Friday, March 22, 2024

 

Novel method to measure root depth may lead to more resilient crops


New approach could lead to faster breeding of plants better able to withstand drought, acquire nitrogen and store carbon deeper in soil



PENN STATE

Corn field 

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ALTHOUGH THIS METHOD OF IDENTIFYING DEEP-ROOTING PLANTS WAS ACCOMPLISHED WITH CORN, SHOWN HERE ON A CLOUDY SUMMER MORNING GROWING AT PENN STATE'S RUSSELL E. LARSON AGRICULTURAL RESEARCH CENTER, IT CAN BE USED WITH ALL PLANTS, THE RESEARCHERS SAID. 

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CREDIT: PENN STATE





UNIVERSITY PARK, Pa. — As climate change worsens global drought conditions, hindering crop production, the search for ways to capture and store atmospheric carbon causing the phenomenon has intensified. Penn State researchers have developed a new high-tech tool that could spur changes in how crops withstand drought, acquire nitrogen and store carbon deeper in soil.

In findings published in the January issue of Crop Science, they describe a process in which the depth of plant roots can be accurately estimated by scanning leaves with X-ray fluorescence spectroscopy, a process that detects chemical elements in the foliage. The method recognizes that roots take up elements they encounter, depending on the depth they reach, and a correlation exists between chemical elements in the leaves and root depth.

The new technology is the subject of a provisional patent application by Penn State, because it promises to speed up the plant-breeding process, according to research team leader Jonathan Lynch, distinguished professor of plant science in the College of Agricultural Sciences. The ability to measure the depth of plant roots without excavating them is a game-changing technology, he said.

“We've known about the benefits of deeper rooting crops for a long time — they are more drought tolerant and have an enhanced ability to take up nitrogen, which tends to move deep with water — but the problem has been how to measure root depth in the field,” he said. “To breed deeper-rooted crops, you need to look at thousands of plants. Digging them up is expensive and time consuming because some of those roots are down two meters or more. Everybody wants deep-rooted crops — but until now, we didn’t know how to get them.”

An added benefit to deeper-rooting crops, Lynch noted, is that they store carbon in the soil more effectively. And soil is the right place to put carbon, he pointed out, because carbon in the atmosphere is a bad thing — it causes global warming. Carbon in the soil is a good thing — it boosts fertility.

“Having deeper roots means that carbon the plants get from photosynthesis is stored down deeper in the soil when they build roots. And the deeper carbon is put in the soil, the longer it stays in the soil,” he said. “The U.S. Department of Energy estimates that just having deep-rooted crops in America alone could offset years of our total carbon emissions. That’s huge — think about all the acres growing crops in America. If those roots grow just a little bit deeper, then we’re storing massive amounts of carbon deeper in the soil.”

Developing the new method — which the researchers called LEADER (Leaf Element Accumulation from DEep Root) — took six years and involved the collection and analysis of more than 2,000 soil core samples at four research sites across the country, noted Molly Hanlon, a former postdoctoral scholar in Lynch’s research group, who spearheaded the study.

It involved growing a set of 30 genetically distinct lines of corn at Penn State’s Russell E. Larson Agricultural Research Center, the University of Colorado’s Agricultural Research and Education Center, the University of Wisconsin Arlington Agricultural Research Station, and the University of Wisconsin Hancock Agricultural Research Station. The researchers found that they could correctly classify the plots with the longest deep root lengths — deeper than 30 or 40 centimeters — using the LEADER method with high accuracy.

A major tenet of soil science is that biological, physical and chemical properties vary with soil depth, explained Hanlon, now a senior research scientist with Donald Danforth Plant Science Center in St. Louis.

“And plant roots grow through these different soil layers,” she said. “The elements are then transported to the shoot where we can quickly and easily assay the elemental content of leaf tissue using X-ray fluorescence. In this way, the leaves can serve as indicators or sensors of where the roots are in the soil.”

In the study, the researchers were able to accurately estimate root depth by analyzing the foliar accumulation of elements naturally occurring in diverse soils. As an alternative method for assessing root depth, in both field and greenhouse experiments, they injected strontium into the soil at a set depth as a tracer for LEADER analysis. Later, they harvested plants growing nearby and determined that strontium detected in the leaves strongly correlated to the depth of their roots.

Although the LEADER method was accomplished with corn, it offers a wider application, Lynch suggested.

“It shows promise as a tool for measuring root depth in different plant species and soils,” he said. “It made sense to do this research with corn — it’s one of the world’s most important crops, grown extensively as a staple food for humans, livestock feed, as a biofuel and as a starting material in industry. Deeper-rooted corn crops able take up more water and nitrogen under limiting conditions, with increased long-term soil carbon storage would be a major development. But this LEADER method can be used with all plants.”

Kathleen Brown, Penn State professor emeritus of plant stress biology, contributed to the research.

This research was funded by the U.S. Department of Energy ARPA-e and the U.S. Department of Agriculture’s National Institute of Food and Agriculture.

 

Scientists develop catalyst designed to make ammonia production more sustainable


Created at a FAPESP-supported research center, the material helps produce ammonia by electrochemical reduction of nitrogen gas, dispensing with the high temperature and pressure required by the conventional method




FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO





Ammonia is one of the most widely produced chemicals in the world, and is used in a great many manufacturing and service industries. The conventional production technology is the Haber-Bosch process, which combines nitrogen gas (N2) and hydrogen gas (H2) in a reactor in the presence of a catalyst. This process requires high levels of temperature and pressure, resulting in substantial power consumption. Indeed, ammonia production is estimated to consume 1%-2% of the world’s electricity and to account for about 3% of global carbon emissions.

In pursuit of more sustainable alternatives, researchers affiliated with the Center for Development of Functional Materials (CDMF) have developed an electrochemical nitrogen reduction process using catalysts made of iron oxide and molybdenum disulfide. Because the process is electrochemical, it does not require high temperature and pressure.

CDMF is one of the Research, Innovation and Dissemination Centers (RIDCs) supported by FAPESP, and is hosted by the Federal University of São Carlos (UFSCar).

An article on the subject is published in the journal Electrochimica Acta. The authors are Caio Vinícius da Silva Almeida, a postdoctoral fellow at UFSCar with a scholarship from FAPESP, and Lucia Helena Mascaro, a professor in UFSCar’s Department of Chemistry.

The catalysts in question are prepared by electrodeposition, a simple and inexpensive method. As reported in the article, they are efficient, stable and durable. The results of the research open up possibilities for the use of simple, low-cost catalysts in ammonia production and the synthesis of amorphous materials for nitrogen fixation.

About São Paulo Research Foundation (FAPESP)

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.

 

New reactor could save millions when making ingredients for plastics and rubber from natural gas


With oil production dropping, a process using natural gas is needed to avert a shortage of a workhorse chemical used for automotive parts, cleaning products and more


Peer-Reviewed Publication

UNIVERSITY OF MICHIGAN

 


 

Images

 

A new way to make an important ingredient for plastics, adhesives, carpet fibers, household cleaners and more from natural gas could reduce manufacturing costs in a post-petroleum economy by millions of dollars, thanks to a new chemical reactor designed by University of Michigan engineers.

The reactor creates propylene, a workhorse chemical that is also used to make a long list of industrial chemicals, including ingredients for nitrile rubber found in automotive hoses and seals as well as blue protective gloves. Most propylene used today comes from oil refineries, which collect it as a byproduct of refining crude oil into gasoline.

As oil and gasoline fall out of vogue in favor of natural gas, solar, and wind energy, production of propylene and other oil-derived products could fall below the current demand without new ways to make them.

Natural gas extracted from shale holds one potential alternative to propylene sourced from crude oil. It's rich in propane, which resembles propylene closely enough to be a promising precursor material, but current methods to make propylene from natural gas are still too inefficient to bridge the gap in supply and demand.

"It's very hard to economically convert propane into propylene," said Suljo Linic, the Martin Lewis Perl Collegiate Professor of Chemical Engineering and the corresponding author of the study published in Science.

"You need to heat that reaction to drive it, and standard methods require very high temperatures to produce enough propylene. At those temperatures, you don't just get propylene but solid carbon deposits and other undesirable products that impair the catalyst. To regenerate the reactor, we need to burn off the solid carbon deposits often, which makes the process inefficient."

The researchers' new reactor system efficiently makes propylene from shale gas by separating propane into propylene and hydrogen gas. It also gives hydrogen a way out, changing the balance between the concentration of propane and reaction products in a way that allows more propylene to be made. Once separated, the hydrogen can also be safely burned away from the propane, heating the reactor enough to speed up the reactions without making any undesirable compounds.

This separation is achieved through the reactor's nested, hollow-fiber membrane tubing. The innermost tube is made up of materials that splits the propane into propylene and hydrogen gas. While the tubing keeps most of the propylene inside the innermost chamber, the hydrogen gas can escape into an outer chamber through pores in a membrane layer of the material. Inside that chamber, the hydrogen gas is controllably burned by mixing in precise amounts of oxygen.

Because the hydrogen can be burned inside the reactor and can operate under higher propane pressures, the technology could allow plants to produce propylene from natural gas without installing extra heaters. A plant that produces 500,000 metric tons of propylene annually could save as much as $23.5 million over other methods starting with shale gas, according to the researchers' estimates. Those savings come on top of the operational savings from burning hydrogen produced in reaction, rather than other fuels

The research was funded by the U.S. Department of Energy's Office of Basic Energy Sciences, the RAPID Manufacturing Institute and the National Science Foundation.

The reactor materials were studied at the Michigan Center for Materials Characterization. The team is pursuing patent protection with the assistance of U-M Innovation Partnerships and is seeking partners to bring the technology to market.

Suljo Linic is also a professor of integrative systems and design.

Study: Overcoming limitations in propane dehydrogenation by co-designing

catalysts/membrane systems (DOI: 10.1126/science.adh3712)

 

Bar-Ilan University researchers develop cost-effective method to detect low concentrations of pharmaceutical waste and contaminants in water



Peer-Reviewed Publication

BAR-ILAN UNIVERSITY





Pharmaceutical waste and contaminants present a growing global concern, particularly in the context of drinking water and food safety. Addressing this critical issue, a new study by researchers at Bar-Ilan University’s Department of Chemistry and Institute of Nanotechnology and Advanced Materials has resulted in the development of a highly sensitive plasmonic-based detector, specifically targeting the detection of harmful piperidine residue in water.

Piperidine, a small potent molecule that serves as a building block in the pharmaceutical and food additive industries, poses significant health risks to both humans and animals due to its toxic nature. Detecting even miniscule amounts of piperidine is essential for ensuring drinking water and food safety. The plasmonic substrate developed at Bar-Ilan University, comprising triangular cavities milled in a silver thin film and protected by a 5-nanometer layer of silicon dioxide, offers unparalleled sensitivity to piperidine, detecting low concentrations in water.

Mohamed Hamode, a PhD student at Bar-Ilan’s Department of Chemistry, in collaboration with Dr. Elad Segal, developed the dime-sized device using a focused ion microscope to drill nanometer-sized holes on a metal surface. By programming the beam with a custom-built computer program, Hamode creates holes of various shapes. These holes, smaller than the wavelength of visible light, enhance the electrical field on the surface, leading to concentrated light in very small areas. This amplification enables optical phenomena to be significantly increased, allowing for the identification of a low concentration of molecules that were previously undetectable with optical probes.

Due to its confined and enhanced electromagnetic field, the plasmonic substrate offers an efficient alternative to other substrates currently used in Surface Enhanced Raman Spectroscopy (SERS), opening avenues for the use of cost-effective and portable Raman devices that enable quicker and more affordable analysis.

“This study represents a significant advancement in the field of environmental monitoring,” said lead researcher Prof. Adi Salomon, of Bar-Ilan’s Department of Chemistry and Institute of Nanotechnology and Advanced Materials. “By leveraging nano-patterned metallic surfaces, we’ve demonstrated the detection of low concentrations of piperidine in water using affordable optics, offering a promising solution for environmental analytical setups.”

The findings of the study, just published in the journal Environmental Science: Nano underscore the potential of plasmonic-based detectors in revolutionizing environmental monitoring, particularly in the detection of pharmaceutical waste and contaminants.

Next week Mohamed Hamode will present the innovation at an international conference on microscopy taking place in Italy.

 

  

Bridging the gap: USUS computer scientists develop model to enhance water data from satellites


Pursuing NSF-funded research, Utah State University researchers publish findings in AGU's 'Water Resources Research' journal



UTAH STATE UNIVERSITY

USU Computer Scientists Develop Hydro-GAN Model to Enhance Satellite Water Data 

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UTAH STATE UNIVERSITY COMPUTER SCIENTISTS POUYA HOSSEINZADEH, LEFT, DOCTORAL STUDENT, WITH FACULTY MENTOR SOUKAINA FILALI BOUBRAHIMI, RIGHT, ASSISTANT PROFESSOR IN THE DEPARTMENT OF COMPUTER SCIENCE, PUBLISHED A DESCRIPTION OF A MACHINE LEARNING METHOD TO ENHANCE WATER DATA COLLECTED BY SATELLITES IN AN AGU JOURNAL. HOSSEINZADEH PRESENTS THE RESEARCH AT USU'S 2024 SPRING RUNOFF CONFERENCE MARCH 26-27, IN LOGAN, UTAH, USA. 

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CREDIT: USU/M. MUFFOLETTO




LOGAN, UTAH, USA -- Satellites encircling the Earth collect a bounty of water data about our planet, yet distilling usable information from these sources about our oceans, lakes, rivers and streams can be a challenge.

“Water managers need accurate data for water resource management tasks, including lake coastal zone monitoring, rising seas border shift detection and erosion monitoring,” says Utah State University computer scientist Pouya Hosseinzadeh. “But they face a trade-off when reviewing data from currently deployed satellites, which yield complementary data that are either of high spatial or high temporal resolutions. We’re trying to integrate the data to provide more accurate information.”

Varied data fusion approaches present limitations, including sensitivity to atmospheric disturbances and other climatic factors that can result in noise, outliers and missing data.

A proposed solution, say Hosseinzadeh, a doctoral student, and his faculty mentor Soukaina Filali Boubrahimi, is the Hydrological Generative Adversarial Network – known as Hydro-GAN. The scientists developed the Hydro-GAN model with USU colleagues Ashit Neema, Ayman Nassar and Shah Muhammad Hamdi, and describe this tool in the March 13, 2024, online issue of the American Geophysical Union journal Water Resources Research.

The team’s research is supported by the National Science Foundation.

Hydro-GAN, says Filali Boubrahimi, assistant professor in USU’s Department of Computer Science, is a novel machine learning-based method that maps the available satellite data at low resolution to a high-resolution data counterpart.

“In our paper, we describe integrating data collected by MODIS, a spectroradiometer aboard the Terra Earth Observing System satellite, and the Landsat 8 satellite, both of which have varied spatial and temporal resolutions,” she says. “We’re trying to bridge the gap by generating new data samples from images collected by these satellites that improve the resolution of the shape of water boundaries.”

The dataset used in this research consists of image data collected during a seven-year span (2015-2021) of 20 reservoirs in the United States, Australia, Mexico and other countries. The authors present a case study of Lake Tharthar, a salt water lake in Iraq, comparable in size to Great Salt Lake and facing similar climate and usage pressures.

“Using seven years of data from MODIS and Landsat 8, we evaluated our proposed Hydro-GAN model on Lake Tharthar’s shrinking and expansion behaviors,” Hosseinzadeh says. “Using Hydro-GAN, we were able to improve our predictions about the lake’s changing area.”

Such information is critical for the region’s hydrologists and environmental scientists, he says, who need to monitor seasonal dynamics and make decisions about how to sustain the lake’s water supply.

The scientists demonstrate Hydro-GAN can generate high-resolution data at historical time steps, which is otherwise unavailable, for situations where a large amount of historical data is needed for accurate forecasting.

“We think this will be a valuable tool for water managers and, moving forward with similar models, we can employ a multi-modal approach to provide data in addition to images, including information about topology, snow data amounts, streamflow, precipitation, temperature and other climate variables,” says Hosseinzadeh, who presents the research during USU’s 2024 Spring Runoff Conference March 26-27 at the Cache County Fairgrounds and Utah State’s Logan campus.

 

Best way to bust deepfakes? Use AI to find real signs of life, say Klick Labs scientists


Researchers identify audio deepfakes with new algorithm and vocal biomarkers



KLICK APPLIED SCIENCES





NEW YORK, NY / TORONTO, ON – March, 21, 2024 – Artificial intelligence may make it difficult for even the most discerning ears to detect deepfake voices – as recently evidenced in the fake Joe Biden robocall and the bogus Taylor Swift cookware ad on Meta – but scientists at Klick Labs say the best approach might actually come down to using AI to look for what makes us human.

Inspired by their clinical studies using vocal biomarkers to help enhance health outcomes, and their fascination with sci-fi films like “Blade Runner,” the Klick researchers created an audio deepfake detection method that taps into signs of life, such as breathing patterns and micropauses in speech.

“Our findings highlight the potential to use vocal biomarkers as a novel approach to flagging deepfakes because they lack the telltale signs of life inherent in authentic content,” said Yan Fossat, senior vice president of Klick Labs and principal investigator of the study. “These signs are usually undetectable to the human ear, but are now discernible thanks to machine learning and vocal biomarkers.”

‘Investigation of Deepfake Voice Detection using Speech Pause Patterns: Algorithm Development and Validation,’ published today in the open-access journal JMIR Biomedical Engineering, describes how vocal biomarkers, along with machine learning, can be used to distinguish between deepfakes and authentic audio with reliable precision. As part of the study, Fossat and his team at Klick Labs looked at 49 participants from diverse backgrounds and accents. Deepfake models were then trained on voice samples provided by the participants, and deepfake audio samples were generated for each person. After analyzing speech pause metrics, the scientists discovered their models could distinguish between the real and fakes with approximately 80 percent accuracy.

These findings follow recent high-profile voice cloning scams, Meta’s announced plan to introduce AI-generated content labels, and the Federal Communications Commission’s February ruling to make deepfake voices in robocalls illegal. In December, a PBS NewsHour report cited public policy and AI experts’ concerns that deepfake usage will increase with the upcoming U.S. presidential election.

While the new study offers one solution to this growing problem, Fossat acknowledged the need to keep evolving detection technology as deepfakes become more and more realistic. 

Today’s news highlights Klick’s ongoing work in vocal biomarkers and AI. In October, it announced groundbreaking research in Mayo Clinic Proceedings: Digital Health around the AI model it created to detect Type 2 diabetes using 10 seconds of voice.
 

About Klick Applied Sciences (including Klick Labs)

Klick Applied Sciences’ diverse team of data scientists, engineers, and biological scientists conducts scientific research and develops AI/ML and software solutions as part of the company’s work to support commercial efforts using its proven business, scientific, medical, and technological expertise. Its 2019 Voice Assistants Medical Name Comprehension study laid the scientific foundation for rigorously testing voice assistant consumer devices in a controlled manner. Klick Applied Sciences is part of the Klick Group of companies, which also includes Klick Health (including Klick Katalyst and btwelve), Klick Media Group, Klick Consulting, Klick Ventures, and Sensei Labs. Established in 1997, Klick has offices in New York, Philadelphia, Toronto, London, São Paulo, and Singapore. Klick has consistently been ranked a Best Managed Company, Great Place to Work, Best Workplace for Women, Best Workplace for Inclusion, Best Workplace for Professional Services, and Most Admired Corporate Culture.

 

For more information, or a copy of the abstract, please contact Klick PR at pr@klick.com or 416-214-4977.

 

Kulangareth NV, Kaufman J, Oreskovic J, Fossat Y

Investigation of Deepfake Voice Detection Using Speech Pause Patterns: Algorithm Development and Validation

JMIR Biomed Eng 2024;9:e56245

URL: https://biomedeng.jmir.org/2024/1/e56245/   

doi: 10.2196/56245