Volcanic bubbles help foretell the fate of coral in more acidic seas

By 2100 Australian and global coral reef communities will be slow to recover, less complex, and dominated by fleshy algae, as high carbon dioxide changes ocean chemistry

Australian Institute of Marine Science

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A research vessel over volcanic seeps in PNG

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Credit: © AIMS | Katharina Fabricius

An international study published today in Communications Biology has used unique coral reefs in Papua New Guinea to determine the likely impact of ocean acidification on coral reefs in the face of climate change.

Oceans are becoming more acidic as they absorb carbon dioxide from the atmosphere, and that acid will dissolve coral limestone. But it’s hard to predict what impact this will have on whole ecosystems from studies using aquariums and models.

The research team, led by the Australian Institute of Marine Science (AIMS), studied entire coral reefs, locally enriched with CO2 that is seeping from the sea floor, near some of Papua New Guinea’s remote shallow submarine volcanoes.

Dr. Katharina Fabricius, a coral researcher at AIMS in Townsville and senior author on the paper, says the research has revealed which species can thrive under lifelong exposure to elevated CO2.

“These unique natural laboratories are like a time machine,” said Dr Fabricius.  

“The CO2 seeps have allowed us to study the reefs’ tolerance limits and make predictions. How will coral reefs cope if emissions are in line with the Paris Agreement level emissions? How will they respond to higher CO2 emissions scenarios?”

In 2000 Dr Fabricius came across bubbles of gas emerging through coral reefs while surveying species in Milne Bay, about 500 km east of Port Moresby. In 2009, as ocean acidification emerged as an issue, she thought back to that experience, had samples of the gas analysed and discovered it was nearly pure CO2.

The scene was set for the creation of a unique living laboratory and a decade-long research program to study how tropical marine ecosystems may adapt and how organisms acclimatise after generations of exposure to high CO2. 

Dr Sam Noonan, also from AIMS and first author on the paper, said: “These Papua New Guinea reefs are telling us that with every bit of increase in CO2, we will see fewer corals and more fleshy algae. Importantly, we also found far fewer baby corals, which means reefs won’t be able to grow and recover quickly. That has implications for all the species that depend on them, including humans. Many coastal communities depend on fish that start their lives using coral reefs for shelter and food.”

Oceans are slightly alkaline with a pH of 8.0, but their acidity has already increased by 30%. As CO2 emissions rise, the ocean pH is predicted to decline further down to a pH of 7.8 by the year 2100.

“By studying organisms at 37 sites along a 500-metre gradient of CO2 exposure, we were able to see what happens as CO2 increases. There was no sudden collapse or tipping point, instead, as the CO2 increased, we saw fleshy algae became dominant, replacing and smothering coral and calciferous algae,” Dr Fabricius said.

The reefs are hard to reach, requiring a flight into Papua New Guinea, a second to Milne Bay Province, then six hours in a boat.

“The coral reefs in Milne Bay are amazing, and the local people so welcoming. It was a real privilege to work at their reefs with these volcanic CO2 seeps, which are globally unique,” Dr Fabricius continued.

“Ocean acidification is a massive global problem, which has been understudied and underreported to date. This research is a first of its kind, presenting unique field data and allowing us to assess how whole communities change in the real world.

“We have observed coral reefs starting to change in response to CO2 gradients in the Great Barrier Reef. The Papua New Guinea reefs tell us what will happen next.

“The more CO2 we emit into the atmosphere, the greater the changes will be to coral reefs and the coastal communities which depend on them. This is on top of the impact of global warming and sea level rise.”

The research was conducted with colleagues from The University of Western Australia and Saudi Arabia.

Only bolder corals are found in areas of severe ocean acidification, (pH < 7.7, as predicted when atmospheric CO2 concentrations reach 1000 ppm). Coral reefs cease to exist at such conditions.

Credit

© AIMS | Katharina Fabricius

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Journal

Communications Biology

DOI

10.1038/s42003-025-08889-w 

Method of Research

Observational study

Subject of Research

Not applicable

Article Title

Progressive changes in coral reef communities with increasing ocean acidification

Article Publication Date

24-Nov-2025

 

Snakebites: COVID vaccine tech could limit venom damage

University of Reading

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A Bothrops-species snake. Imagen by University of Reading PhD student Gnaneswar Chandrasekharuni

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Credit: Gnaneswar Chandrasekharuni

The same technology used in COVID-19 vaccines could help prevent muscle damage from snakebites, according to a new study published in Trends in Biotechnology today [24 November]. 

Scientists from the University of Reading and the Technical University of Denmark tested whether mRNA technology could be used to protect against the damage caused by the venom of the Bothrops asper snake, found in Central and South America. This snake's venom destroys muscle tissue, often leaving victims with permanent disabilities even after receiving standard treatment. 

The research team wrapped specific mRNA molecules in tiny fat particles that, when injected into muscle, teach cells to produce protective antibodies, preventing venom damage. The treatment could significantly limit the injury and impacts caused by snakebites, which kill around 140,000 people worldwide and cause 400,000 permanent disabilities each year.  

Professor Sakthi Vaiyapuri, lead author of the study from the University of Reading, said: "For the first time, we've shown that mRNA technology can protect muscle tissue from snake venom-induced damage. This opens a completely new door for treating snakebites, particularly the local injuries that current antivenoms struggle to prevent.” 

Professor Andreas Laustsen, who co-led this study from the Technical University of Denmark said:  "We tested this treatment on snake venom, but this technology could be even more useful for other conditions where toxins cause harm gradually. For example, it might help block harmful toxins produced by bacteria during infections." 

Shielding muscles from damage 

Current antivenoms work well against toxins in the bloodstream but struggle to reach damaged muscle tissue around the bite site. In laboratory tests using human muscle cells, the new treatment reduced damage from both a single toxin and whole venom. The protective antibodies appeared within 12-24 hours of mRNA injection. In mice, a single injection of mRNA protected muscle tissue from toxin-induced injury when given 48 hours before exposure to the venom. 

The treatment reduced key signs of muscle damage. Mice that received the mRNA treatment before being exposed to the toxin showed lower levels of enzymes such as creatine kinase and lactate dehydrogenase, which are released when muscle is injured. The treatment also preserved healthy muscle structure. 

The researchers say their approach could work alongside traditional antivenoms. Standard treatments handle toxins in the blood, while mRNA-delivered antibodies could protect local tissues that antivenoms cannot reach as well as neutralise the toxins in the circulation. 

Tackling remaining challenges 

The research team says various challenges remain before the new treatment could help patients. The antibodies take hours to develop, and the treatment currently targets only one toxin. Future versions would need to protect against multiple venom components. Storage in remote areas without refrigeration also presents difficulties. 

Professor Vaiyapuri said: "We now need to expand this approach to target multiple venom toxins and solve storage challenges for rural areas, as well as ensure faster production of antibodies in tissues. The potential to reduce disabilities among snakebite victims is significant." 

The team plans to develop treatments targeting additional toxins and test whether the approach works when given after a bite occurs. 

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Journal

Trends in Biotechnology

DOI

10.1016/j.tibtech.2025.10.017 

Article Title

Intramuscular delivery of mRNA-encoded single-chain variable fragments prevents myotoxin II-induced skeletal muscle damage in a preclinical model

Article Publication Date

24-Nov-2025

 

Pusan National University researchers develop model to accurately predict vessel turnaround time

Dynamic operation indicators improve vessel turnaround forecasting accuracy, boosting berth planning and overall port efficiency

Pusan National University

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This study presents a time-series forecasting framework that integrates queuing-based operation indicators to improve vessel turnaround time prediction accuracy by up to 28%. Rather than directly optimizing waiting times, the research establishes a foundational predictive model that can support future efforts to enhance port efficiency and resource management.

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Credit: Professor Hyerim Bae from Pusan National University, Korea

In the 21st century, as global trade expands and cargo volumes surge, ports face mounting pressure to operate efficiently. A key challenge lies in accurately predicting vessel turnaround time (VTT)—the period between a ship’s arrival and departure—which directly influences scheduling, congestion management, and energy use. Traditionally, forecasting methods have relied on static factors, such as vessel specifications or container volumes, which fail to capture the highly dynamic nature of port operations.

To address this gap, a team of researchers led by Professor Hyerim Bae and Master’s student Daesan Park from Department of Industrial Engineering at Pusan National University, South Korea, has devised an innovative time-series approach to VTT forecasting via queuing-based operation indicators (OIs). Their findings, published in Volume 69, Part B of the journal Advanced Engineering Informatics on 1 January 2026, were made available online on 15 October, 2025.

This new framework introduces operation indicators as quantitative measures of a system’s changing state, based on queueing theory. These indicators are derived from operational parameters such as arrival rates, service rates, and variability, calculated separately for berth and yard stages, and then linked to represent their interdependence. Unlike static models, this approach captures time-varying fluctuations in congestion, workload, and inter-stage interactions, offering a dynamic picture of how port systems evolve over time. Each variation in the OI corresponds to measurable changes in system behavior, effectively bridging theoretical modeling with real-world operations.

By feeding these dynamic indicators into time-series deep learning models, the framework learns how short-term variations in operational load affect overall turnaround performance. The result is a data-driven, explainable forecasting model that translates complex, fluctuating port activities into accurate and actionable predictions.

“Our framework can be directly applied to port operations to enhance berth scheduling, predict congestion, and optimize the allocation of cranes, trucks, and labor, leading to shorter vessel turnaround times and reduced energy consumption,” explains Prof. Bae. “Its strength lies in modeling how 'interconnected stages, such as vessel berthing, container handling, and yard transfer, affect one another through time. This perspective allows our framework to be extended naturally to other multi-stage service systems beyond ports.”

Potential applications range widely. In airports, similar indicators could help anticipate downstream delays in aircraft handling. In hospitals, they could model patient flow from registration to treatment, aiding workload balance. In urban transport, queue-based modelling could predict how congestion spreads through networks. In manufacturing, the framework could help prevent bottlenecks by mapping interdependencies between production lines and logistics systems.

As Mr. Park concludes, “By viewing each system as a chain of interdependent operations, our approach transforms complex processes into measurable, predictive indicators. It enables smarter, more reliable, and sustainable management systems that can improve everyday experiences—from faster travel and healthcare to more efficient production and mobility.”

 

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Reference
DOI: 10.1016/j.aei.2025.103974

 

About Pusan National University
Pusan National University, located in Busan, South Korea, was founded in 1946 and is now the No. 1 national university of South Korea in research and educational competency. The multi-campus university also has other smaller campuses in Yangsan, Miryang, and Ami. The university prides itself on the principles of truth, freedom, and service and has approximately 30,000 students, 1,200 professors, and 750 faculty members. The university comprises 14 colleges (schools) and one independent division, with 103 departments in all.
Website: https://www.pusan.ac.kr/eng/Main.do

 

Prof. Hyerim Bae
Professor Hyerim Bae is Dean of the Graduate School of Data Science and Professor of Industrial Engineering at Pusan National University, Republic of Korea. He directs the Human-Centered Carbon Neutral Global Supply Chain Research Center and serves as CEO of SmartChain Co., Ltd. His research focuses on AI-driven analytics, process mining, and sustainable logistics, particularly for smart ports and digital supply chains. He is an Associate Editor of IEEE Transactions on Big Data and the International Journal of Innovative Computing, Information, and Control. He contributes to national policy as a member of the Presidential Advisory Council on Science & Technology.
Lab: https://pnubaelab.github.io/
ORCID: 0000-0003-2602-5911

 

Daesan Park
Daesan Park is a Master’s student in Industrial Data Science and Engineering at Pusan National University, Republic of Korea. He received his B.S. degree in Industrial Engineering from Pusan National University. His research focuses on data-driven modeling of complex operational systems, with emphasis on queueing theory, process mining, and time series forecasting using deep learning. His interests include developing predictive frameworks for logistics and healthcare systems under uncertainty and dynamic conditions.
ORCID: 0009-0008-2796-8632
 

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Journal

Advanced Engineering Informatics

DOI

10.1016/j.aei.2025.103974 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Time-series approach to vessel turnaround time forecasting using queuing-based operation indicators

Article Publication Date

1-Jan-2026

COI Statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hyerim Bae reports article publishing charges was provided by National Research Foundation of Korea. Hyerim Bae reports equipment, drugs, or supplies was provided by Korea Institute of Marine Science and Technology Promotion. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.