How low-cost AI is transforming health care logistics

By forecasting demand and correcting for missing data, researchers from Wharton and Penn Engineering developed a low-cost AI tool that helps get lifesaving medicines to the communities in Sierra Leone that need them most.

University of Pennsylvania

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Key takeaways:

1. Penn researchers partnered with Sierra Leone’s government to build a low-cost AI system designed to forecast patient demand and optimize the allocation of essential medical supplies.
2. Their algorithm calculates the most efficient way to distribute limited national stock, ensuring life-saving medicines reach the clinics that need them most.
3. A successful pilot yielded a 19% increase in the consumption of allocated medicines, leading the government to scale the system nationwide—where it now supports over 70 products on just $30 a month in server costs.

Managing a medical supply chain in low- and middle-income countries can mean navigating a landscape prone to extreme and unexpected disruptions. In Sierra Leone, for instance, external forces ranging from an attempted military coup and an infectious disease outbreak to a widespread electricity outage can complicate public health logistics.

The consequences are severe. Despite a national government initiative dedicated to providing free medical care and essential supplies to pregnant women and children under five, Sierra Leone has one of the highest maternal mortality rates in the world, at 717 deaths per 100,000 live births, explains Hamsa Bastani, an operations researcher and statistician at the Wharton School.

A major driver is not always a lack of medicine but a failure to get the right supplies to the right place at the right time, says Bastani. Some clinics end up overstocked while others run dry.

To address that mismatch, Bastani, computer scientist Osbert Bastani, and Ph.D. candidate Angel Tsai-Hsuan Chung partnered with Sierra Leone’s government to build a low-cost, decision-support system that uses machine learning to forecast demand and optimize how medicines are allocated.

Following a pilot rollout in five districts, the researchers found a 19% increase in consumption of allocated medical products in treated areas, a proxy for improved access. Their findings are published in Nature.

The tool predicts how much of each product individual facilities will likely need and then  computes the most efficient way to distribute the limited national stock, explains first author Tsai-Hsuan Chung. It is “designed for a setting where data are sparse, noisy, and often incomplete.”

The new system also addresses previous inequities—facilities serving poorer, more remote populations that frequently experienced chronic stockouts saw a 32% surge in medicine consumption with the new tool.

Based on these results, the government scaled the system nationwide. Today, it supports allocation decisions for more than 70 essential products—including medicines to help with postpartum hemorrhaging and treat the seizures of eclampsia, alongside other essentials like tetanus vaccines, gloves, and antimalarial medicines—across the country, reaching an estimated two million women and children under five. The system runs on only $30 per month in server costs and requires no additional workforce.

Field work leads to real work

To build a tool capable of handling Sierra Leone’s highly varied logistical ecosystem, the researchers knew they could not rely solely on remote data feeds or Zoom calls, so Tsai-Hsuan Chung traveled to the capital city of Freetown.

 “Local officials were worried that an AI tool arriving from abroad might replace their jobs or leave them responsible if something went wrong,” Tsai-Hsuan Chung says.

To secure local buy-in and gain trust, she spent weeks conducting personalized training sessions, ensuring fair compensation for their time. She led the design of a web application that closely mirrored the agency’s preexisting spreadsheet workflows, reducing the friction of forcing workers to learn a complex, alien software system.

“Crucially,” adds Hamsa Bastani, “the system chiefly functions as a ‘decision-support’ tool wherein local officials always retain final say and can override recommendations.”

Under the hood of their AI system

Understaffed and under-resourced clinics are the least able to consistently report data, leading to data gaps clustered around the very places where need is greatest. That leads to a subtle distortion: If a model learns only from the cleanest data, it will favor the best-documented clinics—the ones already better served—while overlooking those where the record is thin but the need is acute.

The team circumvents this bias using multitask learning, which allows the model to borrow shared patterns—like seasonal demand—from places with richer data and apply them where records are sparse.

They paired that with a “backstop” built from external information, including census data and Google Earth images of the vegetation around the clinics, which indicate human activity. This approach helped define catchments on the basis of travel time between those areas and the facilities. When those data were combined with census data on the proportion of women and children living within zones, the algorithm could tease out a baseline estimate for how much medicine the clinic needed based purely on the local demographics.

These estimates do not capture every local fluctuation, but they are able to provide a stable baseline tied to population and geography.

Looking ahead

With ownership of the allocation tool now fully transferred to Sierra Leone’s government, the research team is turning outward. Tsai-Hsuan Chung is currently working on another project with officials from Somaliland, collaborating with Taiwanese partners to adapt similar data-driven approaches to other regional health systems.

Ultimately, the team hopes their work serves as a definitive blueprint for the future, demonstrating that machine learning can powerfully improve health care delivery in resource-constrained environments at low cost.

Hamsa Bastani is an associate professor in the Operations, Information and Decisions Department and the Department of Statistics and Data Science at the Wharton School in the University of Pennsylvania.

Osbert Bastani an associate professor in the Department of Computer and Information Science in the School of Engineering and Applied Science at Penn.

Angel Tsai-Hsuan Chung is a Ph.D. candidate in the Hamsa Bastani Lab at the Wharton School at Penn.

Other authors include Jatu Abdulai, Patrick Bayoh, and Lawrence Sandi of Sierra Leone’s National Medical Supplies Agency, and Francis Smart of Sierra Leone’s Ministry of Health and Sanitation.

This research was supported by the Wharton AI and Analytics Initiative, Wharton Global Initiatives, and the Wharton Mack Institute for Innovation Management.

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Journal

Nature

DOI

10.1038/s41586-026-10433-7 

Method of Research

Data/statistical analysis

Subject of Research

People

Article Title

Improving access to essential medicines via decision-aware machine learning

  

The broader a fungus’s diet, the better it kills insects and helps plants

University of Maryland

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UMD entomologists have discovered that a single underlying trait—metabolic breadth, or the range of nutrients a fungus can use—links its ability to kill insects, partner with plants and thrive in different ecological roles. Rather than trading one lifestyle for another, some fungi become better at all of them.

Many fungi lead triple lives—acting as deadly insect pathogens, decomposers in the soil and helpful partners living inside and transferring insect-derived nitrogen to plant roots. Scientists have long wondered what allows a single species to pull off these very different roles.

A new study offers a surprisingly simple answer: metabolic flexibility—the ability to use many different food sources. Working with the insect-killing fungus Metarhizium robertsii, University of Maryland entomologists found that strains capable of using a wider range of nutrients were both faster and deadlier at killing insects and more effective at colonizing plant roots. The findings were published July 1, 2026, in the Proceedings of the National Academy of Sciences.

"We expected to see a trade-off—that becoming a better plant partner would come at the cost of being a good killer or vice versa," said the study’s senior author Raymond St. Leger, a Distinguished University Professor of Entomology at UMD. "Instead, the two abilities rise and fall together, and what links them is the fungus's underlying nutritional flexibility." 

Different strains, different lives

The researchers combined genome-based analysis of eight M. robertsii strains spanning the species' evolutionary tree with laboratory tests measuring virulence, plant-root colonization, toxin activity and growth on 95 different nutrients. They chose to study M. robertsii because it’s already used worldwide as a natural biological control agent against insect pests and is increasingly being explored for its ability to promote crop growth. 

St. Leger and entomology postdoctoral associate Huiyu Sheng (Ph.D. ’24, entomology) found that the strains split into two distinct groups. The fungal strains that diverged early (at least 6 million years ago) behaved like “sleepers.” They kill insects slowly but pour resources into multiplying inside the host and producing huge numbers of spores, allowing them to survive until they encounter another host. Fungal strains that diverged more recently behave like "creepers." They germinate quickly on both insect skin and plant roots, kill rapidly, often deploy paralyzing toxins and grow as creeping threads from insect cadavers onto nearby roots, rather than forming spores.

The key difference between these two fungal strategies was metabolic breadth—the range of nutrients each strain could feed on. Fungi that could grow on a wider menu of sugars, amino acids and organic acids consistently proved better at both infecting insects and colonizing plant roots.

New thinking, new applications

The new study’s results reframe some insect-killing fungi as broadly "environmentally competent" organisms—whose ability to attack insects and partner with plants comes from the same nutritional toolkit. The team’s findings provide a useful model for understanding how microbes evolve the capacity to switch ecological roles.

"Instead of forcing fungi to choose between being insect killers or plant partners, evolution appears to have favored strains that are simply better at making use of whatever resources they encounter," St. Leger said. "Their versatility begins with metabolism."

The research also has practical implications for agriculture and could help researchers select fungal strains tailored for different agricultural goals. Broadly metabolizing strains of fungi could provide rapid suppression of insect pests while colonizing crop roots and promoting plant growth in the field. In contrast, fungal strains that produce large numbers of spores may be better suited for longer-term pest control.

“What we’ve learned could help growers use fungal pathogens more effectively,” St. Leger said.

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The paper, "Metabolic breadth links insect pathogenicity and plant association in Metarhizium robertsii," by Huiyu Sheng and Raymond J. St. Leger, was published in Proceedings of the National Academy of Sciences on July 1, 2026.

Research reported in this release was supported 100% by the U.S. Department of Agriculture’s National Institute of Food and Agriculture and Agricultural Research Service Biotechnology Risk Assessment Grants Program under grant number 2022-33522-38272 and the U.S. National Science Foundation’s Plant Biotic Interactions Program under grant number DEB-1911777. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the USDA or the NSF.

About the College of Computer, Mathematical, and Natural Sciences

The College of Computer, Mathematical, and Natural Sciences at the University of Maryland educates more than 10,000 future scientific leaders in its undergraduate and graduate programs each year. The college's 10 departments and seven interdisciplinary research centers foster scientific discovery with annual sponsored research funding exceeding $250 million.

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Journal

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.260869412 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Metabolic breadth links insect pathogenicity and plant association in Metarhizium robertsii

Article Publication Date

1-Jul-2026

Unearthing new cancer treatments from fungi

Penn Engineers led by Xue ‘Sherry’ Gao have developed a gene-editing tool built specifically for fungi, unlocking a hidden library of molecules—including some with early anti-cancer promise—from one of biology’s most overlooked kingdoms.

University of Pennsylvania

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Three fungal colonies in a single dish. Cultures like these yielded eight molecules new to science, three of them with early anti-cancer activity.

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Credit: Eric Sucar / University of Pennsylvania

Researchers have spent decades—and billions of dollars—sequencing animal and crop genomes, but fungi have historically been the forgotten middle child of genomics, only noticed when they’re ruining bread or colonizing toes.

“This neglect is kind of remarkable considering how fungi have shaped modern medicine,” says chemical and biomolecular engineer Xue “Sherry” Gao. “From the serendipitous discovery of penicillin to cholesterol-lowering statins, we owe many recent breakthroughs in longevity to fungal chemistry. But despite this, the vast majority of the fungal kingdom remains a black box.”

A main driver for this mystery is that when grown in sterile lab conditions, fungi turn off the drug-producing gene pathways they synthesize in the wild to fight off bacteria.

“To turn those silent pathways back on, we needed a powerful way to precisely manipulate fungal genome, such as editing their master regulatory genes, but traditional tools weren’t up to the task,” Gao says.

Now, Gao and her team at the School of Engineering and Applied Science have developed a novel genome editing tool, called fPE7max, to navigate the complex genetic architecture of thread-like molds known as filamentous fungi—think Aspergillus, or the Penicillium that gave the world penicillin—and finally unlock the secrets of this overlooked kingdom.

Their findings are published in Nature Biotechnology.

“We isolated 18 distinct complex molecules, eight of which possessed chemical structures entirely new to science,” says first author Chunxiao Sun, a postdoctoral researcher in the Gao Lab. “Of these uncovered molecules, three exhibited promising anti-cancer properties. These molecules can serve as lead compounds for disease treatment, providing a vital new pipeline for drug discovery.”

Sun says that one novel molecule showed selective toxicity against human breast, hepatic, and leukemia cancer cells.

Rewriting the genomics textbook for fungi

Over the last decade, CRISPR-Cas9 has been the headline-grabbing gene-splicing tool. But Gao explains that in filamentous fungi, which are rich sources of antibacterial compounds, it can be a blunt instrument, resulting in unintended mutations.

A newer technology called prime editing avoids double-strand breaks entirely, allowing for precise control over DNA sequences. But adapting prime editing for the fungal kingdom was a challenge.

First, the team had to ensure their genetic instructions actually survived the trip through the cell. Prime editing relies on a guide RNA—a molecular instruction manual that tells the tool where to go and what new code to write. But when researchers try to make massive edits, these instruction manuals can get unreasonably long, making them fragile and prone to degrading before the editing job is done.

Their workaround was integrating a special protein—fLa—into their tool. fLa acts as a sturdy, protective binder that shields the fragile RNA instructions, allowing fPE7max to handle the massive DNA insertions and deletions that cause other tools to break down.

Second, the team had to stop the fungal cells from spotting the researchers’ new edits, flagging them as errors, and reverting the DNA back to its original sequence. To outsmart that, the team incorporated a specialized protein that mutes the fungus’s natural repair system just long enough for the new genetic code to permanently take hold.

Ancient organisms, new science

The resulting platform, fPE7max, achieves editing efficiency approaching 90%. And by using fPE7max to flip the switch on these silent fungal gene clusters, the team uncovered previously unknown compounds.

To test their new tool, the researchers targeted the regulatory sequences of a master gene called laeA, which controls a vast network of biosynthetic pathways. By using fPE7max to precisely edit out the molecular roadblocks that naturally keep this gene’s translation repressed, they successfully awakened silent gene clusters across several different fungal species, finding molecules with promising anti-cancer properties.

“It’s a compelling proof-of-concept demonstrating that the next generation of life-saving therapeutics might already exist in nature,” Gao adds.

Looking ahead, the team plans to deploy fPE7max across a much wider array of fungal species to continue hunting for novel natural products. The researchers hope to move away from the treasure-hunt approach of searching for wild fungi that might produce useful drugs and into an era of systematic optimization.

Xue “Sherry” Gao is the Presidential Penn Compact Associate Professor in the Department of Chemical and Biomolecular Engineering, the Department of Bioengineering, and the Center for Precision Engineering for Health at the University of Pennsylvania.

Chunxiao Sun is a postdoctoral researcher in the Gao Lab at Penn Engineering.

Other authors include Chris Keum, Qiuyue Nie, Yihui Shen, and Naomi Straub of Penn Engineering.

This research was supported by the National Institutes of Health (NIH grant R35GM138207) and startup funds provided by the University of Pennsylvania.

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Journal

Nature Biotechnology

DOI

10.1038/s41587-026-03202-4 

Method of Research

Experimental study

Subject of Research

Cells

Article Title

Prime editing for precise genome engineering and modulation of fungal metabolism

Article Publication Date

30-Jun-2026

 

Smarter microgrid management could cut household energy costs and diesel emissions

Shenyang Agricultural University Collaborative Journals

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Optimal energy management of distributed energy resources for a hybrid residential microgrid

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Credit: Temitope Raphael Ayodele, Ayodeji Samson Olatunji Ogunjuyigbe, Ifiokobong John Dickson & Richard Oladayo Olarewaju

A new study shows that an intelligent optimization strategy can make hybrid residential microgrids cleaner, cheaper, and more reliable by coordinating solar power, wind power, diesel backup, and battery storage.

As the world looks for practical ways to reduce emissions from electricity generation, hybrid residential microgrids are gaining attention as a promising solution for homes and communities. These systems can combine renewable energy sources, such as solar photovoltaic panels and wind turbines, with diesel generators and battery energy storage systems. However, managing these different resources efficiently remains a major challenge because sunlight, wind, and household demand all fluctuate throughout the day.

In a new study published in Energy & Environment Nexus, researchers developed an optimized energy management strategy for a hybrid residential microgrid using Particle Swarm Optimization, a computational method inspired by the collective movement of birds, fish, and other swarms. The study compared this PSO-based strategy with results from HOMER, a widely used commercial simulation platform for renewable energy systems.

The results showed that the PSO-based approach delivered clear advantages. Compared with HOMER, the new method reduced the Net Present Cost by 12.01%, the cost of energy by 16.09%, diesel fuel consumption by 50%, and CO₂ emissions by 17.65%. These findings suggest that intelligent optimization can help residential microgrids deliver energy more economically while lowering environmental impacts.

“Hybrid microgrids are not only about adding renewable energy resources, but also about managing them wisely,” said corresponding author Richard Oladayo Olarewaju. “Our study shows that when solar, wind, diesel generation, and battery storage are coordinated through an effective optimization strategy, the system can reduce costs, cut fuel use, and maintain reliable power supply.”

The research team modeled a typical hybrid residential microgrid containing four key components: solar photovoltaic arrays, wind turbines, a diesel generator, and a battery energy storage system. The energy management strategy prioritized renewable energy first. When solar and wind power exceeded demand, surplus energy was stored in the battery. When renewable generation was insufficient, the battery helped meet the shortfall. The diesel generator was used only when renewable power and battery discharge could not fully satisfy demand.

This approach is important because diesel generators are often used as backup power in off-grid or weak-grid communities, but they increase fuel costs and greenhouse gas emissions. By using battery storage to absorb excess renewable energy and release it when needed, the microgrid can rely less on diesel power.

The study found that adding a battery energy storage system had a particularly strong impact. Battery implementation reduced diesel fuel consumption by 74.44%, CO₂ emissions by 80.81%, and the cost of energy by 46.34%. According to the authors, this demonstrates that storage is not simply an add-on technology, but a central component for improving microgrid performance.

Among the six system configurations tested, the best performance came from the full hybrid setup combining solar PV, wind turbine, diesel generator, and battery storage. This configuration achieved the lowest cost, lowest fuel consumption, and lowest emissions while maintaining reliable electricity supply.

“The battery storage system acts as the bridge between renewable energy availability and household demand,” Olarewaju added. “It allows the microgrid to use more clean energy when it is available and to reduce dependence on diesel generation when renewable output drops.”

The findings provide a useful framework for designing cleaner and more cost-effective residential microgrids, especially in areas where reliable grid electricity is limited or diesel backup remains common. The authors note that optimized energy management can help communities move toward more sustainable power systems without compromising reliability.

 

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Journal reference: Ayodele TR, Ogunjuyigbe ASO, Dickson IJ, Olarewaju RO. 2026. Optimal energy management of distributed energy resources for a hybrid residential microgrid. Energy & Environment Nexus 2: e012 doi: 10.48130/een-0026-0005  

https://www.maxapress.com/article/doi/10.48130/een-0026-0005  

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About Energy & Environment Nexus:
Energy & Environment Nexus (e-ISSN 3070-0582) is an open-access journal publishing high-quality research on the interplay between energy systems and environmental sustainability, including renewable energy, carbon mitigation, and green technologies.

Follow us on Facebook, X, and Bluesky. 

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Journal

Energy & Environment Nexus

DOI

10.48130/een-0026-0005 

Method of Research

Experimental study

Article Title

Optimal energy management of distributed energy resources for a hybrid residential microgrid

 

Wild yeast discovery enables non-GM brewing of ornithine-enriched craft beer

Researchers uncover a naturally derived yeast mutation that boosts ornithine production while preserving brewing performance for value-added fermentation

Nara Institute of Science and Technology

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Japanese researchers developed a nongenetically modified (non-GM) yeast that naturally produces 9× more ornithine, paving the way for functional craft beer.

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Credit: Professor Hiroshi Takagi from Nara Institute of Science and Technology, Japan, and Professor Akira Nishimura from Iwate University, Japan.

Ikoma, Japan—As consumer interest grows in foods and beverages with added nutritional value, brewers are exploring ways to improve fermentation itself rather than relying on post-production additives. Ornithine, a naturally occurring amino acid involved in several biological processes, has attracted attention as a promising ingredient for value-added products. However, increasing ornithine production in brewing yeast is difficult because the metabolic pathway is tightly regulated, making conventional improvement strategies challenging.

Addressing this challenge, Professor Hiroshi Takagi and his research team (Associate Professor Akira Nishimura, Assistant Professor Shota Isogai, and Dr. Ryoya Tanahashi) of the Laboratory of Fermentation Science at Nara Institute of Science and Technology, Japan, combined traditional microbial breeding with modern molecular analysis to develop a practical, non-genetically modified brewing yeast. Their findings were published in the Journal of Industrial Microbiology and Biotechnology on May 20, 2026. More information about the Laboratory of Fermentation Science is available at https://www.naist.jp/iri/takagi/.

The project began with a wild Saccharomyces cerevisiae strain isolated from a university campus, highlighting the continuing value of local microbial resources. Instead of using gene editing, the team applied chemical mutagenesis followed by selection with canavanine, a toxic arginine analog. This traditional breeding-compatible strategy produced hundreds of candidate strains, from which one mutant, ADHorn49, showed more than nine times higher intracellular ornithine levels than the original yeast.

Whole-genome sequencing revealed that the enhanced trait could be traced to a single genetic change. The researchers identified a Gly351Asp substitution in the ARG6 gene, which encodes a key enzyme in ornithine biosynthesis. Additional experiments showed that introducing this mutation into different industrial yeast backgrounds consistently increased ornithine accumulation. Structural modeling further suggested that the mutation may alter interactions within the enzyme and reduce the strength of normal metabolic regulation.

“Valuable microorganisms can still be discovered from local natural environments,” says Assoc. Prof. Nishimura (currently Professor at Iwate University), emphasizing that the work connects biodiversity exploration with modern fermentation science. He added that exploring wild yeast resources can create new opportunities for scientifically validated fermentation innovation.

Importantly, the improved yeast retained normal brewing performance. Fermentation tests showed that carbon dioxide production was comparable to that of the parental strain, while the mutant secreted significantly more ornithine into the brewing medium. The final fermentation broth contained 7.0 mg/L of free ornithine, demonstrating that the trait can be expressed under practical brewing conditions without compromising industrial usability.

“This study clearly demonstrates a practical non-genetically modified strategy that combines traditional microbial breeding with molecular understanding,” says Prof. Takagi. “By linking natural-environment yeast resources with modern fermentation biotechnology, we hope to support the development of value-added fermented foods and beverages.”

Overall, the study bridges traditional fermentation culture and cutting-edge biotechnology by combining wild yeast exploration, whole-genome sequencing, structural modeling, and nongenetically modified (non-GM) breeding. Beyond the possibility of producing ornithine-enriched craft beer, the approach could support the development of other value-added fermented foods and beverages while strengthening the role of microbial breeding in sustainable food innovation.

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Resource 
Title: Isolation and characterization of Saccharomyces cerevisiae mutants with ornithine accumulation for value-added craft beer brewing
Authors: Akira Nishimura, Shota Isogai, Koya Yamada, Ryoya Tanahashi, and Hiroshi Takagi
Journal: Journal of Industrial Microbiology and Biotechnology
DOI: 10.1093/jimb/kuag013
Information about the Laboratory of Fermentation Science can be found at the following website: https://www.naist.jp/iri/takagi/

About Nara Institute of Science and Technology (NAIST), Japan
Established in 1991, Nara Institute of Science and Technology (NAIST) is a national university located in Kansai Science City, Japan. In 2018, NAIST underwent an organizational transformation to promote and continue interdisciplinary research in the fields of biological sciences, materials science, and information science. Known as one of the most prestigious research institutions in Japan, NAIST lays a strong emphasis on integrated research and collaborative co-creation with diverse stakeholders. NAIST envisions conducting cutting-edge research in frontier areas and training students to become tomorrow's leaders in science and technology.

About Professor Akira Nishimura from Iwate University, Japan
Dr. Akira Nishimura is a Professor in the Department of Food and Agricultural Sciences, Faculty of Agriculture, Iwate University, Japan. He earned his Ph.D. from the Division of Biological Science, Nara Institute of Science and Technology, in 2012. His research spans applied microbiology, applied molecular and cellular biology, proline metabolism, protein engineering, yeast genetics, activated sulfur molecules, and redox biology. He has authored about 60 scientific publications and received the Japan Society for Bioscience, Biotechnology, and Agrochemistry (JSBBA) Award for Young Scientists in 2023.

About Professor Hiroshi Takagi from Nara Institute of Science and Technology, Japan 
Dr. Hiroshi Takagi is a Professor Emeritus and Specially Appointed Professor at the Strategic Initiative for Research and Innovation, Nara Institute of Science and Technology, Japan. He earned his doctoral degree in agriculture from the University of Tokyo. His research focuses on applied molecular and cellular biology, applied biochemistry, and applied microbiology, with a particular interest in microbial metabolism and cellular functions. He has published 256 scientific papers. Notably, he received the Medal with Purple Ribbon for achievements in applied microbiology. He also received the JSBBA Award and the Society Award of the Society for Biotechnology, Japan (SBJ).

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Journal

Journal of Industrial Microbiology & Biotechnology

DOI

10.1093/jimb/kuag013 

Method of Research

Experimental study

Subject of Research

Cells

Article Title

Isolation and characterization of Saccharomyces cerevisiae mutants with ornithine accumulation for value-added craft beer brewing