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Biochemistry

Democratizing Chemical Analysis with Machine Learning and Robotics

Chemists have created a machine learning tool that can identify the chemical composition of dried salt solutions from an image with 99% accuracy. By using robotics to prepare thousands of samples and artificial intelligence to analyze their data, they created a simple, inexpensive tool that could expand possibilities for performing chemical analysis.

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Democratizing Chemical Analysis with Machine Learning and Robotics

A team of chemists at Florida State University has made significant strides in developing a machine learning tool that can identify the chemical composition of dried salt solutions from an image with remarkable accuracy. This breakthrough, published in Digital Discovery, has far-reaching implications for various fields, including space exploration, law enforcement, home testing, and more.

“We are living in the age of artificial intelligence and big data,” said co-author Oliver Steinbock, a professor in the FSU Department of Chemistry and Biochemistry. “We thought that if we used sufficiently large databases with enough pictures of different compounds and stains, we could maybe use AI to determine what the composition is.”
The research built upon a previous study from Steinbock’s lab, where researchers used machine learning to identify the chemical composition of salt stains from photos. In this new paper, they amplified that work by using a robot to process samples that were later analyzed by an improved machine learning program.

The Robotic Drop Imager (RODI) was created to prepare more than 2,000 samples per day, allowing the researchers to build a library of over 23,000 images. After preparing samples and taking photos, they simplified each image by converting them to grayscale and extracted 47 features, such as pattern area, brightness, and other attributes, which were used in their analysis.

With additional images, the accuracy of their machine learning program increased from around 90% to almost 99%. The researchers also analyzed the initial concentration of the salt solution at five different levels and trained their machine learning program to distinguish among them. The program reached 92% accuracy in identifying the concentration of the solution and the salt’s identity.

The accuracy demanded in different analyses will vary depending upon the situation,” said paper co-author Amrutha S.V., a postdoctoral researcher. “From my experience, I know that some types of spectroscopy and other analysis methods are expensive and require specialized technical expertise to operate. That’s why I’m excited about the possibility of a simple method — just taking a photo to determine chemical composition. That would be incredibly useful.”
Most chemical analysis methods focus on the molecular level, examining atoms, molecules, or crystal structures.

“That works great if you have good samples, a few hundred thousand dollars for the instruments and no weight restrictions,” Steinbock said. “But if you want to go on a space mission and ship things to a moon of Saturn, for example, every gram matters. If you can do chemical analysis with a camera, that’s a game changer.”
The project was developed for NASA, which was looking for inexpensive, low-cost, low-weight methods for determining chemical concentrations. Instead of transporting samples to Earth, an extraterrestrial rover equipped with a simple chemistry lab and camera could analyze the chemical composition of materials on site.

Along with space exploration, the method developed in Steinbock’s lab could be used to provide chemical analysis for other applications. The testing relies on minute sample amounts — just a few milligrams — making it valuable in scenarios where obtaining large samples is difficult. Law enforcement could run preliminary tests on suspected drugs, laboratories could test spilled materials for safety, and hospitals without access to a full chemical analysis lab could use it to aid diagnoses for patients.

“This is important because it could democratize chemical analysis,” Steinbock said.
Artificial intelligence promises to transform what is possible in research. Faculty at Florida State University are engaging in innovative projects that push the boundaries of this rapidly developing tool.

FSU’s artificial intelligence efforts are providing tools and insight for faculty in teaching and researching.

“I think it’s very helpful to be at a place where you get this kind of support, and it doesn’t necessarily have to be money, but just appreciation for trying new things,” Steinbock said. “AI is changing how we approach scientific discovery. What once required expensive equipment and specialized expertise can now be done with a simple camera and the right algorithm. This opens up new possibilities — not just for space missions, but for medicine, forensics, and beyond.”

Biochemistry

Unveiling Molecular Motion: A Breakthrough in Synthetic Biology and Soft Matter Physics

Scientists have uncovered a previously unknown type of molecular motion inside DNA-based droplets: instead of spreading randomly, guest molecules advance in an organized wave. This surprising discovery opens the door to understanding how cells might organize internal processes without membranes. Using customizable DNA condensates as experimental models, the team showed how molecular waves emerge through precise DNA interactions. These insights could not only transform our grasp of cellular signaling but may even lay groundwork for treating neurodegenerative diseases by influencing how molecules behave inside aging cells.

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In a groundbreaking discovery, researchers from Johannes Gutenberg University Mainz, the Max Planck Institute for Polymer Research, and the University of Texas at Austin have uncovered a form of molecular motion that defies conventional understanding. When guest molecules penetrate droplets of DNA polymers, they don’t diffuse haphazardly; instead, they propagate through them in a clearly-defined frontal wave.

“This is an effect we didn’t expect at all,” says Weixiang Chen, a leading researcher from the Department of Chemistry at JGU. The findings have been published in Nature Nanotechnology, and the implications are significant.

In contrast to traditional diffusion models, where molecules spread out randomly, the observed behavior of guest molecules in DNA droplets is structured and controlled. This takes the form of a wave of molecules or a mobile boundary, as explained by Professor Andreas Walther from JGU’s Department of Chemistry, who led the research project.

The researchers used thousands of individual strands of DNA to create droplets, known as biomolecular condensates. These structures can be precisely determined and have counterparts in biological cells, which employ similar condensates to arrange complex biochemical processes without membranes.

“Our synthetic droplets represent an excellent model system for simulating natural processes and improving our understanding of them,” emphasizes Chen.

The intriguing motion of guest molecules is attributed to the way that added DNA and the DNA present in the droplets combine on the basis of the key-and-lock principle. This results in swollen, dynamic states developing locally, driven by chemical binding, material conversion, and programmable DNA interactions.

The findings are not only fundamental to our understanding of soft matter physics but also relevant to improving our knowledge of cellular processes. “This might be one of the missing pieces of the puzzle that, once assembled, will reveal to us how cells regulate signals and organize processes on the molecular level,” states Walther.

This new insight could contribute to the treatment of neurodegenerative disorders, where proteins migrate from cell nuclei into the cytoplasm, forming condensates. As these age, they transform from a dynamic to a more stable state and build problematic fibrils. “It is quite conceivable that we may be able to find a way of influencing these aging processes with the aid of our new insights,” concludes Walther.

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Biochemistry

A Game-Changing mRNA Vaccine that’s More Effective and Less Costly to Develop

A new type of mRNA vaccine is more scalable and adaptable to continuously evolving viruses such as SARS-CoV-2 and H5N1, according to a new study.

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A team of researchers from the University of Pittsburgh School of Public Health and Pennsylvania State University has made a groundbreaking discovery in the field of vaccine development. They have created a new type of mRNA vaccine that is not only more effective but also less costly to develop, making it a game-changer in the fight against infectious diseases.

The current mRNA vaccines, such as those used to prevent COVID-19, have two significant challenges: they require a high amount of mRNA to produce and are constantly evolving due to the changing nature of viruses like SARS-CoV-2 and H5N1. The researchers addressed these challenges by creating a proof-of-concept COVID-19 vaccine using what’s known as a “trans-amplifying” mRNA platform.

In this approach, the mRNA is separated into two fragments: the antigen sequence and the replicase sequence. The latter can be produced in advance, saving crucial time in the event of a new vaccine needing to be developed urgently and produced at scale. Additionally, the researchers analyzed the spike-protein sequences of all known variants of SARS-CoV-2 for commonalities, rendering what’s known as a “consensus spike protein” as the basis for the vaccine’s antigen.

The results are promising: in mice, the vaccine induced a robust immune response against many strains of SARS-CoV-2. This has the potential for more lasting immunity that would not require updating, because the vaccine has the potential to provide broad protection. Additionally, this format requires an mRNA dose 40 times less than conventional vaccines, so this new approach significantly reduces the overall cost of the vaccine.

The lessons learned from this study could inform more efficient vaccine development for other constantly evolving RNA viruses with pandemic potential, such as bird flu. The researchers hope to apply the principles of this lower-cost, broad-protection antigen design to pressing challenges like bird flu, making it a crucial step in preparing for future pandemics.

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Aerospace

Unveiling the Molecular Link Between Air Pollution and Pregnancy Risks: A Groundbreaking Study

A new study found exposure to specific tiny particles in air pollution during pregnancy are associated with increased risk of various negative birth outcomes.

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The air we breathe has long been a concern for public health, but a recent study by Emory University researchers sheds light on a specific and alarming link between air pollution and pregnancy risks. Published in Environmental Science & Technology, the research reveals that exposure to tiny particles in air pollution during pregnancy can disrupt maternal metabolism, leading to increased risk of various negative birth outcomes.

The study analyzed blood samples from 330 pregnant women in the Atlanta metropolitan area, providing a detailed insight into how ambient fine particulate matter (PM2.5) affects the metabolism of pregnant women and contributes to increased risks of preterm and early term births. This pioneering work marks the first time researchers have been able to investigate the specific fine particles responsible for these adverse outcomes.

“The link between air pollution and premature birth has been well established, but for the first time we were able to look at the detailed pathway and specific fine particles to identify how they are reflected in the increased risk of adverse birth outcomes,” says Donghai Liang, PhD, study lead author and associate professor of environmental health. “This is important because if we can figure out the ‘why’ and ‘how,’ then we can know better how to address it.”

Previous research has shown that pregnant women and fetuses are more vulnerable than other populations to exposure to PM2.5, which is emitted from combustion sources such as vehicle exhaust, industrial processes, and wildfires. This increased vulnerability is linked to a higher likelihood of preterm births, the leading cause of death globally among children under the age of five.

Preterm birth is associated with complications such as cerebral palsy, respiratory distress syndrome, and long-term noncommunicable disease risks. Early term births (37-39 weeks of gestation) are also linked to increased neonatal morbidity and developmental challenges. Approximately 10% of preterm births worldwide are attributable to PM2.5 exposure.

As an air pollution scientist, Liang emphasizes the importance of addressing this issue beyond simply asking people to move away from highly polluted areas. “From a clinical intervention standpoint, it’s critical to gain a better understanding on these pathways and molecules affected by pollution,” he says. “In the future, we may be able to target some of these molecules to develop effective strategies or clinical interventions that could help reduce these adverse health effects.”

This groundbreaking study highlights the urgent need for policymakers and healthcare providers to take action against air pollution, particularly in areas with high levels of PM2.5 exposure. By understanding the molecular link between air pollution and pregnancy risks, we can work towards developing targeted solutions to mitigate these negative outcomes and protect the health of future generations.

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