Toxic molds, fossil fuels, antibiotics linked to chemical intolerance: Survey

In Idalia’s wake, harmful mold poses major health risk

Peer-Reviewed Publication

UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO

SAN ANTONIO (Sept. 1, 2023) — What initiates chemical intolerance (CI)? In a newly released survey of thousands of U.S. adults, respondents most frequently cited exposures to biological sources, such as mold and algae “blooms,” and/or fossil fuels, their combustion products and synthetic chemical derivatives such as pesticides, plastics and persistent organic pollutants. 

It's an issue in the news, as toxic mold spawned by the moisture left behind by flood waters from Hurricane Idalia could lead to severe health problems for people who suffer from chemical intolerance. This mold also could initiate the condition in some individuals.

“Everyone should avoid prolonged exposure to mold whenever possible,” said physician-researcher Claudia Miller, MD, MS, from The University of Texas Health Science Center at San Antonio, also called UT Health San Antonio. “Research has increasingly shown that toxic mold is much more dangerous than was previously recognized.”

In the survey, published in the journal Environmental Sciences Europe, 17.5% of participants who attributed their illness to an initiating event cited mold exposure as the perceived cause of their chemical intolerance. CI is estimated to afflict up to 20% to 30% of Americans, Miller, senior author of the study, said.

Participants were queried about antibiotic use, as well. According to the results, prolonged courses of antibiotics were associated with an increased risk of CI.

The survey data also indicate that with each additional initiating exposure respondents can recall, the odds of their reporting CI nearly triple. 

“With climate change contributing to more severe storms and more intense flooding worldwide, the danger posed by toxic mold is likely to increase dramatically in the near future,” Miller said. “As mold exposure is known to be a major initiator, the likelihood of more and more people with chemical intolerance is also unfortunately on the rise."

TILT

The study furthers understanding about how a two-stage disease process called TILT (toxicant-induced loss of tolerance) begins. The survey asked 10,981 people to state their self-perceptions about the events that began the downward spiral through TILT and into chemical intolerance.

“TILT can develop rapidly, for instance after a pesticide exposure, or gradually if someone is working or living in a setting such as a moldy building,” Miller said. She first proposed TILT in 1996 and is professor emerita of family and community medicine at UT Health San Antonio. 

Unknown origins

“Initiating events commonly go unrecognized and therefore unreported, leaving triggers and symptoms as the only documented components,” Miller said. “This has thwarted our understanding of the actual causes of TILT.”

Participants completed an 80-question online survey called the Personal Exposure Inventory. It included items concerning individuals’ medical diagnoses and personal exposures including antibiotic use.

Chemical intolerance was assessed using the Quick Environmental Exposure and Sensitivity Inventory (QEESI©) developed by Miller 25 years ago. It is a validated, self-administered questionnaire now used worldwide to differentiate individuals with CI from the general population. One-fifth of survey respondents met the QEESI criteria for chemical intolerance. 

Toxic mold

Exposure to mold was the most frequently mentioned initiating event on the Personal Exposure Inventory. “In recent years, global warming has led to more rainfall, floods, hurricanes, roof leaks and water intrusion, resulting in increased mold growth indoors,” said paper co-author Raymond F. Palmer, PhD, a biostatistician and professor of family and community medicine at UT Health San Antonio.

Mold was followed in rank order by exposures to pesticides (cited by 13.8% of respondents), medical/surgical procedures (12.6%), remodeling/new construction (12.0%), fires/combustion products (7.2%) and breast implants (1.8%).

Antibiotics

Respondents answered questions pertaining to how many courses of antibiotics they had completed for specific types of infections. Antibiotics prescribed for infections categorized as skin, tonsil, gastrointestinal, prostate, sinus, wound and pneumonia were most strongly associated with chemical intolerance.

“Our search for the underlying causes of CI represents a much-needed addition to the CI/TILT literature, whose principal focus has been on triggers that elicit CI symptoms from day to day with no attempt to determine what initiated TILT,” Miller said.

'A cohesive narrative'

“Taken together, our data support the idea that the person who reports multiple symptoms, multiple intolerances and recurrent infections as well as a history of exposure events is sharing a cohesive narrative, one that points to physiological (as opposed to psychosomatic) explanations of their oft-confusing complaints,” she said.

Although certain exposures such as medical/surgical procedures may be difficult to avoid, reducing exposures to contaminants related to pesticide use, new construction/remodeling and mold is possible and should be the focus of efforts to prevent future CI/TILT, the authors wrote.

Digging in

Finally, they encourage practitioners who see patients with medically unexplained symptoms — currently one in four primary care patients — to consider administering the QEESI. “‘TILTed’ individuals who report brain fog, memory, mood and concentration difficulties often receive referrals to psychiatrists, psychologists or social workers who explore their psychosocial environments but do not ask about changes in their actual — physical and chemical — environments,” Miller said. “If initiating exposures such as pesticides, toxic mold, implants and combustion products are not stopped, sensitivities can spiral out of control.”  

Teaching in schools of medicine, public health, architecture and engineering has not kept pace with these toxicants, many of which are new to the planet since World War ll, Miller noted. This is exacerbated by energy conservation efforts that have increased exposures to indoor air toxicants, she said.  

Marilyn Brachman Hoffman

In their acknowledgments, the authors “thank the Marilyn Brachman Hoffman Foundation for generously funding this study and Marilyn Hoffman for her prescient bequest prioritizing research on toxicant-induced loss of tolerance. We are deeply grateful to the patients who participated in this groundbreaking study.”

Hoffman’s bequest specified research on TILT. “She suffered terribly from chemical, food and drug intolerances herself, but especially from not being believed by family members and her doctors,” Miller said. “She was a citizen-scientist who read all my papers and book, “Chemical Exposures: Low Levels and High Stakes,” co-authored with Nicholas Ashford, PhD, JD, of the Massachusetts Institute of Technology. 

“More than anything, Mrs. Hoffman wanted to discover the biomechanism for TILT,” Miller said. “She knew that it was essential for helping patients like herself. Her bequest has led to publication of the biomechanism for TILT in a series of papers over the past two years in Environmental Sciences Europe, a journal read by regulatory toxicologists around the world.” 

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If you suspect that you or a loved one has developed chemical intolerance or TILT, answer this brief, three yes-or-no question screening test, called BREESI. A positive response to any of the questions should lead to taking the more extensive, validated diagnostic questionnaire, the QEESI, or Quick Environmental Exposure and Sensitivity Inventory. People who have high scores on the QEESI are seen as likely to be chemically intolerant and are encouraged to share the information with their health care providers.

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What Initiates Chemical Intolerance? Findings from a Large Population-Based Survey of U.S. Adults

Claudia S. Miller, Raymond F. Palmer, David Kattari, Shahir Masri, Nicholas A. Ashford, Rodolfo Rincon, Roger B. Perales, Carl Grimes, Dana R. Sundblad

First published: Aug. 14, 2023, Environmental Sciences Europe

https://enveurope.springeropen.com/articles/10.1186/s12302-023-00772-x

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The University of Texas Health Science Center at San Antonio (UT Health San Antonio) is one of the country’s leading health science universities and is designated as a Hispanic-Serving Institution by the U.S. Department of Education. With missions of teaching, research, patient care and community engagement, its schools of medicine, nursing, dentistry, health professions, graduate biomedical sciences and public health have graduated more than 42,300 alumni who are leading change, advancing their fields and renewing hope for patients and their families throughout South Texas and the world. To learn about the many ways “We make lives better®,” visit UTHealthSA.org.

Stay connected with The University of Texas Health Science Center at San Antonio on Facebook, Twitter, LinkedIn, Instagram and YouTube.

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JOURNAL

Environmental Sciences Europe

DOI

10.1186/s12302-023-00772-x 

METHOD OF RESEARCH

Survey

SUBJECT OF RESEARCH

People

ARTICLE TITLE

What initiates chemical intolerance? Findings from a large population-based survey of U.S. adults

$26M NIH grant addresses environmental influences on child health

MSU, Henry Ford Health, University of Michigan, Wayne State University, and Michigan Department of Health and Human Services collaborate on statewide initiative

Grant and Award Announcement

MICHIGAN STATE UNIVERSITY

 

IMAGE: JEAN KERVER, ASSOCIATE PROFESSOR OF EPIDEMIOLOGY & BIOSTATISTICS AT MICHIGAN STATE UNIVERSITY’S COLLEGE OF HUMAN MEDICINE. view more 

CREDIT: COURTESY PHOTO MICHIGAN STATE UNIVERSITY.

EAST LANSING, Mich. – Backed by a $26 million federal grant, researchers at three Michigan universities, a leading health care system, and a state agency will continue a long-term study of how exposure to environmental factors during pregnancy and early childhood can impact health for a lifetime. 

The funding from the National Institutes of Health, or NIH, is for the second phase of a national research program called ECHO, which stands for the Environmental Influences on Child Health Outcomes, and includes a sample of mothers, infants and children from across the United States. The first phase began in 2016. 

“This award shows the research potential we have across the state,” said Jean Kerver, an associate professor of epidemiology and biostatistics at the Michigan State University College of Human Medicine and the lead principal investigator for the Michigan-based part of ECHO. “The partnership brings together some of the greatest research minds of our state. It’s definitely greater than the sum of its parts.” 

In addition to MSU, the partners include the University of Michigan, Wayne State University, Henry Ford Health and the Michigan Department of Health and Human Services.  

ECHO is not a single study but encompasses many research projects all over the country with the goal of improving the health of children as they grow into adults and for generations to come. The collaborative alliance of scientists in Michigan is called Child Health Advances from Research with Mothers, or CHARM. The goal of CHARM is to improve the health of mothers and children in Michigan. 

The investigators study the health effects of a broad range of environmental exposures that occur during pregnancy and early childhood. That includes air pollution, chemical exposures and inadequate nutrition, as well as societal factors, such as stress and poverty. 

Some of the child health outcomes studied include preterm birth, brain development and neurodevelopmental disorders, asthma and obesity.  

Nationally, ECHO has collected data from 105,000 participants, including more than 64,000 children. In Michigan, the cohorts include more than1,500 pairs of mothers and their children enrolled through 11 hospitals and 21 prenatal clinics around the state. In this second phase, researchers plan to enroll 500 more pairs of mothers and children from the Detroit, Flint and Traverse City areas, Kerver said. 

Kerver credited her predecessor as the lead principal investigator, Nigel Paneth, with forming the statewide partnerships. Although retired from a full-time faculty position, Paneth, an emeritus University Distinguished Professor of epidemiology and biostatistics and pediatrics and human development at the MSU College of Human Medicine, remains active in the ECHO program and other research. 

Charles Barone, a pediatrician at Henry Ford Health, has been key in maintaining clinical and stakeholder relationships.  

“It's both important and gratifying to receive such a strong commitment from the NIH in support of our affiliated Michigan institutions continuing to build upon the success of the ECHO program for an additional seven years,” said Barone. “This research helps us to better understand how environmental factors affect child health from birth through adolescence, and what can be done to mitigate and improve their health outcomes for generations to come.” 

Michael Elliott, professor of biostatistics at U-M, developed the hospital and prenatal clinic sampling plan that ensures results are representative of all births in the Lower Peninsula of Michigan.  

"This new grant will allow us to continue following our representative sample of Michigan births through infancy and childhood to assess how prenatal factors affect child health, pointing to ways to improve child health from birth on," Elliott said. "We will also leverage the relationships we developed at sampled hospitals in Detroit, Flint and Traverse City to continue recruiting mothers and babies in minority, low-income and rural communities, all areas at greater risk of poor child health. My work and the work of other researchers at the University of Michigan will be integral to these efforts." 

Douglas Ruden, a professor and director of epigenomics at Wayne State University’s Institute of Environmental Health Sciences, is joined by experts in immunology and toxicologists who study environmental exposures to both the mother and father. 

“The second Michigan ECHO grant is a tremendous opportunity to understand how a mother’s exposure to environmental chemicals and stressors affects the health of her children,” said Ruden. “This important alliance will impact the health of many around the U.S., now and into the future.”  

Kerver said she was excited when she learned that the NIH approved funding for the Michigan partners in the ECHO program. Some of the findings, she said, could immediately lead to better health for Michigan residents. 

“It absolutely should,” she said. “What we hope to do is solve health problems people in Michigan and across the U.S. have right now. That is the main thing. That’s what we’re all working for.” 

MEDIA CONTACTS

Geri Kelley, Michigan State University, 616-350-7976,  kelleyg3@msu.edu

Jeffrey Adkins, Henry Ford Health, 586-307-2027, jadkins6@hfhs.org

Kim North Shine, University of Michigan, 313-549-4995, kshine@umich.edu

Julie O’Connor, Wayne State University, 313-577-8845, julie.oconnor@wayne.edu

Lynn Sutfin, MDHHS, 517-284-4772, SutfinL1@michigan.gov

By Pat Shellenbarger.

Read on MSUToday.

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Michigan State University has been advancing the common good with uncommon will for more than 165 years. One of the world's leading research universities, MSU pushes the boundaries of discovery to make a better, safer, healthier world for all while providing life-changing opportunities to a diverse and inclusive academic community through more than 400 programs of study in 17 degree-granting colleges.

For MSU news on the Web, go to MSUToday. Follow MSU News on Twitter at twitter.com/MSUnews.

 

Seismologists use deep learning for improved earthquake forecasting

Peer-Reviewed Publication

UNIVERSITY OF CALIFORNIA - SANTA CRUZ

 

IMAGE: DAMAGE FROM A 2020 EARTHQUAKE IN PUERTO RICO. view more 

CREDIT: UNITED STATES GEOLOGICAL SURVEY

For more than 30 years, the models that researchers and government agencies use to forecast earthquake aftershocks have remained largely unchanged. While these older models work well with limited data, they struggle with the huge seismology datasets that are now available.

To address this limitation, a team of researchers at the University of California, Santa Cruz and the Technical University of Munich created a new model that uses deep learning to forecast aftershocks: the Recurrent Earthquake foreCAST (RECAST). In a paper published today in Geophysical Research Letters, the scientists show how the deep learning model is more flexible and scalable than the earthquake forecasting models currently used.

The new model outperformed the current model, known as the Epidemic Type Aftershock Sequence (ETAS) model, for earthquake catalogs of about 10,000 events and greater.

“The ETAS model approach was designed for the observations that we had in the 80s and 90s when we were trying to build reliable forecasts based on very few observations,” said Kelian Dascher-Cousineau, the lead author of the paper who recently completed his PhD at UC Santa Cruz. “It’s a very different landscape today.” Now, with more sensitive equipment and larger data storage capabilities, earthquake catalogs are much larger and more detailed

“We’ve started to have million-earthquake catalogs, and the old model simply couldn’t handle that amount of data,” said Emily Brodsky, a professor of earth and planetary sciences at UC Santa Cruz and co-author on the paper. In fact, one of the main challenges of the study was not designing the new RECAST model itself but getting the older ETAS model to work on huge data sets in order to compare the two. 

“The ETAS model is kind of brittle, and it has a lot of very subtle and finicky ways in which it can fail,” said Dascher-Cousineau. “So, we spent a lot of time making sure we weren’t messing up our benchmark compared to actual model development.”

To continue applying deep learning models to aftershock forecasting, Dascher-Cousineau says the field needs a better system for benchmarking. In order to demonstrate the capabilities of the RECAST model, the group first used an ETAS model to simulate an earthquake catalog. After working with the synthetic data, the researchers tested the RECAST model using real data from the Southern California earthquake catalog.

They found that the RECAST model — which can, essentially, learn how to learn — performed slightly better than the ETAS model at forecasting aftershocks, particularly as the amount of data increased. The computational effort and time were also significantly better for larger catalogs.

This is not the first time scientists have tried using machine learning to forecast earthquakes, but until recently, the technology was not quite ready, said Dascher-Cousineau. New advances in machine learning make the RECAST model more accurate and easily adaptable to different earthquake catalogs.

The model’s flexibility could open up new possibilities for earthquake forecasting. With the ability to adapt to large amounts of new data, models that use deep learning could potentially incorporate information from multiple regions at once to make better forecasts about poorly studied areas.

“We might be able to train on New Zealand, Japan, California and have a model that's actually quite good for forecasting somewhere where the data might not be as abundant,” said Dascher-Cousineau.

Using deep-learning models will also eventually allow researchers to expand the type of data they use to forecast seismicity.

“We’re recording ground motion all the time,” said Brodsky. “So the next level is to actually use all of that information, not worry about whether we’re calling it an earthquake or not an earthquake but to use everything."

In the meantime, the researchers hope the model sparks discussions about the possibilities of the new technology.

“It has all of this potential associated with it,” said Dascher-Cousineau. “Because it is designed that way.”

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JOURNAL

Geophysical Research Letters

DOI

10.1029/2023GL103909 

ARTICLE TITLE

Using Deep Learning for Flexible and Scalable Earthquake Forecasting

 

New research reveals Earth's ancient ‘breath’: Study reveals connection between atmospheric changes and mantle chemistry

Peer-Reviewed Publication

UNIVERSITY OF PORTSMOUTH

 

IMAGE: GRAPH SHOWING CHANGES IN ATMOSPHERIC CONDITIONS view more 

CREDIT: DR HUGO MOREIRA

An international team of scientists have uncovered an important link between Earth’s early atmosphere and the chemistry of its deep mantle.

The study, which was led by researchers at the University of Portsmouth and University of Montpellier, sheds new light on the evolution of life on our planet and the rise of atmospheric oxygen.

The team investigated magmas formed in ancient subduction zones, where portions of Earth’s crust sink back into the mantle, from a pivotal moment in Earth's history – the Great Oxidation Event (GOE). This event, which is estimated to have happened between 2.1 and 2.4 billion years ago, was a period of time when oxygen levels in Earth's atmosphere increased rapidly and transformed life and environments on Earth.

However, there has been little research into how atmospheric changes have left their mark on the Earth’s mantle.

The new study, published in the journal Nature Geoscience, examined the role of plate tectonics – the process by which our planet's outer shell moves and reshapes its surface – in cycling and exchanging elements between the atmosphere, Earth's surface, and the deep mantle. Until now, reliable methods to understand these interactions were elusive.

By studying magmas from before and after the GOE, the team found a shift from reduced to more oxidised magmas. This was a result of the deep subduction of oxidised sediments from mountains transformed into sediments during weathering and erosion that were then recycled into the mantle via subduction processes – revealing how sediment recycling provided atmospheric access to the mantle.

This discovery implies that these ‘whiffs’ of oxygen may have changed the mantle by contributing to increased oxidation of calc-alkaline magma, altering the composition of the continental crust, and leading to the formation of ore deposits on Earth.

Lead author, Dr Hugo Moreira from the University of Montpellier and visiting researcher at the University of Portsmouth, said: “With these findings, our understanding of Earth's ancient ‘breath’ has taken a significant leap forward. Not only does it provide crucial insights into Earth's geological evolution, but it also sheds light on how the deep Earth and its mantle are intimately connected to atmospheric changes. It provides us a better understanding of the relationship between Earth's external and internal reservoirs.

“Moreover, it raises fascinating questions about the role that oxygen played in shaping our planet's history and the conditions that paved the way for life as we know it.”

The research team used the ID21 beamline at the European Synchrotron Radiation Facility in France to analyse sulphur state in minerals found in two-billion-year-old zircon crystals from the Mineiro Belt in Brazil, which acted as time capsules, preserving their original composition. They discovered that minerals from magmas that crystallised before the GOE had a reduced sulphur state. However, after the GOE, these became more oxidised.

Dr Moreira said: “Mantle oxygen fugacity, in simple terms, is a measure of oxygen's ability to drive chemical reactions in magmas and is critical for understanding volcanic activity and ore formation. However, in the past, we lacked a reliable way to track changes in this parameter for ancient parts of Earth’s history – until now.

“It provides a powerful tool for understanding the relationship between Earth's external and internal reservoirs. Sulphur speciation and magma fugacity are dynamic parameters that can change throughout a magma's journey from formation to crystallization. While our study considered factors like pressure and temperature, further analyses are needed to trace the complete ‘fugacity path’ from magma generation to final crystallisation.”

Co-author Professor Craig Storey, Professor of Geology at the University of Portsmouth, said: “Our study opens exciting new avenues of research, offering a deeper understanding of the Earth's ancient past and its profound connection to the development of our atmosphere. It challenges us to ponder questions about the evolution of magma types over time and the intricate interplay between plate tectonics and atmospheric cycles.”

Dr Moreira added: “As we continue to probe the mysteries of Earth’s geological history, one thing is certain - there is much more to discover beneath the surface.”

The study involved researchers from the University of Portsmouth, the Universities of Brest, Montpellier and University of Sorbonne, (France), the Federal University of Ouro Preto and University of São Paulo (Brazil) and the European Synchrotron Radiation Facility.

Reduced apatite inclusions

Oxidised apatite inclusions

CREDIT

Dr Hugo Moreira

CAPTION

The European Synchrotron Radiation Facility

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JOURNAL

Nature Geoscience

METHOD OF RESEARCH

Imaging analysis

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Sub-arc mantle fugacity shifted by sediment recycling across the Great Oxidation Event’

TELL THE GOP

An ‘introspective’ AI finds diversity improves performance

Peer-Reviewed Publication

NORTH CAROLINA STATE UNIVERSITY

An artificial intelligence with the ability to look inward and fine tune its own neural network performs better when it chooses diversity over lack of diversity, a new study finds. The resulting diverse neural networks were particularly effective at solving complex tasks.

“We created a test system with a non-human intelligence, an artificial intelligence (AI), to see if the AI would choose diversity over the lack of diversity and if its choice would improve the performance of the AI,” says William Ditto, professor of physics at North Carolina State University, director of NC State’s Nonlinear Artificial Intelligence Laboratory (NAIL) and co-corresponding author of the work. “The key was giving the AI the ability to look inward and learn how it learns.”

Neural networks are an advanced type of AI loosely based on the way that our brains work. Our natural neurons exchange electrical impulses according to the strengths of their connections. Artificial neural networks create similarly strong connections by adjusting numerical weights and biases during training sessions. For example, a neural network can be trained to identify photos of dogs by sifting through a large number of photos, making a guess about whether the photo is of a dog, seeing how far off it is and then adjusting its weights and biases until they are closer to reality.

Conventional AI uses neural networks to solve problems, but these networks are typically composed of large numbers of identical artificial neurons. The number and strength of connections between those identical neurons may change as it learns, but once the network is optimized, those static neurons are the network.

Ditto’s team, on the other hand, gave its AI the ability to choose the number, shape and connection strength between neurons in its neural network, creating sub-networks of different neuron types and connection strengths within the network as it learns. 

“Our real brains have more than one type of neuron,” Ditto says. “So we gave our AI the ability to look inward and decide whether it needed to modify the composition of its neural network. Essentially, we gave it the control knob for its own brain. So it can solve the problem, look at the result, and change the type and mixture of artificial neurons until it finds the most advantageous one. It’s meta-learning for AI. 

“Our AI could also decide between diverse or homogenous neurons,” Ditto says. “And we found that in every instance the AI chose diversity as a way to strengthen its performance.”

The team tested the AI’s accuracy by asking it to perform a standard numerical classifying exercise, and saw that its accuracy increased as the number of neurons and neuronal diversity increased. A standard, homogenous AI could identify the numbers with 57% accuracy, while the meta-learning, diverse AI was able to reach 70% accuracy.
 
According to Ditto, the diversity-based AI is up to 10 times more accurate than conventional AI in solving more complicated problems, such as predicting a pendulum’s swing or the motion of galaxies.

“We have shown that if you give an AI the ability to look inward and learn how it learns it will change its internal structure – the structure of its artificial neurons – to embrace diversity and improve its ability to learn and solve problems efficiently and more accurately,” Ditto says. “Indeed, we also observed that as the problems become more complex and chaotic the performance improves even more dramatically over an AI that does not embrace diversity.”

The research appears in Scientific Reports, and was supported by the Office of Naval Research (under grant N00014-16-1-3066) and by United Therapeutics. John Lindner, emeritus professor of physics at the College of Wooster and visiting professor at NAIL, is co-corresponding author. Former NC State graduate student Anshul Choudhary is first author. NC State graduate student Anil Radhakrishnan and Sudeshna Sinha, professor of physics at the Indian Institute of Science Education and Research Mohali, also contributed to the work. 

-peake-

Note to editors: An abstract follows.

“Neuronal diversity can improve machine learning for physics and beyond”

DOI: 10.1038/s41598-023-40766-6

Authors: Anshul Choudhary, Anil Radhakrishnan, John F. Lindner, William L. Ditto, North Carolina State University Nonlinear Artificial Intelligence Laboratory; Sudeshna Sinha, Indian Institute of Science Education and Research Mohali
Published: Aug. 21, 2023 in Scientific Reports

Abstract:
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and physics-informed Hamiltonian neural networks learning Hénon–Heiles stellar orbits and the swing of a video recorded pendulum clock. Such learned diversity provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.
 

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JOURNAL

Scientific Reports

DOI

10.1038/s41598-023-40766-6 

METHOD OF RESEARCH

Computational simulation/modeling

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Neuronal diversity can improve machine learning for physics and beyond

Amsterdam UMC is building models to enable greater use of AI in the health care system

Grant and Award Announcement

AMSTERDAM UNIVERSITY MEDICAL CENTERS

80% of all patient data is unstructured. Notes from a conversation with a GP, the evaluation of a specialist in a university medical centre or even a recommendation from a pharmacist. While this 'unstructured’ data is no problem for the human eye, it presents an unsurmountable challenge to an AI-algorithm. One that is "preventing AI from reaching its full potential," in the view of Amsterdam UMC, Assistant Professor Iacer Calixto. To give AI the helping hand that it needs, Calixto is set to lead a project that will "tackle the important challenges that hinder its use in clinical practice,” thanks to funding from the NWO. 

"We need to devise methods that are human-centred and responsible by design if we want these methods to be implemented in practice," says Calixto. The project will build on Natural Language Processing (NLP) techniques that already underpin the increasingly popular, ChatGPT. Currently, the unstructured nature of this data means that software such as ChatGPT cannot be easily used in the health care sector. However, the software itself offers plenty of opportunities for the sector. With promises to improve data entry, decision making and to free up crucial time that doctors and nurses can instead spend on patient care.  

Ensuring Privacy is Maintained  

"Protecting the privacy of our patients is a top priority at Amsterdam UMC, and that isn't different when we are developing, testing or using AI-algorithms," says Mat Daemen, vice-dean of Research at Amsterdam UMC. 

To ensure that AI can also be used in a safe way, this project will also address issues relating to privacy. By developing new 'synthetic' patient records, based around simulated information. These records mimic real patient records, in order to facilitate healthcare and research, while protecting the information of the 'real' patients.  

"One of the main bottlenecks of doing research in healthcare is access to high-quality data to train and validate machine learning models. Part of our project will generate synthetic patient records that include not only structured but also unstructured data such as free-text highlights from a consultation with a GP. These synthetic records, though not from real patients, can still be very useful to enable easier access to high-quality healthcare data for researchers and clinicians," says Calixto. 

Responsibly Dutch 

Another sticking point for the use of AI in the Dutch health sector, is a rather more self-evident one: language. Software such as ChatGPT are built on language databases, and these are predominantly in English. By building new models that are trained on Dutch medical records, the project will increase the reliability of existing tools as well as making them easier to use for professionals on the wards or in the treatment room.  

This is a bold project that will ensure the Amsterdam UMC is one of the forces driving innovation in healthcare with artificial intelligence and natural language processing. Results obtained in this project, for instance, synthetic patient records will benefit the entire Dutch healthcare ecosystem, including other hospitals and university medical centres, says Calixto. 

The responsibility of this AI project is not only limited to the important goal of maintaining patient privacy. The project will also seek to remove any aspects of discrimination and unfairness that may exist in existing AI models. For Daemen, this is an essential condition for the use of AI in Amsterdam UMC, and something that this project has at its core. "This project is an important addition to the efforts of many experts in Amsterdam UMC and in the Amsterdam region to introduce and use AI tools in a human centred and responsible way," he concludes.   

 

Genomic model suggests population decline in human ancestors

Peer-Reviewed Publication

AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE (AAAS)

Between 800,000 and 900,000 years ago, the population of human ancestors crashed, according to a new genomic model by Wangjie Hu and colleagues. They suggest that there were only about 1280 breeding individuals during this transition between the early and middle Pleistocene, and that the population bottleneck lasted for about 117,000 years. The researchers say about 98.7% of the ancestral population was lost at the beginning of the bottleneck. This decline coincided with climate changes that turned glaciations into long-term events, a decrease in marine surface temperatures, and a possible long period of drought in Africa and Eurasia. Hu et al. developed a coalescence model that looks at divergence between gene lineages and can be used to estimate past population size, using it to analyze genomic sequences from 3154 people from 10 African and 40 non-African populations. The ancient “bottleneck was directly found in all 10 African populations, but only a weak signal of the existence of such was detected in all 40 non-African populations,” Hu et al. write. The proposed bottleneck also coincided with the time that many researchers think the last common ancestor of Denisovans, Neanderthals and modern Homo sapiens lived, but the bottleneck theory needs to be tested against the archaeological and fossil human evidence, Nick Ashton and Chris Stringer write in a related Perspective. “If, as seems likely, humans were widespread inside and outside of Africa in the period between about 800-900,000 years BP … whatever caused the inferred bottleneck was limited in its effects on the wider non-sapiens lineage populations, or any effects were short-lived,” the Perspective authors add.

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JOURNAL

Science

DOI

10.1126/science.abq7487 

ARTICLE TITLE

Genomic inference of a severe human bottleneck during the Early to Middle Pleistocene transition

Early ancestral bottleneck could’ve spelled the end for modern humans

Peer-Reviewed Publication

CHINESE ACADEMY OF SCIENCES HEADQUARTERS

 

IMAGE: THE CORE FORMULA OF OUR NEW INFERENCE METHOD IS SHOWN. THE IMAGE DEPICTS A CLIFF PAINTING, ILLUSTRATING THE POPULATION OF HUMAN ANCESTOR PULL TOGETHER TO SURVIVE THE UNKNOWN DANGER IN THE DARKNESS DURING THE ANCIENT SEVERE BOTTLENECK. view more 

CREDIT: IMAGE BY SHANGHAI INSTITUTE OF NUTRITION AND HEALTH, CAS

How a new method of inferring ancient population size revealed a severe bottleneck in the human population which almost wiped out the chance for humanity as we know it today.

An unexplained gap in the African/Eurasian fossil record may now be explained thanks to a team of researchers from China, Italy and the United States. Using a novel method called FitCoal (fast infinitesimal time coalescent process), the researchers were able to accurately determine demographic inferences by using modern-day human genomic sequences from 3,154 individuals. These findings indicate that early human ancestors went through a prolonged, severe bottleneck in which approximately 1,280 breeding individuals were able to sustain a population for about 117,000 years. While this research has illuminated some aspects of early to middle Pleistocene ancestors, there are many more questions to be answered since uncovering this information.

A large amount of genomic sequences were analyzed in this study. However, “the fact that FitCoal can detect the ancient severe bottleneck with even a few sequences represents a breakthrough,” says senior author Yun-Xin Fu, a theoretical population geneticist at University of Texas Health Science Center at Houston.

Researchers will publish their findings online in Science on August 31, 2023 (America Eastern Standard Time). The results determined using FitCoal to calculate the likelihood for present-day genome sequences found that early human ancestors experienced extreme loss of life and therefore, loss of genetic diversity.

“The gap in the African and Eurasian fossil records can be explained by this bottleneck in the Early Stone Age as chronologically. It coincides with this proposed time period of significant loss of fossil evidence,” says senior author Giorgio Manzi, an anthropologist at Sapienza University of Rome. Reasons suggested for this downturn in human ancestral population are mostly climatic: glaciation events around this time lead to changes in temperatures, severe droughts, and loss of other species, potentially used as food sources for ancestral humans.

An estimated 65.85% of current genetic diversity may have been lost due to this bottleneck in the early to middle Pleistocene era, and the prolonged period of minimal numbers of breeding individuals threatened humanity as we know it today. However, this bottleneck seems to have contributed to a speciation event where two ancestral chromosomes may have converged to form what is currently known as chromosome 2 in modern humans. With this information, the last common ancestor has potentially been uncovered for the Denisovans, Neanderthals, and modern humans (Homo sapiens).

We all know that once a question is answered, more questions arise.

“The novel finding opens a new field in human evolution because it evokes many questions, such as the places where these individuals lived, how they overcame the catastrophic climate changes, and whether natural selection during the bottleneck has accelerated the evolution of human brain,” says senior author Yi-Hsuan Pan, an evolutionary and functional genomics at East China Normal University (ECNU).

Now that there is reason to believe an ancestral struggle occurred between 930,000 and 813,000 years ago, researchers can continue digging to find answers to these questions and reveal how such a small population persisted in assumably tricky and dangerous conditions. The control of fire, as well as the climate shifting to be more hospitable for human life, could have contributed to a later rapid population increase around 813,000 years ago.

“These findings are just the start. Future goals with this knowledge aim to paint a more complete picture of human evolution during this Early to Middle Pleistocene transition period, which will in turn continue to unravel the mystery that is early human ancestry and evolution,” says senior author LI Haipeng, a theoretical population geneticist and computational biologist at Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences (SINH-CAS).

This research was jointly led by LI Haipeng at SINH-CAS and Yi-Hsuan Pan at ECNU. Their collaborators, Fabio Di Vincenzo at the University of Florence, Giogio Manzi at Sapienza University of Rome, and Yun-Xin Fu at the University of Texas Health Science Center at Houston, have made important contribution to the findings. The research was first-authored by HU Wangjie and HAO Ziqian who used to be students/interns at SINH-CAS and ECNU. They are currently affiliated with Icahn School of Medicine at Mount Sinai, and Shandong First Medical University & Shandong Academy of Medical Sciences, respectively. DU Pengyuan at SINH-CAS, and CUI Jialong at ECNU also contributed to this research.

The African hominin fossil gap and the estimated time period of chromosome fusion is shown on the right.

CREDIT

Image by Science

JOURNAL

Science

DOI

10.1126/science.abq7487 

ARTICLE TITLE

Genomic inference of a severe human bottleneck during the Early to Middle Pleistocene transition