Aurelia Butler
More stories
- in Heart
Air pollution made an impression on Monet and other 19th century painters
The 19th century landscape paintings hanging in London’s Tate Britain museum looked awfully familiar to climate physicist Anna Lea Albright. Artist Joseph Mallord William Turner’s signature way of shrouding his vistas in fog and smoke reminded Albright of her own research tracking air pollution.“I started wondering if there was a connection,” says Albright, who had been visiting the museum on a day off from the Laboratory for Dynamical Meteorology in Paris. After all, Turner — a forerunner of the impressionist movement — was painting as Britain’s industrial revolution gathered steam, and a growing number of belching manufacturing plants earned London the nickname “The Big Smoke.”
Turner’s early works, such as his 1814 painting “Apullia in Search of Appullus,” were rendered in sharp details. Later works, like his celebrated 1844 painting “Rain, Steam and Speed — the Great Western Railway,” embraced a dreamier, fuzzier aesthetic.
Science News headlines, in your inbox
Headlines and summaries of the latest Science News articles, delivered to your email inbox every Thursday.
Thank you for signing up!
There was a problem signing you up.
Perhaps, Albright thought, this burgeoning painting style wasn’t a purely artistic phenomenon. Perhaps Turner and his successors painted exactly what they saw: their environs becoming more and more obscured by smokestack haze.
To find out how much realism there is in impressionism, Albright teamed up with Harvard University climatologist Peter Huybers, who’s an expert in reconstructing pollution before instruments existed to closely track air quality. Their analysis of nearly 130 paintings by Turner, Paris-based impressionist Claude Monet and several others tells a tale of two modernizing cities.
Low contrast and whiter hues are hallmarks of the impressionist style. They are also hallmarks of air pollution, which can affect how a distant scene looks to the naked eye. Tiny airborne particles, or aerosols, can absorb or scatter light. That makes the bright parts of objects appear dimmer while also shifting the entire scene’s color toward neutral white.
The artworks that Albright and Huybers investigated, which span from the late 1700s to the early 1900s, decrease in contrast as the 19th century progresses. That trend tracks with an increase in air pollution, estimated from historical records of coal sales, Albright and Huybers report in the Feb. 7 Proceedings of the National Academy of Sciences.
“Our results indicate that [19th century] paintings capture changes in the optical environment associated with increasingly polluted atmospheres during the industrial revolution,” the researchers write.
Albright and Huybers distinguished art from aerosol by first using a mathematical model to analyze the contrast and color of 60 paintings that Turner made between 1796 and 1850 as well as 38 Monet works from 1864 to 1901. They then compared the findings to sulfur dioxide emissions over the century, estimated from the trend in the annual amount of coal sold and burned in London and Paris. When sulfur dioxide reacts with molecules in the atmosphere, aerosols form.
The early works of British painter Joseph Mallord William Turner, such as “Apullia in Search of Appullus,” left, painted in 1814, were rendered in sharp details. His later works, like “Rain, Steam and Speed — the Great Western Railway,” right, painted in 1844, embraced a dreamier aesthetic. The decrease in contrast between the paintings tracks with increasing air pollution from the industrial revolution, researchers say.From left: Apullia in Search of Appullus vide Ovid, Joseph Mallord William Turner/The Tate Collection (CC BY-NC-ND 3.0); World History Archive/Alamy Stock Photo
As sulfur dioxide emissions increased over time, the amount of contrast in both Turner’s and Monet’s paintings decreased. However, paintings of Paris that Monet made from 1864 to 1872 have much higher contrast than Turner’s last paintings of London made two decades earlier.
The difference, Albright and Huybers say, can be attributed to the much slower start of the industrial revolution in France. Paris’ air pollution level around 1870 was about what London’s was when Turner started painting in the early 1800s. It confirms that the similar progression in their painting styles can’t be chalked up to coincidence, but is guided by air pollution, the pair conclude.
The researchers also analyzed the paintings’ visibility, or the distance at which an object can be clearly seen. Before 1830, the visibility in Turner’s paintings averaged about 25 kilometers, the team found. Paintings made after 1830 had an average visibility of about 10 kilometers. Paintings made by Monet in London around 1900, such as “Charing Cross Bridge,” have a visibility of less than five kilometers. That’s similar to estimates for modern-day megacities such as Delhi and Beijing, Albright and Huybers say.
To strengthen their argument, the researchers also analyzed 18 paintings from four other London- and Paris-based impressionists. Again, as outdoor air pollution increased over time, the contrast and visibility in the paintings decreased, the team found. What’s more, the decrease seen in French paintings lagged behind the decrease seen in British ones.
Overall, air pollution can explain about 61 percent of contrast differences between the paintings, the researchers calculate. In that respect, “different painters will paint in a similar way when the environment is similar,” Albright says. “But I don’t want to overstep and say: Oh, we can explain all of impressionism.” More
New method creates material that could create the next generation of solar cells
Perovskites, a family of materials with unique electric properties, show promise for use in a variety fields, including next-generation solar cells. A Penn State-led team of scientists created a new process to fabricate large perovskite devices that is more cost- and time-effective than previously possible and that they said may accelerate future materials discovery.
“This method we developed allows us to easily create very large bulk samples within several minutes, rather than days or weeks using traditional methods,” said Luyao Zheng, a postdoctoral researcher in the Department of Materials Science at Penn State and lead author on the study. “And our materials are high quality — their properties can compete with single-crystal perovskites.”
The researchers used a sintering method called the electrical and mechanical field-assisted sintering technique (EM-FAST) to create the devices. Sintering is a commonly used process to compress fine powders into a solid mass of material using heat and pressure.
A typical process for making perovskites involves wet chemistry — the materials are liquefied in a solvent solution and then solidified into thin films. These materials have excellent properties, but the approach is expensive and inefficient for creating large perovskites and the solvents used may be toxic, the scientists said.
“Our technique is the best of both worlds,” said Bed Poudel, a researcher professor at Penn State and a co-author. “We get single-crystal-like properties, and we don’t have to worry about size limitations or any contamination or yield of toxic materials.”
Because it uses dry materials, the EM-FAST technique opens the door to include new dopants, ingredients added to tailor device properties, that are not compatible with the wet chemistry used to make thin films, potentially accelerating the discovery of new materials, the scientists said.“This opens up possibilities to design and develop new classes of materials, including better thermoelectric and solar materials, as well as X- and γ-ray detectors,” said Amin Nozariasbmarz, assistant research professor at Penn State and a co-author. “Some of the applications are things we already know, but because this is a new technique to make new halide perovskite materials with controlled properties, structures, and compositions, maybe there is room in the future for new breakthroughs to come from that.”
In addition, the new process allows for layered materials — one powder underneath another — to create designer compositions. In the future, manufactures could design specific devices and then directly print them from dry powders, the scientists said.
“We anticipate this FAST perovskite would open another dimension for high throughput material synthesis, future manufacturing directly printing devices from powder and accelerating the material discovery of new perovskite compositions,” said Kai Wang, an assistant research professor at Penn State and a co-author.
EM-FAST, also known as spark plasma sintering, involves applying electric current and pressure to powders to create new materials. The process has a 100% yield — all the raw ingredients go into the final device, as opposed to 20 to 30% in solution-based processing.
The technique produced perovskite materials at .2 inch per minute, allowing scientists to create quickly create large devices that maintained high performance in laboratory tests. The team reported their findings in the journal Nature Communications.Penn State scientists have long used EM-FAST to create thermoelectric devices. This work represents the first attempt to create perovskite materials with the technique, the scientists said.
“Because of the background we have, we were talking and thought we could change some parameters and try this with perovskites,” Nozariasbmarz said. “And it just opened a door to a new world. This paper is a link to that door — to new materials and new properties.”
Other Penn State researchers on the project were Wenjie Li and Dong Yang, assistant research professors; Ke Wang, staff scientist in the Materials Research Institute; Jungjin Yoon, Tao Ye and Yu Zhang, postdoctoral researchers; Yuchen Hou, doctoral candidate; and Shashank Priya, former associate vice president for research and director of strategic initiatives and professor of materials science and engineering.
Also contributing was Mohan Sanghadasa, U.S. Army Combat Capabilities Development Command Aviation and Missile Center.
Researchers received support from the National Science Foundation Industry-University Research Partnerships’ Center for Energy Harvesting Materials and Systems, U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, Air Force Office of Scientific Research, and Office of Naval Research and Army Research. MoreReaching like an octopus: A biology-inspired model opens the door to soft robot control
Octopus arms coordinate nearly infinite degrees of freedom to perform complex movements such as reaching, grasping, fetching, crawling, and swimming. How these animals achieve such a wide range of activities remains a source of mystery, amazement, and inspiration. Part of the challenge comes from the intricate organization and biomechanics of the internal muscles.
This problem was tackled in a multidisciplinary project led by Prashant Mehta and Mattia Gazzola, professors of mechanical science & engineering at the University of Illinois Urbana-Champaign. As reported in Proceedings of the Royal Society A, the two researchers and their groups have developed a physiologically accurate model of octopus arm muscles. “Our model, the first of its kind, not only provides insight into the biological problem, but a framework for design and control of soft robots going forward,” Mehta said.
The impressive capabilities of octopus arms have long served as an inspiration for the design and control of soft robots. Such soft robots have the potential to perform complex tasks in unstructured environments while operating safely around humans, with applications ranging from agriculture to surgery.
Graduate student Heng-Sheng Chang, the study’s lead author, explained that soft-bodied systems like octopuses’ arms present a major modeling and control challenge. “They are driven by three major internal muscle groups — longitudinal, transverse, and oblique — that cause the arm to deform in several modes — shearing, extending, bending, and twisting,” he said. “This endows the soft muscular arms with significant freedom, unlike their rigid counterparts.”
The team’s key insight was to express the arm musculature using a stored energy function, a concept borrowed from the theory of continuum mechanics. Postdoctoral scholar and corresponding author Udit Halder explained that “The arm rests at the minimum of an energy landscape. Muscle actuations modify the stored energy function, thus shifting the equilibrium position of the arm and guiding the motion.”
Interpreting the muscles using stored energy dramatically simplifies the arm’s control design. In particular, the study outlines an energy-shaping control methodology to compute the necessary muscle activations for solving manipulation tasks such as reaching and grasping. When this approach was numerically demonstrated in the software environment Elastica, This model led to remarkably life-like motion when an octopus arm was simulated in three dimensions. Moreover, according to Halder, “Our work offers mathematical guarantees of performance that are often lacking in alternative approaches, including machine learning.”
“Our work is part of a larger ecosystem of ongoing collaborations at the University of Illinois,” Mehta said. “Upstream, there are biologists who perform experiments on octopuses. Downstream, there are roboticists who are taking these mathematical ideas and applying them to real soft robots.”
Mehta’s and Gazzola’s groups collaborated with Rhanor Gillette, Illinois Professor Emeritus of molecular and integrative physiology, to incorporate observed octopus physiology into their mathematical model for this study. Future work will discuss the biological implications of energy-based control. In addition, the researchers are collaborating with Girish Krishnan, an Illinois professor of industrial & enterprise systems engineering, to incorporate their mathematical ideas into real soft robot design and control. This will not only create a systematic way of controlling soft robots, but will also provide a deeper understanding of their working mechanisms.
This work was part of the CyberOctopus project, a multidisciplinary university research initiative in the University of Illinois’ Coordinated Science Laboratory supported by the Office of Naval Research. MoreUsing the power of artificial intelligence, new open-source tool simplifies animal behavior analysis
A team from the University of Michigan has developed a new software tool to help researchers across the life sciences more efficiently analyze animal behaviors.
The open-source software, LabGym, capitalizes on artificial intelligence to identify, categorize and count defined behaviors across various animal model systems.
Scientists need to measure animal behaviors for a variety of reasons, from understanding all the ways a particular drug may affect an organism to mapping how circuits in the brain communicate to produce a particular behavior.
Researchers in the lab of U-M faculty member Bing Ye, for example, analyze movements and behaviors in Drosophila melanogaster—or fruit flies—as a model to study the development and functions of the nervous system. Because fruit flies and humans share many genes, these studies of fruit flies often offer insights into human health and disease.
“Behavior is a function of the brain. So analyzing animal behavior provides essential information about how the brain works and how it changes in response to disease,” said Yujia Hu, a neuroscientist in Ye’s lab at the U-M Life Sciences Institute and lead author of a Feb. 24 Cell Reports Methods study describing the new software.
But identifying and counting animal behaviors manually is time-consuming and highly subjective to the researcher who is analyzing the behavior. And while a few software programs exist to automatically quantify animal behaviors, they present challenges.“Many of these behavior analysis programs are based on pre-set definitions of a behavior,” said Ye, who is also a professor of cell and developmental biology at the Medical School. “If a Drosophila larva rolls 360 degrees, for example, some programs will count a roll. But why isn’t 270 degrees also a roll? Many programs don’t necessarily have the flexibility to count that, without the user knowing how to recode the program.”
Thinking more like a scientist
To overcome these challenges, Hu and his colleagues decided to design a new program that more closely replicates the human cognition process—that “thinks” more like a scientist would—and is more user-friendly for biologists who may not have expertise in coding. Using LabGym, researchers can input examples of the behavior they want to analyze and teach the software what it should count. The program then uses deep learning to improve its ability to recognize and quantify the behavior.
One new development in LabGym that helps it apply this more flexible cognition is the use of both video data and a so-called “pattern image” to improve the program’s reliability. Scientists use videos of animals to analyze their behavior, but videos involve time series data that can be challenging for AI programs to analyze.To help the program identify behaviors more easily, Hu created a still image that shows the pattern of the animal’s movement by merging outlines of the animal’s position at different timepoints. The team found that combining the video data with the pattern images increased the program’s accuracy in recognizing behavior types.
LabGym is also designed to overlook irrelevant background information and consider both the animal’s overall movement and the changes in position over space and time, much as a human researcher would. The program can also track multiple animals simultaneously.
Species flexibility improves utility
Another key feature of LabGym is its species flexibility, Ye said. While it was designed using Drosophila, it is not restricted to any one species.
“That’s actually rare,” he said. “It’s written for biologists, so they can adapt it to the species and the behavior they want to study without needing any programming skills or high-powered computing.”
After hearing a presentation about the program’s early development, U-M pharmacologist Carrie Ferrario offered to help Ye and his team test and refine the program in the rodent model system she works with.
Ferrario, an associate professor of pharmacology and adjunct associate professor of psychology, studies the neural mechanisms that contribute to addiction and obesity, using rats as a model system. To complete the necessary observation of drug-induced behaviors in the animals, she and her lab members have had to rely largely on hand-scoring, which is subjective and extremely time-consuming.
“I’ve been trying to solve this problem since graduate school, and the technology just wasn’t there, in terms of artificial intelligence, deep learning and computation,” Ferrario said. “This program solved an existing problem for me, but it also has really broad utility. I see the potential for it to be useful in almost limitless conditions to analyze animal behavior.”
The team next plans to further refine the program to improve its performance under even more complex conditions, such as observing animals in nature.
This research was supported by the National Institutes of Health.
In addition to Ye, Hu and Ferrario, study authors are: Alexander Maitland, Rita Ionides, Anjesh Ghimire, Brendon Watson, Kenichi Iwasaki, Hope White and Yitao Xi of the University of Michigan, and Jie Zhou of Northern Illinois University.
Study: LabGym: quantification of user-defined animal behaviors 1 using learning-based holistic assessment (DOI: 10.1016/j.crmeth.2023.100415) (available once embargo lifts) More
