈥淭he visible text translates to 鈥楥old Noodles鈥� in English.鈥�

The prototype system called VueBuds takes low-resolution, black-and-white images, which it transmits over Bluetooth to a phone or other nearby device. A small artificial intelligence model on the device then answers questions about the images within around a second. For privacy, all of the processing happens on the device, a small light turns on when the system is recording, and users can immediately delete images.听

The team will April 14 at the Association for Computing Machinery Conference on Human Factors in Computing Systems in Barcelona.听

鈥淲e haven鈥檛 seen most people adopt smart glasses or VR headsets, in part because a lot of people don鈥檛 like wearing glasses, and they often come with , such as recording high-resolution video and processing it in the cloud,鈥� said senior author , a UW professor in the Paul G. Allen School of Computer Science & Engineering. 鈥淏ut almost everyone wears earbuds already, so we wanted to see if we could put visual intelligence into tiny, low-power earbuds, and also address privacy concerns in the process.鈥�

Cameras use far more power than the microphones already in earbuds, so using the same sort of high-res cameras as those in smart glasses wouldn鈥檛 work. Also, large amounts of information can鈥檛 stream continuously over Bluetooth, so the system can鈥檛 run continuous video.听

The team found that using a low-power camera 鈥� roughly the size of a grain of rice 鈥� to shoot low-resolution, black-and-white still images limited battery drain and allowed for Bluetooth transmission while preserving performance.

There was also the matter of placement.听

鈥淥ne big question we had was: Will your face obscure the view too much? Can earbud cameras capture the user鈥檚 view of the world reliably?鈥� said lead author , who completed this work as a UW doctoral student in the Allen School.听

The team found that angling each camera 5-10 degrees outward provides a 98-108 degree field of view. While this creates a small blind spot when objects are held closer than 20 centimeters from the user, people rarely hold things that close to examine them 鈥� making it a non-issue for typical interactions.

Sixteen participants also wore VueBuds and tested the system鈥檚 ability to translate and answer basic questions about objects. VueBuds achieved 83-84% accuracy when translating or identifying objects and 93% when identifying the author and title of a book.

This study was designed to gauge the feasibility of integrating cameras in wireless earbuds. Since the system only takes grayscale images, it can鈥檛 answer questions that involve color in the scene.听

The team wants to add color to the system 鈥� color cameras require more power 鈥� and to train specialized AI models for specific use cases, such as translation.听聽

鈥淭his study lets us glimpse what鈥檚 possible just using a general purpose language model and our wireless earbuds with cameras,鈥� Kim said. 鈥淏ut we鈥檇 like to study the system more rigorously for applications like reading a book 鈥� for people who have low vision or are blind, for instance 鈥� or translating text for travelers.鈥澛�

Co-authors include , a UW master鈥檚 student in the Allen School, and , , , and , all UW students in electrical and computer engineering.听

For more information, contact vuebuds@cs.washington.edu.

Even on a campus like the 痳豆在线鈥檚 鈥� home to particle accelerators, wave tanks and countless other bespoke pieces of equipment 鈥� the machinery in the stands out. Take the dilution fridge, a large, white, cylindrical device that can cool a small chamber to one hundredth of a kelvin above absolute zero 鈥� the coldest possible temperature in the universe.听

鈥淭his is the coldest fridge money can buy,鈥� said , a UW assistant professor of electrical and computer engineering and the former director of the lab, which goes by the nickname QT3. 鈥淲hen it鈥檚 running, the chamber inside this device is about 100 times colder than outer space. At that temperature, it鈥檚 much easier to study and manipulate a material鈥檚 quantum properties.鈥�

The lab also houses a photon qubit tabletop lab: a nondescript set of boxes, lasers and lenses that can demonstrate the 鈥渟pooky鈥� 鈥� a term scientists actually use 鈥� phenomenon known as quantum entanglement, where two particles appear to communicate instantaneously with each other despite being physically apart.

Or there鈥檚 the lab鈥檚 latest acquisition, the scanning tunneling microscope, which can image individual atoms within a solid material, allowing researchers to study the structure of materials at the smallest scales.

An interdisciplinary group of researchers has been marshalling resources and expertise to create QT3 for three years, and now, the lab is opening its doors as a unique one-stop shop resource for quantum researchers and educators at the UW.

鈥淭he idea of this lab is to improve access to quantum hardware,鈥� Parsons said. 鈥淚t’s rather hard to acquire equipment like this. And there are a lot of researchers that may have good ideas that they want to test, but don鈥檛 have the resources yet for their own equipment. So we鈥檙e inviting researchers, initially from across campus, but also from other universities and from industry, to come in and test their ideas. This can be a hub for quantum experts to share their ideas and collaborate.鈥�

The lab also boasts hardware that can demonstrate known quantum principles and techniques, making it useful for students in quantum fields. In addition to the entanglement device, Parsons鈥� students developed a machine that can suspend charged particles 鈥� in this case, tiny grains of pollen 鈥� in midair using electric fields. Researchers use the same technique to trap single atoms and manipulate their quantum properties, making the lab鈥檚 ion-trapping machine good practice for more complex work.

Some students even work at the lab through an undergraduate staffing program, and have helped install instrumentation, write code to power equipment and build parts for custom microscopes. The program provides yet another avenue for students to get hands-on experience with unusual machinery and techniques.听

鈥淨uantum mechanics is inherently counterintuitive, and that makes it a powerful teaching tool,鈥� Parsons said. 鈥淚n the QT3 lab, students will encounter systems where their everyday intuition breaks down, and they must rely on careful reasoning and experimentation instead. They learn how to debug when results don鈥檛 match expectations, how to test simple cases and how to build understanding about hardware step by step.鈥�

The cosmically cold dilution fridge remains something of a centerpiece, even as the lab fills up with specialized equipment. The extreme environment within the device strips heat, light and other stray energy away from materials, allowing researchers to observe the peculiar quantum properties that remain. One such property is superposition, or the ability of a particle like an electron to maintain multiple mutually exclusive properties at the same time. Scientists use superposition to create a powerful, tiny piece of technology: a quantum bit, or qubit.听

鈥淭raditional computers use bits, which can only be one or zero. A qubit, on the other hand, we can make one plus zero,鈥� Parsons said. 鈥淚t’s both at the same time, and only when we measure it do we find out which one it is. We can use this unusual property to build a new class of computers that excel at tasks like communications and encryption.鈥�

QT3 is part of a collaborative effort to solidify UW as a leader in quantum research and applications. Most of the lab hardware was funded by a congressional earmark championed by Senator Maria Cantwell鈥檚 office. Departmental funding from across the College of Engineering and the College of Arts and Sciences helped rehab the lab space. The National Science Foundation provided seed funding for the instructional lab equipment.

The UW has also spent the past decade investing heavily in faculty with quantum expertise.

鈥淰ery few places have expertise across the full quantum stack, from materials up to algorithms,鈥� said , a UW professor of physics and founder of QT3. 鈥淭he UW has quantum faculty in electrical and mechanical engineering, physics, computer science, materials science and chemistry. Our faculty work on superconducting qubits, spin defects, photons, trapped ions, neutral atoms and topological qubits. Our advantage is the breadth of our investment.鈥�

The lab is now available to researchers and students across the UW, and private companies are encouraged to reach out about partnering. Parsons has already used the lab to teach a graduate-level class in electrical and computer engineering for students who included employees from Boeing, Microsoft and quantum computing company IonQ. The lab is hiring for a full-time manager to maintain the equipment and help users make the most of the facility.听

鈥淗ere in academia, we can improve the building blocks for applied technologies like quantum computing, and then transfer those learnings to industry for further scaling,鈥� Parsons said.

For more information, contact Parsons at mfpars@uw.edu.

This winter was ; as a result, many snowy, alpine areas have seen bouts of winter rainfall where there would ordinarily only be snow. These unusual weather patterns have contributed to an abysmal ski season, but they can also set the stage for dangerous avalanches. At temperatures close to freezing, precipitation can fall as rain but freeze when it hits the snow, forming an icy crust. Snow that accumulates on top of that crust is unstable and prone to abrupt slides, causing an avalanche that can close down a major highway in moments, endanger backcountry skiers and more.

Avalanche experts in Western Washington know how to manage the risks associated with rain-on-snow events, but many of their counterparts in colder regions like Eastern Washington, Idaho and Montana are less familiar with these dynamics. New research from the 痳豆在线 shows that as winters in these regions warm, their snowpacks may come to resemble those of maritime areas, with more rain-on-snow events, icy crusts and complex avalanche forecasting.听

The findings in ARC Geophysical Research.

鈥淭his winter鈥檚 warmth is a harbinger,鈥� said lead author , a UW graduate student of civil and environmental engineering. 鈥淲e know that temperatures will keep rising, and our work is a red flag for cooler regions of the greater Pacific Northwest, such as Idaho and Western Montana, that aren鈥檛 used to dealing with ice crusts and their resulting avalanche problems.鈥�

The study is part of a larger effort to understand the structure of snow as it accumulates, which has implications for weather and avalanche forecasting, wildlife dynamics and more.听

鈥淪now scientists are pretty good at measuring snow depth and volume,鈥� said senior author , a UW professor of civil and environmental engineering. 鈥淲e鈥檙e also pretty good at figuring out how much water you get if all that snow melts. But our models aren鈥檛 as good at representing snow structure, such as layers of different densities and crystal types that increase avalanche risks. And we really want to know how the structure of snow changes as the climate changes. That鈥檚 a tricky question that no one has tackled, particularly for rain-on-snow conditions.鈥�

To dig into that question, the researchers studied how warming influences ice layer formation in seasonal snowpacks. First, they collected temperature and precipitation data captured by 53 monitoring stations across the Pacific Northwest for the past 25 years. They used a computer model to identify days when ice layers likely formed at each location. They then checked the model against real-world measurements at one of the locations 鈥� a station at Snoqualmie Pass 鈥� and found that the model matched the measurements with 74% accuracy.

Finally, they used the same model to simulate those same 25 winters at 2 C and 4 C warmer than they were, and looked for changes to the number of ice crusts across the region. , the Pacific Northwest is expected to warm by 2 C to 5 C by 2050 as compared to pre-2000 temperatures.

The results were split regionally by the Cascade mountains. In colder, inland parts of the Pacific Northwest 鈥� places like Eastern Washington, Idaho and Montana 鈥� higher temperatures created more rain-on-snow days and more avalanche-prone ice layers. Locations in the warmer, maritime Cascades saw the opposite effect: Higher temperatures created slush instead of ice, potentially reducing the avalanche risk associated with ice crusts.听

The predicted snowpack changes may also impact wildlife behavior. Some foraging mammals, such as reindeer, dig down into the snow in search of food and may have a hard time breaking through an icy crust. Conversely, firm ice might provide a better running surface for animals fleeing predators. Specific regional effects will require additional study.

What鈥檚 clear now is that those who work or play in avalanche terrain in broad swaths of the Pacific Northwest 鈥� and even beyond 鈥� may need to adjust to a new set of risk factors.

鈥淚鈥檇 love to see this shared with avalanche forecasters widely, both as a call to action and as a way to help them understand what their snowpack might look like in the future,鈥� Alden said.

, the director of geospatial science at Audubon Alaska and former doctoral student of environmental and forest sciences at the UW, is a co-author.

This research was funded by the NASA Interdisciplinary Research in Earth Science program and the UW Program on Climate Change鈥檚 Graubard Fellowship.

For more information, contact Alden at cdalden@uw.edu.

At the 痳豆在线 Harris Hydraulics Lab, an odd scene plays out. Over and over again, researchers from the UW and the (PNNL) pass a small rubber model of a marine animal through a large tank filled with flowing water and fitted with a spinning turbine. On some runs, the model bonks against the turbine blades; on others, it receives a glancing blow or sails past undisturbed. When bonks or knicks occur, a small collision sensor on one of the turbine鈥檚 blades detects the impacts and plots the interactions in a computer program.

The researchers are repeatedly simulating something that they hope will rarely happen in the wild: a collision between marine wildlife like a seabird, seal, fish or whale 鈥� or submerged debris like logs 鈥� and an underwater turbine.听

鈥淲e want to make sure we鈥檙e minimizing the chances of a collision in the first place,鈥� said Aidan Hunt, a senior research engineer in mechanical engineering at the UW and member of the (PMEC). 鈥淏ut if a collision were to occur, we want to be able to detect it, and potentially avoid it, in real time. The available evidence suggests that collisions are rare, but we鈥檙e taking a 鈥榯rust-but-verify鈥� approach.鈥�

Marine energy 鈥� power harvested from tides, waves and currents 鈥� has enormous potential as a clean, renewable resource. But more information is needed about how large, commercial installations of underwater turbines or power-generating buoys could affect marine wildlife, whether through increased noise in the environment, habitat change or direct interactions with equipment.听

The marine collision experiments are part of the , a collection of projects led by PNNL to study the environmental impact of marine energy.听

The work at Harris Hydraulics follows a by PNNL and the UW Applied Physics Lab using a four-foot-tall prototype turbine installed at the entrance to Sequim Bay. In that study, researchers trained an underwater camera on the turbine for 109 days and then catalogued every instance of an animal approaching or interacting with the turbine. The camera captured more than 1,000 instances of fish, birds and seals approaching the turbine blades. There were only four collisions, and all were small fish.听

鈥淭his study was a first step, but a promising one,鈥� said co-author , a research scientist at the UW Applied Physics Lab. 鈥淲e didn鈥檛 see any endangered species in our study, and the risk of collision for seals and sea birds seemed to be quite low. We鈥檙e excited to get back out there with the camera and learn even more.鈥�

The Sequim Bay experiment generated hours of valuable data, but that degree of intense monitoring may not be practical in large commercial installations in the future. Cheaper impact sensors, like the ones logging bath toy impacts at Harris Hydraulics, could be a solution, researchers say.听聽

The project is funded by the U.S. Department of Energy鈥檚 Hydropower & Hydrokinetics Office, through the Pacific Northwest National Laboratory鈥檚 Triton Initiative and the TEAMER program.

For more information, contact Hunt at ahunt94@uw.edu or Emma Cotter at emma.cotter@pnnl.gov.

As climate change nudges weather in the eastern Cascades in extreme and volatile directions, forest managers in the region have a lot to juggle. Hotter, drier summers are contributing to bigger and more frequent wildfires. Meanwhile, warmer winters may cause the Cascades to lose 50% of its annual snowpack over the next 70 years. Mountain snow supplies the Yakima River Basin with 75% of its water supply, making it a crucial reservoir for both nature and agriculture . Less winter snow also leads to drier and more fire-prone forests in the summer.

To encourage fire resilience, forest managers use tried-and-true tools like controlled burning and the selective felling of trees to thin out the forest. Both methods remove fuel and help return forests to historical conditions 鈥� but less is known about their impact on snowpack.

To address this knowledge gap, a team of researchers at the 痳豆在线 and The Nature Conservancy (TNC) embarked on an ambitious, multiyear study of snowpack along Cle Elum Ridge, an area of the eastern Cascades in the headwaters of the Yakima River Basin. The group experimentally thinned the forest to varying degrees in a roughly 150-acre area. Then, they measured the amount and duration of snowpack during the winter of 2023 and compared it to a previous winter before the forest treatment.听

The results were encouraging: Forest thinning efforts increased snowpack by 30% on north-facing slopes and by 16% on south-facing slopes. Thinning aided snowpack the most where it created a patchwork of gaps in the forest rather than a more even density; gaps of 4-16 meters in diameter seemed to be the 鈥渟weet spot鈥� for snow.听

The research points toward more refined forest management practices that can optimize for both wildfire resilience and snowpack.

in Frontiers in Forest and Global Change.

鈥淎t its core, this research shows that reducing wildfire risk and protecting water resources don鈥檛 have to be competing goals,鈥� said lead author , a postdoctoral researcher at the University of Alaska who completed this work as a UW doctoral student of civil and environmental engineering. 鈥淭hat鈥檚 genuinely good news for a place facing both growing wildfire threats and increasing water vulnerability. So much of the climate conversation focuses on loss, which makes findings like this especially meaningful.鈥�

Predicting snowpack in forested areas, especially those at higher altitudes, hinges on understanding how much snow reaches the ground and how much lands in the forest canopy. Snow on the ground is more likely to stick around through the season, whereas snow in the trees may either melt or sublimate back into water vapor. In either case, it wouldn鈥檛 add to the reservoir of water that melts in the spring and summer.听聽

鈥淭rees intercept snow and so can reduce snowpack, but trees also shade snow and so can retain snowpack,鈥� said senior author , a UW professor of civil and environmental engineering. 鈥淭he dominant effect depends on winter temperatures, and the Cascade crest near Cle Elum is right on the border where the effect flips from trees decreasing snow to trees saving snow.鈥澛�

found that natural gaps in the forests of the eastern Cascades accumulated more snow. This, combined with other research, gave the team reason to hope for a positive connection between forest thinning and snowpack, though it wasn鈥檛 a sure thing. have found that open areas elsewhere in the Western U.S. saw reduced snowpack.

Thus, it was time for a direct 鈥� and complex 鈥� study of managed forests.

Researchers picked Cle Elum Ridge for the work, where TNC鈥檚 forest managers were planning thinning treatments to improve forest health and wildfire resiliency. The orientation of the ridge allowed them to compare north- and south-facing slopes 鈥� southern slopes in the region see more sunshine and less snow retention on average. From October 2021 to September 2022, the researchers worked with TNC鈥檚 forest managers and local contract loggers to remove trees on both slopes in a gradient, from no thinning to extensive. The team also set up time-lapse cameras at several strategic points to measure snow depth over time.

Then, they waited for snow to fall.

By March 2023, the area was close to its peak snowpack, and the team returned with staff and equipment from the UW (RAPID). The RAPID crew flew a specialized drone that generated a detailed 3D map of the study area using a laser-mapping technology called lidar.听

By comparing the new 3D map and timelapse imagery to lidar data captured before the forest treatment, the team was finally ready to calculate two things: the change to the forest structure, and its effect on the snowpack.

Across the whole study area, the team found that thinning helped the forest recover 12.3 acre-feet (or about four million gallons) of water in the form of snow per 100 acres on north-facing slopes, and 5.1 acre-feet (or about 1.5 million gallons) per 100 acres on south-facing slopes.听

As expected, areas where the thinning opened gaps in the canopy were most effective at restoring snow storage that had been previously lost to environmental degradation and climate change. Gaps of 4-16 meters in diameter seemed to retain the most snow, though there were few gaps larger than 16 meters to evaluate.

One surprising result: The way forest managers thin forests doesn鈥檛 reliably create gaps. Forest managers map out their reductions using the density of trunks in an area, not canopies, as their primary measurement.

鈥淚magine a group of 100 people all holding umbrellas in the rain,鈥� said co-author , director of the UW Climate Impacts Group. 鈥淭hey鈥檙e standing close enough together that their umbrellas overlap, so none of the rain hits the ground. If you remove 10 of the umbrellas randomly, you鈥檇 still have plenty of coverage overall. But, if you remove 10 umbrellas that are right next to one another, you create a gap in the umbrella 鈥榗anopy,鈥� and you get a 10% increase in the amount of rain that hits the ground.鈥�

That realization adds a nuance to the findings. It鈥檚 likely that forest thinning can benefit both wildfire and snowpack resilience at the same time, but only if managers keep canopy gaps in mind.听

鈥淥ne thing we all learned was that snow people and tree people speak different languages,鈥� Lumbrazo said. 鈥淒ifferent experts look at totally different variables to help them decide whether or not to cut down a single tree. So an important goal is to get everyone speaking the same language. And I think this paper is one step towards better communication.鈥�

Overall, the results suggest practical changes to forest management practices in the eastern Cascades. For example, managers might consider more tree-thinning on north-facing slopes, since snowpack gains may be greater there. With further research, these learnings may also extend to other regions in the Pacific Northwest.听

The work could also aid collaboration between forest managers and hydrologists at a time when the region needs all the water it can get.

鈥淎s we lose snowpack, everything becomes really squeezed,鈥� said co-author , a senior aquatic ecologist at TNC who earned her doctorate in aquatic and fishery sciences at the UW. 鈥淲e are currently in our third consecutive year of water restrictions in the Yakima River Basin, and are staring down one of the lowest snow years on record. However, our research shows that the treatments currently used for restoring fire resilient forests are compatible with the forest structure needed for supporting water security. And in a world where climate change is reducing water supplies and increasing wildfire severity, we are pleased to report that the same forest treatments can support both goals.鈥�

Co-authors include , a former UW graduate student of civil and environmental engineering; , a former UW undergraduate student of atmospheric and climate science; , a data processing specialist at the UW RAPID facility; and , director of Forest Conservation and Management at The Nature Conservancy.

This research was funded by The Washington Department of Natural Resources, The Nature Conservancy and the National Science Foundation.听

For more information, contact Lundquist at jdlund@uw.edu, Dickerson-Lange at dickers@uw.edu or Howe at emily.howe@tnc.org.听

Heart rate is an important sign of fetal health, yet few technologies exist to easily and inexpensively track fetal heart rates outside of doctors鈥� offices. This can create risks for pregnancies in low-resource regions where doctors are far away or inaccessible.听

A team led by 痳豆在线 researchers has created DopFone, a system that uses an off-the-shelf smartphone鈥檚 existing speaker and microphone to accurately estimate fetal heart rate. The phone mimics a Doppler ultrasound, emitting a tone and listening for the subtle variations in its echo caused by fetal heart beats. A machine learning model then estimates the heart rate. In a clinical test with 23 pregnant women, DopFone estimated heart rate with an average error of 2 beats per minute, or bpm. The accepted clinical range is within 8 bpm.听

The team Dec. 2 in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.听

鈥淓ventually DopFone could let people test fetal heart rate regularly, rather than relying on the intermittent tests at a doctor鈥檚 office, or not getting tested at all,鈥� said lead author , a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. 鈥淧atients might then send this data to doctors so that they can better judge patients鈥� health when they鈥檙e not in a clinic.鈥�

Traditional Doppler ultrasounds, the clinical standard for fetal heart rate monitoring, work by sending high-frequency sound into a person鈥檚 body and tracking how the echo changes in frequency. They鈥檙e very accurate at measuring fetal heart rate but require costly equipment and a skilled technician to operate it.

To use DopFone, a user places the phone鈥檚 microphone against their abdomen for one minute. The phone emits a subaudible 18 kilohertz tone. The team chose this low frequency because 鈥� unlike a Doppler鈥檚 high frequencies, above 2,000 kilohertz 鈥斅� it sits within the range smartphone microphones can record while still traveling well through tissue. As the tone is reflected through the user鈥檚 abdomen, the fetus鈥檚 heartbeat creates small shifts in the sound.听

A machine learning model then estimates the heart rate using the audio and the patient鈥檚 demographic information

The team tested DopFone in UW Medicine鈥檚 maternal-fetal medicine division on 23 pregnant patients between 19 and 39 weeks of pregnancy. On average its readings were within 2.1 bpm of the medical Doppler ultrasound. Its accuracy was slightly diminished for patients with high body mass indexes, though those readings were still within normal limits. Because an irregular fetal heartbeat is often an emergency, DopFone was not tested on patients with irregularities.听

Next, the team plans to gather more data outside a lab to better train the model. Eventually they want to deploy it as a publicly available app.

鈥淭his women鈥檚 health space is often overlooked,鈥� Garg said. 鈥淪o I want to focus on accessible alternatives that can be available to people in low resource areas, whether that鈥檚 here in the U.S. or in other countries. Because health belongs to everyone.鈥�

Co-authors include , a UW graduate student in electrical and computer engineering; and , both OB/GYNs in UW Medicine鈥檚聽 maternal-fetal medicine division; and , a UW assistant professor in the Allen School. , a UW professor in the Allen School and in electrical and computer engineering, and of the Georgia Institute of Technology, were senior authors. This research was funded by the UW Gift Fund.听

For more information, contact Garg at pgarg70@uw.edu.

Keeping up with the latest research is vital for scientists, but given that are published every year, that can prove difficult. Artificial intelligence systems show promise for quickly synthesizing seas of information, but they still tend to make things up, or 鈥渉allucinate.鈥澛�

For instance, when a team led by researchers at the 痳豆在线 and , or Ai2, studied a recent OpenAI model, , they found it fabricated 78-90% of its research citations. And general-purpose AI models like ChatGPT often can鈥檛 access papers that were published after their training data was collected.听

So the UW and Ai2 research team built OpenScholar, an open-source AI model designed specifically to synthesize current scientific research. The team also created the first large, multi-domain for evaluating how well models can synthesize and cite scientific research. In tests, OpenScholar cited sources as accurately as human experts, and 16 scientists preferred its response to those written by subject experts 51% of the time.听

The team Feb. 4 in Nature. The project鈥檚 are publicly available and free to use.

鈥淎fter we started this work, we put the demo online and quickly, we got a lot of queries, far more than we鈥檇 expected,鈥� said senior author , a UW associate professor in the Paul G. Allen School of Computer Science & Engineering and senior director at Ai2. 鈥淲hen we started looking through the responses we realized our colleagues and other scientists were actively using OpenScholar. It really speaks to the need for this sort of open-source, transparent system that can synthesize research.鈥�

Researchers trained the model and then created a set of 45 million scientific papers for OpenScholar to pull from to ground its answers in established research. They coupled this with a technique called “,鈥� which lets the model search for new sources, incorporate them and cite them after it鈥檚 been trained.听

鈥淓arly on we experimented with using an AI model with Google鈥檚 search data, but we found it wasn鈥檛 very good on its own,鈥� said lead author , a research scientist at Ai2 who completed this research as a UW doctoral student in the Allen School. 鈥淚t might cite some research papers that weren鈥檛 the most relevant, or cite just one paper, or pull from a blog post randomly. We realized we needed to ground this in scientific papers. We then made the system flexible so that it could incorporate emerging research through results.鈥澛�

To test their system, the team created ScholarQABench, a benchmark against which to test systems on scientific search. They gathered 3,000 queries and 250 longform answers written by experts in computer science, physics, biomedicine and neuroscience.听

鈥淎I is getting better and better at real world tasks,鈥� Hajishirzi said. 鈥淏ut the big question ultimately is whether we can trust that its answers are correct.鈥�

The team compared OpenScholar against other state-of-the-art AI models, such as OpenAI鈥檚 GPT-4o and two models from Meta. ScholarQABench automatically evaluated AI models鈥� answers on metrics such as their accuracy, writing quality and relevance.听

OpenScholar outperformed all the systems it was tested against. The team had 16 scientists review answers from the models and compare them with human-written responses. The scientists preferred OpenScholar answers to human answers 51% of the time, but when they combined OpenScholar citation methods and pipelines with GPT-4o (a much bigger model), the scientists preferred the AI written answers to human answers 70% of the time. They picked answers from GPT-4o on its own only 32% of the time.

鈥淪cientists see so many papers coming out every day that it鈥檚 impossible to keep up,鈥� Asai said. 鈥淏ut the existing AI systems weren鈥檛 designed for scientists鈥� specific needs. We鈥檝e already seen a lot of scientists using OpenScholar and because it鈥檚 open-source, others are building on this research and already improving on our results. We鈥檙e working on a followup model, , which builds on OpenScholar鈥檚 findings and performs multi-step search and information gathering to produce more comprehensive responses.鈥澛�

Other co-authors include , , , all UW doctoral students in the Allen School; , a UW professor emeritus in the Allen School and general manager and chief scientist at Ai2; , a UW postdoc in the Allen School and postdoc at Ai2; , a UW professor in the Allen School; , a UW assistant professor in

the Allen School; Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D鈥橝rcy, David Wadden, Matt Latzke, Jenna Sparks and Jena D. Hwang of Ai2; Wen-tau Yih of Meta; Minyang Tian, Shengyan Liu, Hao Tong and Bohao Wu of University of Illinois Urbana-Champaign; Pan Ji of University of North Carolina; Yanyu Xiong of Stanford University; and Graham Neubig of Carnegie Mellon University.

For more information, contact Asai at akaria@allenai.org and Hajishirzi at hannaneh@cs.washington.edu.