Luckily for astronomers, this bygone era can be observed. Distant light takes time to reach us, and our telescopes can pick up light emitted by galaxies and stars billions of years ago (our universe is 13.8 billion years old). But the details of this chapter in our universe’s history are murky since most of the stars being formed are faint and hidden by dust.

A new Caltech project, called COMAP (CO Mapping Array Project), will offer us a new glimpse into this epoch of galaxy assembly, helping to answer questions about what really caused the universe’s rapid increase in the production of stars.

“Most instruments might see the tip of an iceberg when looking at galaxies from this period,” says Kieran Cleary, the project’s principal investigator and the associate director of Caltech’s Owens Valley Radio Observatory (OVRO). “But COMAP will see what lies underneath, hidden from view.”

The current phase of the project uses a 10.4-meter “Leighton” radio dish at OVRO to study the most common kinds of star-forming galaxies spread across space and time, including those that are too difficult to view in other ways because they are too faint or hidden by dust. The radio observations trace the raw material from which stars are made: cold hydrogen gas. This gas is not easy to pinpoint directly, so instead COMAP measures bright radio signals from carbon monoxide (CO) gas, which is always present along with the hydrogen. COMAP’s radio camera is the most powerful ever built to detect these radio signals.

The first science results from the project have just been published in seven papers in The Astrophysical Journal. Based on observations taken one year into a planned five-year survey, COMAP set upper limits on how much cold gas must be present in galaxies at the epoch being studied, including the ones that are normally too faint and dusty to see. While the project has not yet made a direct detection of the CO signal, these early results demonstrate that it is on track to do so by the end of the initial five-year survey and ultimately will paint the most comprehensive picture yet of the universe’s history of star formation.

“Looking to the future of the project, we aim to use this technique to successively look further and further back in time,” Cleary says. “Starting 4 billion years after the Big Bang, we will keep pushing back in time until we reach the epoch of the first stars and galaxies, a couple of billion years earlier.”

Anthony Readhead, the co-principal investigator and the Robinson Professor of Astronomy, Emeritus, says that COMAP will see the not only the first epoch of stars and galaxies, but also their epic decline. “We will observe star formation rising and falling like an ocean tide,” he says.

COMAP works by capturing blurry radio images of clusters of galaxies over cosmic time rather than sharp images of individual galaxies. This blurriness enables the astronomers to efficiently catch all the radio light coming from a larger pool of galaxies, even the faintest and dustiest ones that have never been seen.

“In this way, we can find the average properties of typical, faint galaxies without needing to know very precisely where any individual galaxy is located,” explains Cleary. “This is like finding the temperature of a large volume of water using a thermometer rather than analyzing the motions of the individual water molecules.”

The project has received funding from the Keck Institute for Space Studies (for critical early technology development) and from the National Science Foundation (NSF), for building the “Pathfinder” early phase of the project and performing the survey. The project is a collaboration between Caltech; the Jet Propulsion Laboratory (JPL), which is managed by Caltech for NASA; New York University; Princeton University; Stanford University; Université de Genève; University of Oslo; The University of Manchester; University of Maryland; University of Miami; and the University of Toronto (including the Canadian Institute for Theoretical Astrophysics and the Dunlap Institute for Astronomy and Astrophysics).

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Materials provided by California Institute of Technology. Original written by Whitney Clavin. Note: Content may be edited for style and length.

Cory Simon, assistant professor of chemical engineering, and Xiaoli Fern, associate professor of computer science, led the project, which involved training a machine learning model to predict whether any proposed new herbicide, fungicide or insecticide would be toxic to honey bees based on the compound’s molecular structure.

The findings, featured on the cover of The Journal of Chemical Physics in a special issue, “Chemical Design by Artificial Intelligence,” are important because many fruit, nut, vegetable and seed crops rely on bee pollination.

Without bees to transfer the pollen needed for reproduction, almost 100 commercial crops in the United States would vanish. Bees’ global economic impact is annually estimated to exceed $100 billion.

“Pesticides are widely used in agriculture, which increase crop yield and provide food security, but pesticides can harm off-target species like bees,” Simon said. “And since insects, weeds, etc. eventually evolve resistance, new pesticides must continually be developed, ones that don’t harm bees.”

Graduate students Ping Yang and Adrian Henle used honey bee toxicity data from pesticide exposure experiments, involving nearly 400 different pesticide molecules, to train an algorithm to predict if a new pesticide molecule would be toxic to honey bees.

“The model represents pesticide molecules by the set of random walks on their molecular graphs,” Yang said.

A random walk is a mathematical concept that describes any meandering path, such as on the complicated chemical structure of a pesticide, where each step along the path is decided by chance, as if by coin tosses.

Imagine, Yang explains, that you’re out for an aimless stroll along a pesticide’s chemical structure, making your way from atom to atom via the bonds that hold the compound together. You travel in random directions but keep track of your route, the sequence of atoms and bonds that you visit. Then you go out on a different molecule, comparing the series of twists and turns to what you’ve done before.

“The algorithm declares two molecules similar if they share many walks with the same sequence of atoms and bonds,” Yang said. “Our model serves as a surrogate for a bee toxicity experiment and can be used to quickly screen proposed pesticide molecules for their toxicity.”

The National Science Foundation supported this research.

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Materials provided by Oregon State University. Original written by Steve Lundeberg. Note: Content may be edited for style and length.

The work, which appears in the journal Proceedings of the National Academy of Sciences (PNAS), is among the latest to uncover how artificial intelligence (AI) can alter our perceptions and actions.

“There is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded,” says Madalina Vlasceanu, a postdoctoral fellow in New York University’s Department of Psychology and the paper’s lead author. “As a consequence, their use by humans may result in the propagation, rather than reduction, of existing disparities.”

“These findings call for a model of ethical AI that combines human psychology with computational and sociological approaches to illuminate the formation, operation, and mitigation of algorithmic bias,” adds author David Amodio, a professor in NYU’s Department of Psychology and the University of Amsterdam.

Technology experts have expressed concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are ingrained.

“Certain 1950s ideas about gender are actually still embedded in our database systems,” Meredith Broussard, author of Artificial Unintelligence: How Computers Misunderstand the World and a professor at NYU’s Arthur L. Carter Journalism Institute, told the Markup earlier this year.

The use of AI by human decision makers may result in the propagation, rather than reduction, of existing disparities, Vlasceanu and Amodio say.

To address this possibility, they conducted studies that sought to determine whether the degree of inequality within a society relates to patterns of bias in algorithmic output and, if so, whether exposure to such output could influence human decision makers to act in accordance with these biases.

First, they drew from the Global Gender Gap Index (GGGI), which contains rankings of gender inequality for more than 150 countries. The GGGI represents the magnitude of gender inequality in economic participation and opportunity, educational attainment, health and survival, and political empowerment in 153 nations, thereby providing societal-level gender inequality scores for each country.

Next, to assess possible gender bias in search results, or algorithmic output, they examined whether words that should refer with equal probability to a man or a woman, such as “person,” “student,” or “human,” are more often assumed to be a man. Here, they conducted Google image searches for “person” within a nation (in its dominant local language) across 37 countries. The results showed that the proportion of male images yielded from these searches was higher in nations with greater gender inequality, revealing that algorithmic gender bias tracks with societal gender inequality.

The researchers repeated the study three months later with a sample of 52 countries, including 31 from the first study. The results were consistent with those from the initial study, reaffirming societal-level gender disparities are reflected in algorithmic output (i.e. internet searches).

Vlasceanu and Amodio then sought to determine whether exposure to such algorithmic outputs — search-engine results — can shape people’s perceptions and decisions in ways consistent with pre-existing societal inequalities.

To do so, they conducted a series of experiments involving a total of nearly 400 female and male U.S. participants.

In these experiments, the participants were told they were viewing Google image search results of four professions they were likely to be unfamiliar with: chandler, draper, peruker, and lapidary. The gender composition of each profession’s image set was selected to represent the Google image search results for the keyword “person” for nations with high global gender inequality scores (roughly 90% men to 10% women in Hungary or Turkey) as well as those with low global gender inequality scores (roughly 50% men to 50% women in Iceland or Finland) from the 52-nation study above. This allowed the researchers to mimic the results of internet searches in different countries.

Prior to viewing the search results, the participants provided prototypicality judgements regarding each profession (e.g., “Who is more likely to be a peruker, a man or a woman?”), which served as a baseline assessment of their perceptions. Here, the participants, both female and male, judged members of these professions as more likely to be a man than a woman.

However, when asked these same questions after viewing the image search results, the participants in the low-inequality conditions reversed their male-biased prototypes relative to the baseline assessment. By contrast, those in the high-inequality condition maintained their male-biased perceptions, thereby reinforcing their perceptions of these prototypes.

The researchers then assessed how biases driven by internet searches could potentially influence hiring decisions. To do so, they asked participants to judge the likelihood that a man or woman would be hired in each profession (“What type of person is most likely to be hired as a peruker?”) and, when presented with images of two job candidates (one woman and one man) for a position in that profession, to make their own hiring choice (e.g., “Choose one of these applicants for a job as a peruker.”).

Consistent with the other experimental results, exposure to images in the low-inequality condition produced more egalitarian judgments of male vs. female hiring tendencies within a profession and a higher likelihood of choosing a woman job candidate compared with exposure to image sets in the high-inequality condition.

“These results suggest a cycle of bias propagation between society, AI, and users,” Vlasceanu and Amodio write, adding that the “findings demonstrate that societal levels of inequality are evident in internet search algorithms and that exposure to this algorithmic output can lead human users to think and potentially act in ways that reinforce the societal inequality.”

The study was funded by the NYU Alliance for Public Interest Technology and the Netherlands Organization for Scientific Research (VICI 016.185.058).

The study is published in Nature Materials.

In a previous study, the University of Minnesota-led team found that cells have the ability to sense the stiffness of their environment — which ranges from stiff (bone tissue) to soft (fatty tissue) to medium stiffness (muscle tissue) — and their ability to move is dependent upon that environment. Their research showed that the cells can have a “sweet spot” of stiffness, that isn’t too hard or too soft, in which they have better traction and can move faster.

In this study, the researchers found that not only does the stiffness of the environment impact the speed at which cells move, but it also affects the direction in which they move.

For many years, scientists have thought that cells would always gravitate toward a stiffer environment, but the University of Minnesota researchers observed for the first time that cells can actually move toward a “sweet spot” that’s more in the middle.

“This discovery challenges the current thinking in the field, which is that cells only move toward stiffer environments,” said David Odde, a professor in the University of Minnesota Twin Cities Department of Biomedical Engineering and senior author of the study. “I think that this finding will change how people think about this phenomenon. Our mathematical model predicted, and we’ve shown through experiments, that cells actually can move toward the softer side.”

During the study, Odde and his team looked at both brain cancer and breast cancer cells. They placed cells between two environments — a stiffer region and a softer region — and observed where they accumulated.

The research team also found that some cells, like the breast cancer cells they studied, have a feedback mechanism that causes them to grip more strongly onto stiffer environments, which explains why many previous studies showed cells moving to the stiffer side. However, if you turn that mechanism off genetically, the cells will then gravitate more toward the middle.

“We’re basically decoding how cancer cells invade tissue,” Odde said. “They don’t just move randomly. They actually have particular ways in which they like to move, and if we can understand that, we may be better able to trip them up.”

The next step for the researchers is to use this information to build a simulator that shows how cancer cells move through an entire tumor, which will help them better predict cells’ movements based on their environments.

This research was supported primarily by the National Institutes of Health and the National Science Foundation Science and Technology Center for Engineering Mechanobiology with additional support from the University of Turku Doctoral Programme in Molecular Life Sciences, the Company of Biologists Travelling Fellowship, the Finnish Cultural Foundation, the Academy of Finland, the Sigrid Juselius Foundation, the Finnish Cancer Organization, the National Natural Science Foundation of China, the Natural Science Basic Research Plan in Shaanxi Province of China, the Shaanxi Province Youth Talent Support Program, and the Young Talent Support Plan of Xi’an Jiaotong University.

In addition to Odde, the research team included University of Minnesota Department of Biomedical Engineering researchers Jay Hou, Ghaidan Shamsan, Benjamin Fuller, and Jesse Kasim; University of Minnesota Twin Cities Department of Chemistry researchers Keun-Young Park, M. Mohsen Mahmoodi, and Professor Mark Distefano; University of Turku, Finland, researchers Aleksi Isomursu, Mathilde Mathieu, and Professor Johanna Ivaska; and Xi’an Jiaotong University researchers Bo Cheng, Tian Jian Lu, Guy Genin, Feng Xu, and Professor Min Lin.