Fruit fly wing disc. (Mark Alber/UCR)
Fruit fly wing disc. (Mark Alber/UCR)
Tyler O'Neal, Staff Editor LATEST

Studying fruit fly wings with supercomputers: Insights into birth defects, regeneration

Cutting-edge research harnesses the incredible computational power of supercomputers to shed light on the development of fruit fly wings and unlock the potential for understanding and treating human birth defects. This breakthrough paves the way for tissue regeneration and offers optimism for a future where defects can be corrected, transforming lives.

In a remarkable scientific endeavor, researchers at the University of California, Riverside (UCR) have harnessed the immense capabilities of supercomputers to delve into the intricacies of fruit fly wing development. This investigation offers an unprecedented window into the process of tissue formation and gives rise to a promising avenue for understanding and treating birth defects in humans.

Traditionally, biologists have focused on studying individual cells to comprehend tissue development. However, the UCR research team took a groundbreaking approach by simulating the interaction of multiple cells using some of the most powerful supercomputers in California. By examining the mechanical properties of cells, including elasticity and fluid pressure, alongside the division and transformation of a group of diverse cell types called a 'wing disc,' scientists have made astonishing discoveries.

Mark Alber, UCR distinguished mathematics professor and senior co-author of the study, explained, "We modeled hundreds of cells, trying to figure out how they interact with each other, in this case, to become the wing of a fruit fly." This effort has revealed a fascinating transformation in the wing disc during its development.

In the earlier stages, the wing disc appears uniformly curved. However, as development progresses, the top retains its curvature while the bottom flattens out. Jennifer Rangel Ambriz, a UCR mathematics doctoral student and co-first author of the paper, describes this phenomenon as the disc transitioning from something flat to a rainbow-like shape. Understanding the cause of this shape is vital, as improper development can prevent the fruit flies from flying or even surviving.

The researchers have identified that a subcellular structure known as actomyosin plays a significant role in the development process, especially regarding the lower wing disc's flattening. Actomyosin is a dynamic network of actin fibers that influences the stiffness and height of the cells. During cell division and growth, actomyosin pushes the nuclei of different cells back and forth, ultimately shaping the individual cells comprising the wing disc.

Moreover, actomyosin connects with a crucial component, the extracellular matrix (ECM), composed of collagen. The cells within the wing disc adhere to the ECM, preventing them from drifting too far apart, particularly during division. The flexibility or stiffness of the ECM is also crucial for tissue shape and development.

Looking ahead, the researchers aspire to gain a deeper understanding of the genetic and chemical signals that impact actomyosin. While mechanical factors such as pressure and cell membrane surface tension influence tissue shape, it is believed that various chemical signals also play a significant role.

This pioneering project, supported by a grant from the National Science Foundation and led by Mark Alber, holds immense promise. Collaborator Weitao Chen of UCR aims to unravel the mechanisms that can potentially restore damaged tissues to their normal function.

Alber highlights the broader implications of their findings, stating, "What we know now about factors that affect tissue development could have applications beyond fruit flies and might enable tissue regeneration in humans or animals." The researchers harbor hope that their discoveries will not only contribute to correcting defects in human tissue formation but also forge connections between the factors controlling tissue development and specific genes associated with certain birth defects, ultimately enabling their reprogramming or correction.

This groundbreaking research exemplifies the transformative potential of supercomputers in enhancing our understanding of complex biological processes. By combining diverse expertise and leveraging the immense computational power at their disposal, scientists are paving the way for groundbreaking breakthroughs in the field of developmental biology.

As our knowledge and technological capabilities continue to advance, the possibilities for tissue regeneration and the correction of birth defects become increasingly tangible. The optimism surrounding this research is founded not only on the profound insights gained into wing development but also on its potential to positively impact human lives, creating a future where individuals can thrive despite early developmental challenges.

Jiankun Lyu Assistant Professor Evnin Family Laboratory of Computational Molecular Discovery
Jiankun Lyu Assistant Professor Evnin Family Laboratory of Computational Molecular Discovery

AI revolutionizes drug discovery, but building trust is crucial

Tyler O'Neal, Staff Editor LATEST

The emergence of artificial intelligence (AI) in drug discovery is one of the most promising developments in biomedicine today. While AI is already transforming diverse industries from healthcare to finance, its potential in developing new drugs offers hope for treating a range of diseases, including cancer and infectious diseases. However, researchers warn that before we can fully harness the power of AI in drug discovery, we need to establish trust in its ability to deliver reliable results.

At the forefront of this research is AlphaFold, a program developed by Google's DeepMind that uses deep learning algorithms to predict 3D structures of proteins. The program significantly optimizes the molecular characteristics and functions of proteins, paving the way for targeted drug development. Until recently, one of the biggest challenges in drug discovery was deciphering the complex 3D structures of proteins - a process that could take years of lab work. However, AlphaFold's AI-powered approach can achieve this feat in less than an hour, massively accelerating research timelines.

In a recent article published in Science, a team of researchers found that AlphaFold's predicted protein structures could replace traditional experimental methods of determining protein structures. The study’s results offer hope that AI could revolutionize the drug discovery process. However, the researchers emphasize the need for caution, particularly regarding the reliability and transparency of AI technology.

The first author of the study, Jiankun Lyu, remarked that "until now, studies suggested that AlphaFold2 is worse than experimental structures for structure-based drug screen tasks…We found in the two drug targets we tested that the algorithm's model is as reliable as experimental structures when used as inputs in our program to discover ligands, which are the binding molecules you need to identify for drug discovery."

Despite the promising findings, there are concerns about the limited availability of AlphaFold's latest model, which is a black box that can only be accessed through a server. Researchers calling for increased transparency worry about the implications of proprietary software for the field of drug discovery more generally.

In a statement to Science, Lyu expressed concern that "if they don’t open the model up to academic screening use, our present study will be the last of its kind. We would not have been able to run the current study on AlphaFold3. And without that, we can't know whether the new model is better for templating drug discovery."

However, many experts agree that the benefits of AI in drug discovery far outweigh the risks. Lyu remains optimistic about the potential of AI in this field, stating that "there’s a huge market for accurately predicting protein complexes in both basic research and industry...getting an accurate model is a crucial early step that also guides further drug optimization."

To fully leverage the potential of AI in drug discovery, there needs to be increased trust in the reliability of these algorithms to optimize and forecast the structure and characteristics of drug targets. More collaboration between AI researchers and drug discovery experts is essential to ensure that transparency issues are resolved in the field. AI's potential to accelerate drug discovery timelines and lead to new treatments for diseases is too great to ignore. As Lyu noted, we need to ensure that "AI is treated carefully now...[so that it] can help us discover new drugs and ultimately save lives."

Dr Chamila Gunasekara holds a sample of the low-carbon concrete. Credit: Michael Quin, RMIT.
Dr Chamila Gunasekara holds a sample of the low-carbon concrete. Credit: Michael Quin, RMIT.

Revolutionary modeling paves the way for sustainable fly ash concrete

Tyler O'Neal, Staff Editor LATEST

In an exciting leap forward for sustainable construction, researchers at Australia's RMIT University have developed a groundbreaking modeling technology that promises to revolutionize the use of fly ash in concrete production. This innovative approach, featured in a recent article published in the esteemed journal Cement and Concrete Research, not only showcases the long-term resilience of low-carbon concrete but also offers new opportunities for repurposing underutilized resources.

Coal-fired power plants generate over 1.2 billion tonnes of coal ash annually, with Australia alone producing a significant portion of this waste. As the country transitions to renewable energy sources, finding sustainable solutions for coal ash disposal becomes increasingly crucial. Simultaneously, the production of cement, a key ingredient in concrete, contributes to a staggering 8% of global carbon emissions. These challenges guided researchers at RMIT University to partner with AGL's Loy Yang Power Station and the Ash Development Association of Australia to develop a low-carbon concrete that incorporates an impressive 80% of coal fly ash as a substitute for cement.

What sets this research apart is the addition of nano additives, which modify the concrete's chemistry and allow for more fly ash to be used without compromising performance. This breakthrough enables the recycling of twice the amount of coal ash compared to current standards, thereby reducing waste and mitigating environmental hazards. Furthermore, the novel approach also extends to repurposing lower-grade and underutilized "pond ash," sourced from coal slurry storage ponds at power plants. This creates the potential for a significant and largely untapped resource for cement replacement.

To ensure the long-term performance and durability of this innovative concrete mixture, researchers harnessed the power of computer modeling. A computer program developed in collaboration with Dr. Yogarajah Elakneswaran from Hokkaido University allowed the team to forecast the time-dependent behavior of this new sustainable concrete. Dr. Yuguo Yu, an expert in virtual computational mechanics at RMIT, described the model as a remarkable tool to understand how materials will perform over time. By analyzing the interactions of various ingredients in the concrete, the researchers could optimize the mixture and enhance its density and compactness.

The modeling results have brought newfound confidence to the team as they work towards widespread adoption of this environmentally friendly concrete in infrastructure projects. The technology provides valuable insights into the performance and behavior of the low-carbon concrete, giving designers and engineers the ability to optimize their construction plans and guarantee long-lasting structures. Moreover, the broader implications of this modeling technology extend far beyond concrete, pointing towards a future of digitally assisted simulation in infrastructure design and construction.

This groundbreaking research was made possible by the ARC Industrial Transformation Research Hub for Transformation of Reclaimed Waste Resources to Engineered Materials and Solutions for a Circular Economy (TREMS). Led by Professor Sujeeva Setunge at RMIT, this research hub aims to minimize landfill waste and repurpose reclaimed materials for construction and advanced manufacturing. The collaboration brings together top scientists, researchers, and industry experts from multiple Australian universities and partners worldwide to drive sustainable innovations.

The promising results achieved by the RMIT research team paint an optimistic picture for the future of sustainable construction. By utilizing a higher percentage of fly ash, reducing cement requirements, and employing advanced modeling techniques, the development of low-carbon concrete opens the door to a truly circular economy in the construction industry. As the world strives to address climate change and reduce waste, the RMIT University team is leading the charge toward a greener, more sustainable future, where innovative solutions and modeling technologies play a vital role in reshaping the construction landscape.

The wavefunction matching technique involves replacing the short-distance part of the two-body wavefunction, which represents a realistic interaction with strong oscillations, with that of a simple, easily calculable interaction that shows no oscillations. This results in a new interaction that can be analyzed in quantum many-body calculations using perturbation theory. (Figure: Prof. Serdar Elhatisari)
The wavefunction matching technique involves replacing the short-distance part of the two-body wavefunction, which represents a realistic interaction with strong oscillations, with that of a simple, easily calculable interaction that shows no oscillations. This results in a new interaction that can be analyzed in quantum many-body calculations using perturbation theory. (Figure: Prof. Serdar Elhatisari)

Advancements in supercomputing: Unraveling complex physics problems

Tyler O'Neal, Staff Editor LATEST

In a groundbreaking development, an international research team has made significant strides in addressing a challenging physics problem by leveraging cutting-edge supercomputing techniques. The article, published by the University of Bonn, sheds light on the innovative use of advanced computational methods in unraveling complex quantum many-body systems, offering insights that are poised to reshape the realm of nuclear physics and quantum mechanics.

The research, led by Prof. Ulf-G. Meißner from the Helmholtz Institute for Radiation and Nuclear Physics at the University of Bonn outlines the successful application of a new method known as wavefunction matching. This novel approach aims to overcome the computational challenges inherent in ab initio calculations for systems with complex interactions, particularly in nuclear physics and quantum mechanics.

In the realm of nuclear physics, ab initio methods, which describe systems by understanding their elementary components and interactions, face limitations in conducting reliable calculations for systems with intricate interactions. The method of quantum Monte Carlo simulations, employed in these calculations, holds promise but grapples with a significant obstacle termed the "sign problem," leading to inaccuracies in final predictions. Figure: Prof. Serdar Elhatisari

The newfound wavefunction matching technique has emerged as a game-changer in addressing this computational predicament. By adeptly mapping complex problems to simpler model systems through wavefunction matching, the research team has paved the way for precise calculations of crucial properties such as atomic nuclei masses and radii. Prof. Meißner highlighted the successful calculation of nuclear properties, demonstrating a remarkable agreement with real-world measurements. This groundbreaking approach has empowered researchers to tackle calculations that were once deemed impossible due to computational obstacles.

Furthermore, the application of wavefunction matching in lattice quantum Monte Carlo simulations for light and medium-mass nuclei, neutron matter, and nuclear matter has yielded results that closely align with empirical data, heralding a new era of computational accuracy and reliability in nuclear physics.

A striking aspect of this research is the collaborative effort encompassing the University of Bonn, Gaziantep Islam Science and Technology University, Michigan State University, Ruhr University Bochum, South China Normal University, and several other esteemed institutions worldwide. The study received funding from prominent entities such as the U.S. Department of Energy, the German Research Foundation, the National Science Foundation of China, and the European Research Council, underscoring the significance of this research on a global scale.

As supercomputing capabilities continue to expand and evolve, the implications of this research extend beyond the realm of nuclear physics. Prof. Meißner emphasized the potential applications of the wavefunction matching technique in classical and quantum computing, foreseeing its utility in predicting properties of topological materials, particularly crucial in the realm of quantum computing.

The utilization of supercomputing resources has been instrumental in driving this transformative research forward. Notably, the computing time on supercomputers was a crucial component of this work, emphasizing the pivotal role of advanced computational infrastructure in addressing intricate physics problems.

The publication of this research signifies a significant milestone in the field of computational physics. The impact of this pioneering approach in addressing longstanding computational challenges sets the stage for further exploration and innovation in the ever-evolving landscape of supercomputing and quantum mechanics.

The strides made by the research team offer a glimpse into the vast potential of supercomputing in unlocking the mysteries of quantum many-body systems, inviting curious minds to ponder the far-reaching implications of this groundbreaking research.

It is always fascinating to witness how advanced computational techniques continue to unravel the complexities of physics, opening doors to new frontiers of knowledge and understanding. As the boundaries of supercomputing continue to expand, the promise of transformative discoveries beckons, inspiring a sense of wonder and curiosity among the scientific community and beyond.

Spanish study shows how AI can help us better understand turbulence

Tyler O'Neal, Staff Editor LATEST

A team of scientists from the Universitat Politècnica de València, led by Ricardo Vinuesa, has developed a new technique to study turbulence. Turbulence is a phenomenon that occurs when fluids, gases, and liquids behave irregularly and chaotically. It is present in many aspects of our daily lives, from the swirling currents of city air to the surging waters of oceans and rivers. Turbulence also contributes to energy dissipation in transportation modes, accounting for up to 15% of annual CO2 emissions.

The team's study marks a departure from traditional methodologies in the field of fluid mechanics and emphasizes the importance of understanding turbulence. Sergio Hoyas, a professor of aerospace engineering at UPV, notes that comprehending turbulence is essential to enhancing prevailing models in various domains. The team utilized artificial intelligence, specifically a neural network, to forecast the movement of turbulent flows. This approach enabled them to understand, rather than merely simulate or predict, turbulence.

The team leveraged a database of approximately one terabyte and used a selective application of the SHAP algorithm to deconstruct the evolution of flows by removing small structures. This process elucidated the consequential impact of these alterations. Experts have praised the method for its ability to extend existing knowledge amassed over four decades without the neural network being privy to any prior information on physics. Additionally, validation through experimental data obtained from the University of Melbourne attests to the real-world applicability of this method, offering a promising trajectory for future investigations into turbulence comprehension.

The study marks a significant achievement in the field of fluid mechanics by ushering in a paradigm shift in understanding turbulence dynamics. The integration of artificial intelligence not only broadens the horizons of turbulence research but also ignites a new era of inquiry into the intricate behaviors of fluid phenomena. This intersection of cutting-edge technology and scholarly pursuit promises to reshape our understanding of the intricate dance of fluids in the natural world.