The new model predicts several useful new drug combinations that may one day help treat heart attacks, according to researchers at The Ohio State University.

Typically caused by blockages in the coronary arteries — or the vessels that supply blood to the heart — these cardiovascular events are experienced by more than 800,000 Americans every year, and about 30% end up dying. But even for those who survive, the damage these attacks inflict on the muscles of the heart is permanent and can lead to dangerous inflammation in the affected areas of the heart.

Treatment to restore blood flow to these blocked passages of the heart often includes surgery and drugs, or what’s known as reperfusion therapy. Nicolae Moise, lead author of the study and a postdoctoral researcher in biomedical engineering at Ohio State, said the study uses mathematical algorithms to assess the efficacy of the drugs used to combat the potentially lethal inflammation many patients experience in the aftermath of an attack.

“Biology and medicine are starting to become more mathematical,” Moise said. “There’s so much data that you need to start integrating it into some kind of framework.” While Moise has worked on other mathematical models of animal hearts, he said that the framework detailed in the current paper is the most detailed schematic of myocardial infarctions in mice ever made.

The research is published in the Journal of Theoretical Biology.

Represented by a series of differential equations, the model Moise’s team created was made using data from previous animal studies. In medicine, differential equations are often used to monitor the growth of diseases in graph form.

But this study chose to model how certain immune cells like myocytes, neutrophils and macrophages — cells imperative to fighting infection and combating necrosis (toxic injury to the heart) — react to four different immunomodulatory drugs over a period of one month. These drugs are designed to suppress the immune system so that it doesn’t cause as much damaging inflammation in parts of the heart that were damaged.

This research focused on the drugs’ efficacy an hour after the mice were treated.

Their findings showed that certain combinations of these drug inhibitors were more efficient at reducing inflammation than others. “In medicine, math and equations can be used to describe these systems,” Moise said. “You just need to observe, and you’ll find rules and a coherent story between them.

“With the therapies that we’re investigating in our model, we can make the patient outcome better, even with the best available medical care,” he said.

Depending on their health beforehand, it can take a person anywhere from six to eight months to heal from a heart attack. The quality of care patients receive in those first few weeks could set the tone for how long their road to recovery will be.

Because Moise’s simulation is purely theoretical, it won’t lead to improved therapies anytime soon. More precise mouse data is needed before their work can become an asset to other scientists, but Moise said he does envision the model as a potential tool in the fight against the ravages of heart disease.

“Its going to be some years before we can actually integrate this kind of approach into actual clinical work,” Moise said. “But what we’re doing is the first step towards that.”

The co-author of the study was Avner Friedman, professor of mathematics at Ohio State. This research was supported by Ohio State’s Mathematical Biosciences Institute and the National Science Foundation.

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Materials provided by Ohio State University. Original written by Tatyana Woodall. Note: Content may be edited for style and length.

Although biogas and biomethane remain climate-friendlier than non-renewable alternatives, the researchers call for better monitoring and fixing of leaks to ensure biogas and biomethane continue to live up to their green credentials.

Biomethane and biogas are produced from the breakdown of organic matter including food, animal waste, energy crops, grass, or sewage sludge, making them renewable alternatives to natural gas, coal and oil. However, researchers at Imperial College London have identified potential pitfalls in energy supply chains for these climate-friendlier gases, concluding that more efforts should be made to reduce methane leakage.

Methane traps around 27 times the amount of heat in the atmosphere as carbon dioxide (CO₂) and is responsible for at least a quarter of global warming, according to the Intergovernmental Panel on Climate Change’s AR6 report.

The new Imperial study, published today in One Earth journal, found that supply chains for biomethane and biogas release more than twice as much methane as the International Energy Agency (IEA)’s previous estimation. It also reveals that 62 per cent of these leaks were concentrated in a small number of facilities and pieces of equipment within the chain, which they call ‘super-emitters’, though methane was found to be released at every stage.

The researchers say urgent attention is needed to fix the methane leaks, and knowing precisely where the majority of them are happening will help production plants to do so.

Lead author of the study Dr Semra Bakkaloglu, of Imperial’s Department of Chemical Engineering and Sustainable Gas Institute, said: “Biomethane and biogas are great candidates for renewable and clean energy sources, but they can also emit methane. For them to really help mitigate the warming effects of energy use, we must act urgently to reduce their emissions.

“We want to encourage the continued use of biogas and biomethane as a renewable resource by taking the necessary actions to tackle methane emissions.”

The researchers note that compared to the oil and gas industry, the biomethane industry suffers from poorly designed and managed production facilities as well as a lack of investment for modernisation, operation, and monitoring. Because oil and natural gas supply chains have been primarily operated by large companies with huge resources for decades, they have been able to invest more in leak detection and repair.

What is biomethane and biogas?

In response to the climate crisis, many countries are replacing heavily carbon-emitting sources of energy, like natural gas, coal and oil, with biomethane and biogas.

Although they are made from a mixture of CO₂ and methane, biomethane and biogas release less of both gases, making them greener energy alternatives.

However, these replacement fuels still release methane along their supply chains, such as at processing facilities and from long pipelines. This new analysis gives us a more thorough understanding of where, when, and how much methane is released from biomethane and biogas supply.

Analysing emissions

The researchers analysed 51 previously published studies on mobile methane measurements and site data taken from emission sources along the biomethane and biogas supply chain. They analysed the data and calculated the total methane emissions using a statistical model called Monte Carlo. This allowed them to consider all measurements of total supply chain emissions at each stage of the chain, which they then compared with the off-site emissions reported from whole-site measurements in previously published studies.

They found that the supply chains release up to 343 g of CO₂-equivalent methane per megajoule higher heating value, which may account for 18.5 megatonnes of methane per year. IEA estimates had reported emissions as just 9.1 megatonnes in 2021.

While overall methane emissions from biogas and biomethane are lower than those from oil and natural gas, the amount of methane released from their supply chains relative to total gas production is much higher than for oil and gas.

The data is reported in ‘CO₂ equivalents’ — a unit of measurement used to compare emissions across the oil and natural gas supply chain without interfering with downstream operations. This unit also allows us to compare the global-warming potential of various greenhouse gas emissions from different studies.

The researchers identified the reasons behind the leakiness of supply chains as intermittent emissions patterns, which make them harder to track; insufficient usage of process equipment; and inadequate operations and maintenance strategies. Since super-emitters are unlikely to remain constant over time, the researchers say that continual monitoring is required to detect intermittent emission patterns or unpredictable leaks from supply chains.

Dr Bakkaloglu said: “To prevent biogas methane emissions negating the overall benefits of biogas use, urgent attention is needed including continuous monitoring of biogas supply chains. We believe that with the proper detection, measurement, and repair techniques, all emissions can be avoided.We need better regulations, continuous emission measurements, and close collaboration with biogas plant operators in order to address methane emissions and meet Paris Agreement targets.”

“Given the growth in biomethane due to national decarbonisation strategies, urgent efforts are needed for the biomethane supply chain to address not only methane emissions but also the sustainability of biomethane.”

Co-author Dr Jasmin Cooper, also of the Department of Chemical Engineering, said: “Addressing the fundamental design issues and investment problems within the biofuel and methane industry would be a good starting point for stopping these leaks and preventing more from arising.”

The researchers are now focusing on the super-emitters within supply chains to better understand how to reduce them using the best available technologies.

This study was funded by Imperial’s Sustainable Gas Institute.

The new stellar systems contain only young, blue stars, which are distributed in an irregular pattern and seem to exist in surprising isolation from any potential parent galaxy.

The stellar systems — which astronomers say appear through a telescope as “blue blobs” and are about the size of tiny dwarf galaxies — are located within the relatively nearby Virgo galaxy cluster. The five systems are separated from any potential parent galaxies by over 300,000 light years in some cases, making it challenging to identify their origins.

The astronomers found the new systems after another research group, led by the Netherlands Institute for Radio Astronomy’s Elizabeth Adams, compiled a catalog of nearby gas clouds, providing a list of potential sites of new galaxies. Once that catalog was published, several research groups, including one led by UArizona associate astronomy professor David Sand, started looking for stars that could be associated with those gas clouds.

The gas clouds were thought to be associated with our own galaxy, and most of them probably are, but when the first collection of stars, called SECCO1, was discovered, astronomers realized that it was not near the Milky Way at all, but rather in the Virgo cluster, which is much farther away but still very nearby in the scale of the universe.

SECCO1 was one of the very unusual “blue blobs,” said Michael Jones, a postdoctoral fellow in the UArizona Steward Observatory and lead author of a study that describes the new stellar systems. Jones presented the findings, which Sand co-authored, during the 240th American Astronomical Society meeting in Pasadena, California, Wednesday.

“It’s a lesson in the unexpected,” Jones said. “When you’re looking for things, you’re not necessarily going to find the thing you’re looking for, but you might find something else very interesting.”

The team obtained their observations from the Hubble Space Telescope, the Very Large Array telescope in New Mexico and the Very Large Telescope in Chile. Study co-author Michele Bellazzini, with the Istituto Nazionale di Astrofisica in Italy, led the analysis of the data from Very Large Telescope and has submitted a companion paper focusing on that data.

Together, the team learned that most of the stars in each system are very blue and very young and that they contain very little atomic hydrogen gas. This is significant because star formation begins with atomic hydrogen gas, which eventually evolves into dense clouds of molecular hydrogen gas before forming into stars.

“We observed that most of the systems lack atomic gas, but that doesn’t mean there isn’t molecular gas,” Jones said. “In fact, there must be some molecular gas because they are still forming stars. The existence of mostly young stars and little gas signals that these systems must have lost their gas recently.”

The combination of blue stars and lack of gas was unexpected, as was a lack of older stars in the systems. Most galaxies have older stars, which astronomers refer to as being “red and dead.”

“Stars that are born red are lower mass and therefore live longer than blue stars, which burn fast and die young, so old red stars are usually the last ones left living,” Jones said. “And they’re dead because they don’t have any more gas with which to form new stars. These blue stars are like an oasis in the desert, basically.”

The fact that the new stellar systems are abundant in metals hints at how they might have formed.

“To astronomers, metals are any element heavier than helium,” Jones said. “This tells us that these stellar systems formed from gas that was stripped from a big galaxy, because how metals are built up is by many repeated episodes of star formation, and you only really get that in a big galaxy.”

There are two main ways gas can be stripped from a galaxy. The first is tidal stripping, which occurs when two big galaxies pass by each other and gravitationally tear away gas and stars.

The other is what’s known as ram pressure stripping.

“This is like if you belly flop into a swimming pool,” Jones said. “When a galaxy belly flops into a cluster that is full of hot gas, then its gas gets forced out behind it. That’s the mechanism that we think we’re seeing here to create these objects.”

The team prefers the ram pressure stripping explanation because in order for the blue blobs to have become as isolated as they are, they must have been moving very quickly, and the speed of tidal stripping is low compared to ram pressure stripping.

Astronomers expect that one day these systems will eventually split off into individual clusters of stars and spread out across the larger galaxy cluster.

What researchers have learned feeds into the larger “story of recycling of gas and stars in the universe,” Sand said. “We think that this belly flopping process changes a lot of spiral galaxies into elliptical galaxies on some level, so learning more about the general process teaches us more about galaxy formation.”

“The decision making process for e-scooter companies is complex. One of the first questions is where to locate the scooters — In the transportation network, where do e-scooters need to be placed to meet demand? The second question is how to distribute them. It gets more complicated when you introduce different electric charging methods, so that some scooters are being collected by paid contractors and others are being charged by customers, through incentives,” Cheng said.

As the researchers see it, the main benefits of shared mobility are threefold:

1. It’s an affordable first/last mile option for people who cannot afford to own a vehicle;
2. It’s environmentally friendly, using fewer resources more efficiently;
3. It offers a novel solution to the problem of traffic congestion.

The model they created can provide decision makers with a robust solution that enables low cost and high service quality.

What Does the Model Do?

Cheng, along with Dr. Xiaofeng Li, Dr. Yao-Jan Wu, and doctoral student Abolhassan Mohammadi Fathabad, created a two-stage stochastic programming model, for the planning and operation of large shared mobility networks in the presence of demand uncertainty. The model, using a random probability pattern, can answer several questions that arise at the company and policy-maker levels:

• In the face of uncertain demand, how many e-scooters should be placed in the network and in which locations?
• How many charging facilities are needed, where and at what capacities?
• How should the network be evaluated and adjusted daily, to continue to meet demand while minimizing costs?

The model, developed using data from the City of Tucson, allows for optimal placement, charging, and rebalancing of unused (idle) scooters to meet demand in the most efficient manner possible. Successful operation of a shared mobility system requires both careful planning and strategies that minimize operational costs for the company while also increasing the customer satisfaction rate (i.e., an e-scooter is available when it is wanted). From a longer-term planning perspective, it is necessary to consider e-scooter demand patterns on a seasonal basis and plan accordingly for long-term economic benefits. The final report offers a detailed walk-through of the computational model.

Possibilities for Future Research

By predicting scooter rentals, relocations, idle periods, and charging times, the model offers e-scooter management companies a ready-made decision-making system to to effectively design and operate shared e-scooter systems, and thus help to ensure system reliability and cost-effectiveness.

In the future, this research could be extended in several directions. First, to increase the robustness in uncertainty modeling, researchers could develop a distributionally robust optimization framework (a modeling framework for decision-making under uncertainty). Another possible direction is to develop new approaches to solve the e-scooter planning problem when more variables are introduced that would require the model to be capable of solving more complex problems.

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Materials provided by Portland State University. Note: Content may be edited for style and length.

Using a surprisingly comparable process, an interdisciplinary team of Northwestern Engineering researchers pieced together a method to determine how different 2D materials respond to disorder — testing some materials that could possibly replace silicon in new transistors and sensors.

“The analysis method will lead to a better understanding of disorder potentials in 2D materials to help make faster transistors, as well as better gas sensors that can more easily discriminate different gases,” said Matthew Grayson, professor of electrical and computer engineering at the McCormick School of Engineering, and one of the study’s authors.

In the paper “Field-effect Conductivity Scaling for Two-dimensional Materials with Tunable Impurity Density” published June 16 in the journal 2D Materials, the investigators developed a method to determine the fingerprint of the neighboring disorder as seen by a 2D material.

In science, disorder refers to imperfections or nearby charges that might scatter the otherwise straight path of an electron. 2D materials like graphene are particularly susceptible to nearby disorder because they are literally several atoms thick, at most.

“Disorder characterization is paramount in understanding and improving the performance of 2D materials,” Grayson said. “This paper shows that there exists a universal curve that serves as a fingerprint of that disorder. Even though different doses of disorder appear to result in completely different behaviors, these behaviors all represent individual threads of an overall tapestry.”

This is where the similarity comes in between the science and games you play on your phone or printed newspaper.

Using 2D material samples developed by the Hersam and Dravid groups, Grayson and his team implemented a new method to measure electrical conductivity curves using a cryostat, a device that preserves samples at low temperatures for microscopic examination. At room temperature, the charges that constitute disorder are free to shift around until they reach equilibrium, but when frozen in the cryostat, the disorder is frozen in place.

Each individual conductivity curve resembles a puzzle piece. The researchers then utilized an empirical rule to piece together all the curves until they formed a complete a picture.

Sound familiar?

They then used physical arguments to understand why this rule works as well as it does. As a result, they solved the riddle of how each of the materials under study respond to a specific class of imperfections.

“The impressive continuity of this picture when all the puzzle pieces were in place inspired us to dig deeper into the physics to understand what the underlying reason must be for this behavior,” Grayson said. “The same mentality that the general public uses to solve their daily Wordle or crossword puzzle is applied here.”

These findings also have implications for 2D materials research moving forward.

“Instead of seeing individual devices made from the same 2D materials as a bunch of puzzle pieces that each have to be studied independently, you can now locate where a given sample fits into the previously solved puzzle,” Grayson said, “so that each individual piece is instantly recognized as part of a greater picture.

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Materials provided by Northwestern University. Original written by Brian Sandalow. Note: Content may be edited for style and length.