Daily Archives: January 24, 2025

State proposal would remove ‘climate change’ and ‘evolution’ from Iowa science standards

By Amanda Rooker

Click here for updates on this story

    DES MOINES, Iowa (KCCI) — Some Iowans are raising concern about new proposed state science standards. The Iowa Department of Education’s proposal would remove the phrase “climate change” and some other scientific words and concepts from state standards.

Nearly 40 people came in person to speak at Thursday’s final public feedback forum at the Grimes State Office Building in Des Moines. More spoke over Zoom. Teachers, scientists, school administrators, education consultants, parents, employers and a middle school student each had five minutes to share their thoughts.

Some drove more than two hours to share their perspective with the Iowa Department of Education. Every person who spoke Thursday expressed concern about the removal of certain words from Iowa’s science standards.

In Iowa’s new proposed standards, any mention of “climate change” was changed to the phrase “climate trends” and all references to human impact on climate change were removed. Although the standards would still include the concept of biological change over time, the word “evolution” was erased.

An Iowa Department of Education Science Standards Revision Team, which included 37 members with experience in education and science, put together the proposed standards. Drake University science professor Jerrid Kruse was on that revision team.

At Thursday’s forum, Kruse said the version the revision team submitted was different than the state’s proposed standards. He said the revision team was told their proposal would be “copy-edited,” but he was not aware that the state would remove the words “climate change” or “evolution” from their recommended standards.

“I do not know how or when or what or who made the changes later that we’re all concerned about,” Kruse said during Thursday’s forum. “I think we’re all rightfully concerned about those things.”

After Thursday’s meeting, KCCI asked the Iowa Department of Education consultant who led the meeting and the department’s general counsel why the changes were made. Both declined to answer and referred us to a spokesperson for the department.

As of 10:30 p.m. on Thursday, the spokesperson had not responded to KCCI’s email which asked why the changes were made and for the names and credentials of who made the changes.

“I have not heard a single word of support for those changes and so if they are going to truly represent us, the people, then these changes need to be made back,” Kruse said.

The Iowa Department of Education will be collecting public feedback online through this link until Feb. 3. They said Thursday that they will compile the in-person and digital feedback and will consider it as they work to finalize the state science standards in March.

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Scientists Identify Bacteria That Can Break Down Some PFAS and Their Byproducts

Per- and polyfluoroalkyl substances, or PFAS, have taken on the nickname “forever chemicals” for their inability to break down in the environment. These chemicals, which are found in everything from food packaging to camping gear, are popular for their stain-, moisture- and grease-resistant properties, but they have been found to accumulate in the environment and in human bodies. 

But now, researchers may have cracked a way to break down these chemicals and even some of their byproducts, which can be toxic, by using strains of bacteria.

A team of scientists led by University of Buffalo researchers found that the bacteria Labrys portucalensis F11 was effective at breaking down at least three types of PFAS, including the most common forever chemical, perfluorooctane sulfonic acid (PFOS), as well as 5:3 fluorotelomer carboxylic acid (FTCA) and 6:2 fluorotelomer sulfonate (FTS).

Professor Diana Aga, the study’s corresponding author, says the bacteria could one day be deployed to break down PFAS in wastewater treatment plants. Meredith Forrest Kulwicki / University at Buffalo

The bacteria was the most effective at breaking down PFOS, a chemical that was designated as hazardous by the U.S. Environmental Protection Agency (EPA) in 2024. It degraded more than 90% of the compound over a 100-day exposure and removed up to 96% of the PFOS after 194 days. During the first 100 days, the bacteria broke down as much as 58% of FTCA and 21% of FTS. The scientists published their findings in the journal Science of The Total Environment.

“The bond between carbon and fluorine atoms in PFAS is very strong, so most microbes cannot use it as an energy source,” Diana Aga, corresponding author of the study, said in a statement. “The F11 bacterial strain developed the ability to chop away the fluorine and eat the carbon.”

In addition to breaking down the PFAS, the bacteria also broke down the metabolites that occur after the PFAS degradation, with Labrys portucalensis F11 even breaking down or fully removing fluorine in some of the study results. 

“Many previous studies have only reported the degradation of PFAS, but not the formation of metabolites. We not only accounted for PFAS byproducts but found some of them continued to be further degraded by the bacteria,” explained Mindula Wijayahena, first author of the study and a Ph.D. student in Aga’s lab.

Mindula Wijayahena, the study’s first author, analyzed the samples containing PFAS and the bacteria following incubation in Portugal. Meredith Forrest Kulwicki / University at Buffalo

This particular bacteria strain has been previously revealed to degrade fluorobenzene, a flammable and hazardous compound sometimes found in insecticides.

The discovery offers a novel method for cleaning up PFAS; other methods have primarily focused on adsorbing and removing the PFAS, but the bacteria could help break down these chemicals and minimize the amount of time they spend in the environment.

A 2024 study uncovered a way to track PFAS contamination to the source, and a separate study published in 2023 a potential water treatment that would use adsorbing materials and electro- and photochemical processes to destroy PFAS contaminants in drinking water supplies. Yet another separate study published in 2022 found a plant-based material that could help adsorb PFAS, which would then be digested by fungus.

The study authors using Labrys portucalensis F11 for PFAS metabolization will continue their research, noting that although the bacteria did break down the PFAS, it took nearly 200 days, and that was without other food sources present.

“We want to investigate the impact of placing alternative carbon sources alongside the PFAS. However, if that carbon source is too abundant and easy to degrade, the bacteria may not need to touch the PFAS at all,” Aga said. “We need to give the F11 colonies enough food to grow, but not enough food that they lose the incentive to convert PFAS into a usable energy source.”

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Scientists Identify Bacteria That Can Break Down Some PFAS and Their Byproducts

Per- and polyfluoroalkyl substances, or PFAS, have taken on the nickname “forever chemicals” for their inability to break down in the environment. These chemicals, which are found in everything from food packaging to camping gear, are popular for their stain-, moisture- and grease-resistant properties, but they have been found to accumulate in the environment and in human bodies. 

But now, researchers may have cracked a way to break down these chemicals and even some of their byproducts, which can be toxic, by using strains of bacteria.

A team of scientists led by University of Buffalo researchers found that the bacteria Labrys portucalensis F11 was effective at breaking down at least three types of PFAS, including the most common forever chemical, perfluorooctane sulfonic acid (PFOS), as well as 5:3 fluorotelomer carboxylic acid (FTCA) and 6:2 fluorotelomer sulfonate (FTS).

Professor Diana Aga, the study’s corresponding author, says the bacteria could one day be deployed to break down PFAS in wastewater treatment plants. Meredith Forrest Kulwicki / University at Buffalo

The bacteria was the most effective at breaking down PFOS, a chemical that was designated as hazardous by the U.S. Environmental Protection Agency (EPA) in 2024. It degraded more than 90% of the compound over a 100-day exposure and removed up to 96% of the PFOS after 194 days. During the first 100 days, the bacteria broke down as much as 58% of FTCA and 21% of FTS. The scientists published their findings in the journal Science of The Total Environment.

“The bond between carbon and fluorine atoms in PFAS is very strong, so most microbes cannot use it as an energy source,” Diana Aga, corresponding author of the study, said in a statement. “The F11 bacterial strain developed the ability to chop away the fluorine and eat the carbon.”

In addition to breaking down the PFAS, the bacteria also broke down the metabolites that occur after the PFAS degradation, with Labrys portucalensis F11 even breaking down or fully removing fluorine in some of the study results. 

“Many previous studies have only reported the degradation of PFAS, but not the formation of metabolites. We not only accounted for PFAS byproducts but found some of them continued to be further degraded by the bacteria,” explained Mindula Wijayahena, first author of the study and a Ph.D. student in Aga’s lab.

Mindula Wijayahena, the study’s first author, analyzed the samples containing PFAS and the bacteria following incubation in Portugal. Meredith Forrest Kulwicki / University at Buffalo

This particular bacteria strain has been previously revealed to degrade fluorobenzene, a flammable and hazardous compound sometimes found in insecticides.

The discovery offers a novel method for cleaning up PFAS; other methods have primarily focused on adsorbing and removing the PFAS, but the bacteria could help break down these chemicals and minimize the amount of time they spend in the environment.

A 2024 study uncovered a way to track PFAS contamination to the source, and a separate study published in 2023 a potential water treatment that would use adsorbing materials and electro- and photochemical processes to destroy PFAS contaminants in drinking water supplies. Yet another separate study published in 2022 found a plant-based material that could help adsorb PFAS, which would then be digested by fungus.

The study authors using Labrys portucalensis F11 for PFAS metabolization will continue their research, noting that although the bacteria did break down the PFAS, it took nearly 200 days, and that was without other food sources present.

“We want to investigate the impact of placing alternative carbon sources alongside the PFAS. However, if that carbon source is too abundant and easy to degrade, the bacteria may not need to touch the PFAS at all,” Aga said. “We need to give the F11 colonies enough food to grow, but not enough food that they lose the incentive to convert PFAS into a usable energy source.”

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Education and Science Minister Valchev Meets with EU Commissioner Zaharieva

Attracting and retaining scientists and researchers within the European research space and fostering the development of European talent topped the agenda of Friday’s meeting between Education and Science Minister Krasimir Valchev and the European Commissioner for Startups, Research and Innovation, Ekaterina Zaharieva, the Ministry said.
The two discussed ways to boost Bulgaria’s participation in the EU Horizon Europe funding programme for research and innovation, where there has been a significant increase in signed contracts and attracted funding with Bulgarian participation. Valchev emphasized that one of his key priorities would be to continue strengthening and expanding Bulgarian projects within the EU framework programmes for research and innovation.
For her part, Zaharieva congratulated the Education and Science Ministry on establishing a liaison office in Brussels, which supports the involvement of Bulgarian scientific organizations and businesses in European science and innovation programmes. The office provides key information and facilitates links between Bulgarian participants and key EU institutions and expert networks.
Valchev outlined some of the education and science priorities: “One of our main goals is to reform the curricula so that students develop practical skills and are relieved from excessive factual information.” In recent years, there has been a positive trend, with many Bulgarian winners of Olympiads and holders of national diplomas for academic excellence continuing their higher education in Bulgaria. The Education Minister also announced that a programme was being developed to support schools which train students for Olympiads.
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Education and Science Minister Valchev Meets with EU Commissioner Zaharieva

Attracting and retaining scientists and researchers within the European research space and fostering the development of European talent topped the agenda of Friday’s meeting between Education and Science Minister Krasimir Valchev and the European Commissioner for Startups, Research and Innovation, Ekaterina Zaharieva, the Ministry said.
The two discussed ways to boost Bulgaria’s participation in the EU Horizon Europe funding programme for research and innovation, where there has been a significant increase in signed contracts and attracted funding with Bulgarian participation. Valchev emphasized that one of his key priorities would be to continue strengthening and expanding Bulgarian projects within the EU framework programmes for research and innovation.
For her part, Zaharieva congratulated the Education and Science Ministry on establishing a liaison office in Brussels, which supports the involvement of Bulgarian scientific organizations and businesses in European science and innovation programmes. The office provides key information and facilitates links between Bulgarian participants and key EU institutions and expert networks.
Valchev outlined some of the education and science priorities: “One of our main goals is to reform the curricula so that students develop practical skills and are relieved from excessive factual information.” In recent years, there has been a positive trend, with many Bulgarian winners of Olympiads and holders of national diplomas for academic excellence continuing their higher education in Bulgaria. The Education Minister also announced that a programme was being developed to support schools which train students for Olympiads.
/RY/

At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate. “What is happening within the field is revolutionary,” says School of Earth and Atmospheric Sciences Associate Chair and Professor Annalisa Bracco, adding that because many climate-related processes — from ocean currents to melting glaciers and weather patterns — can be described with physical equations, these advancements have the potential to help us understand and predict climate in critically important ways. Bracco is the lead author of a new review paper providing a comprehensive look at the intersection of AI and climate physics.The result of an international collaboration between Georgia Tech’s Bracco, Julien Brajard (Nansen Environmental and Remote Sensing Center), Henk A. Dijkstra (Utrecht University), Pedram Hassanzadeh (University of Chicago), Christian Lessig (European Centre for Medium-Range Weather Forecasts), and Claire Monteleoni (University of Colorado Boulder), the paper, ‘Machine learning for the physics of climate,’ was recently published in Nature Reviews Physics. “One of our team’s goals was to help people think deeply on how climate science and AI intersect,” Bracco shares. “Machine learning is allowing us to study the physics of climate in a way that was previously impossible. Coupled with increasing amounts of data and observations, we can now investigate climate at scales and resolutions we’ve never been able to before.”Connecting hidden dotsThe team showed that ML is driving change in three key areas: accounting for missing observational data, creating more robust climate models, and enhancing predictions, especially in weather forecasting. However, the research also underscores the limits of AI — and how researchers can work to fill those gaps.“Machine learning has been fantastic in allowing us to expand the time and the spatial scales for which we have measurements,” says Bracco, explaining that ML could help fill in missing data points — creating a more robust record for researchers to reference. However, like patching a hole in a shirt, this works best when the rest of the material is intact.“Machine learning can extrapolate from past conditions when observations are abundant, but it can’t yet predict future trends or collect the data we need,” Bracco adds. “To keep advancing, we need scientists who can determine what data we need, collect that data, and solve problems.”Modeling climate, predicting weatherMachine learning is often used when improving climate models that can simulate changing systems like our atmosphere, oceans, land, biochemistry, and ice. “These models are limited because of our computing power, and are run on a three-dimensional grid,” Bracco explains: below the grid resolution, researchers need to approximate complex physics with simpler equations that computers can solve quickly, a process called ‘parameterization’.Machine learning is changing that, offering new ways to improve parameterizations, she says. “We can run a model at extremely high resolutions for a short time, so that we don’t need to parameterize as many physical processes — using machine learning to derive the equations that best approximate what is happening at small scales,” she explains. “Then we can use those equations in a coarser model that we can run for hundreds of years.”While a full climate model based solely on machine learning may remain out of reach, the team found that ML is advancing our ability to accurately predict weather systems and some climate phenomena like El Niño. Previously, weather prediction was based on knowing the starting conditions — like temperature, humidity, and barometric pressure — and running a model based on physics equations to predict what might happen next. Now, machine learning is giving researchers the opportunity to learn from the past. “We can use information on what has happened when there were similar starting conditions in previous situations to predict the future without solving the underlying governing equations,” Bracco says. “And all while using orders-of-magnitude less computing resources.”The human connectionBracco emphasizes that while AI and ML play a critical role in accelerating research, humans are at the core of progress. “I think the in-person collaboration that led to this paper is, in itself, a testament to the importance of human interaction,” she says, recalling that the research was the result of a workshop organized at the Kavli Institute for Theoretical Physics — one of the team’s first in-person discussions after the Covid-19 pandemic.“Machine learning is a fantastic tool — but it’s not the solution to everything,” she adds. “There is also a real need for human researchers collecting high-quality data, and for interdisciplinary collaboration across fields. I see this as a big challenge, but a great opportunity for computer scientists and physicists, mathematicians, biologists, and chemists to work together.”Funding: National Science Foundation, European Research Council, Office of Naval Research, US Department of Energy, European Space Agency, Choose France Chair in AI.DOI: https://doi.org/10.1038/s42254-024-00776-3

At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate. “What is happening within the field is revolutionary,” says School of Earth and Atmospheric Sciences Associate Chair and Professor Annalisa Bracco, adding that because many climate-related processes — from ocean currents to melting glaciers and weather patterns — can be described with physical equations, these advancements have the potential to help us understand and predict climate in critically important ways. Bracco is the lead author of a new review paper providing a comprehensive look at the intersection of AI and climate physics.The result of an international collaboration between Georgia Tech’s Bracco, Julien Brajard (Nansen Environmental and Remote Sensing Center), Henk A. Dijkstra (Utrecht University), Pedram Hassanzadeh (University of Chicago), Christian Lessig (European Centre for Medium-Range Weather Forecasts), and Claire Monteleoni (University of Colorado Boulder), the paper, ‘Machine learning for the physics of climate,’ was recently published in Nature Reviews Physics. “One of our team’s goals was to help people think deeply on how climate science and AI intersect,” Bracco shares. “Machine learning is allowing us to study the physics of climate in a way that was previously impossible. Coupled with increasing amounts of data and observations, we can now investigate climate at scales and resolutions we’ve never been able to before.”Connecting hidden dotsThe team showed that ML is driving change in three key areas: accounting for missing observational data, creating more robust climate models, and enhancing predictions, especially in weather forecasting. However, the research also underscores the limits of AI — and how researchers can work to fill those gaps.“Machine learning has been fantastic in allowing us to expand the time and the spatial scales for which we have measurements,” says Bracco, explaining that ML could help fill in missing data points — creating a more robust record for researchers to reference. However, like patching a hole in a shirt, this works best when the rest of the material is intact.“Machine learning can extrapolate from past conditions when observations are abundant, but it can’t yet predict future trends or collect the data we need,” Bracco adds. “To keep advancing, we need scientists who can determine what data we need, collect that data, and solve problems.”Modeling climate, predicting weatherMachine learning is often used when improving climate models that can simulate changing systems like our atmosphere, oceans, land, biochemistry, and ice. “These models are limited because of our computing power, and are run on a three-dimensional grid,” Bracco explains: below the grid resolution, researchers need to approximate complex physics with simpler equations that computers can solve quickly, a process called ‘parameterization’.Machine learning is changing that, offering new ways to improve parameterizations, she says. “We can run a model at extremely high resolutions for a short time, so that we don’t need to parameterize as many physical processes — using machine learning to derive the equations that best approximate what is happening at small scales,” she explains. “Then we can use those equations in a coarser model that we can run for hundreds of years.”While a full climate model based solely on machine learning may remain out of reach, the team found that ML is advancing our ability to accurately predict weather systems and some climate phenomena like El Niño. Previously, weather prediction was based on knowing the starting conditions — like temperature, humidity, and barometric pressure — and running a model based on physics equations to predict what might happen next. Now, machine learning is giving researchers the opportunity to learn from the past. “We can use information on what has happened when there were similar starting conditions in previous situations to predict the future without solving the underlying governing equations,” Bracco says. “And all while using orders-of-magnitude less computing resources.”The human connectionBracco emphasizes that while AI and ML play a critical role in accelerating research, humans are at the core of progress. “I think the in-person collaboration that led to this paper is, in itself, a testament to the importance of human interaction,” she says, recalling that the research was the result of a workshop organized at the Kavli Institute for Theoretical Physics — one of the team’s first in-person discussions after the Covid-19 pandemic.“Machine learning is a fantastic tool — but it’s not the solution to everything,” she adds. “There is also a real need for human researchers collecting high-quality data, and for interdisciplinary collaboration across fields. I see this as a big challenge, but a great opportunity for computer scientists and physicists, mathematicians, biologists, and chemists to work together.”Funding: National Science Foundation, European Research Council, Office of Naval Research, US Department of Energy, European Space Agency, Choose France Chair in AI.DOI: https://doi.org/10.1038/s42254-024-00776-3