Tyler O'Neal, Staff Editor DEVELOPER TOOLS

China creates the first set of large ensemble simulations with a global climate system model to reveal the role of internal climate variability

Since the Industrial revolution era began, global warming, Arctic sea ice melting, and increasing sea-level rise are likely attributed to human activity, according to the IPCC AR6 report. The climate change response to external forcings (including human activity) is non-linear and is affected by internal variabilities (IVs) generated mainly from internal processes in the climate or Earth system. Recently, scientists have found that IVs, such as the Pacific Decadal Oscillation or Inter-decadal Pacific Oscillation, and the Atlantic Multi-decadal Oscillation, will greatly impact the Walker Circulation and Global Monsoon throughout the next three decades. IVs are also important sources of uncertainties in understanding historical climate change, especially at the regional scale. Put succinctly, IVs are useful for studying climate change, mitigation strategies, and providing guidance for policy makers. Change in surface air temperatures (SATs) at different time periods (relative to 1961-1990) and the internal variabilities (The dots identify signal strength and are significant to the study). The line is the globally averaged surface air temperature. Below the year, the SAT change and the range of IVs are denoted. CREDIT Pengfei Lin

Climate system models aid IV studies by providing simulations, especially when employing single-model initial-condition large ensemble simulations, which are an ensemble of simulations tied to a single climate model under a particular radiative forcing scenario. The large ensemble simulations apply perturbations, or deviations from normal input, to the initial conditions of each member to create diverging weather and climate trajectories. The ensemble sizes of large ensemble simulations are subject to computational and resource limits similar to those used in previous studies. Recently, several modeling center research groups have conducted single-model initial-condition large ensemble simulations that are now possible with rapidly increasing computer abilities. 

Employing large ensemble simulations to study climate change has been a hotspot in climate research. For instance, the National Center for Atmospheric Research (NCAR) released a large ensemble simulation in 2015 that has been cited more than one thousand times. Until then, the ensemble sizes have featured no greater than 100 members, and, even today, few of ensemble simulations have 100 ensemble sizes. 

To study the impact of IVs on future global monsoon projections, the LASG ocean model team group from the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) produced a super-large ensemble simulation with 110 members from their FGOALS-g3 climate system model. The full breadth of their research is available in a data description paper entitled "The Super-large Ensemble experiments of CAS FGOALS-g3" now published in Advances in Atmospheric Sciences.

"The Super-large Ensemble experiments of CAS FGOALS-g3 are the first set of large ensemble simulations from a global climate system model named FGOALS-g3 developed by IAP, CAS," said lead author Bowen Zhao. "The large ensemble simulation has the largest sample numbers in the world."

Each member contains a simulation for the climate system model, including ocean, atmosphere, sea ice, and land components. Researchers fully sampled the different phases of decadal ocean variability as the initial model states under the standard CMIP6 historical forcing conditions. They also included the Shared Socioeconomic Pathway scenario (SSP5-8.5), which suggests very high greenhouse gas emissions. These simulations cover a period between 1850 to 2099. 

"Our assessment also shows that these ensembles are capable of accurately capturing surface air temperature response and land precipitation, including extreme climate events as well as external forcings, and we can quantify the internal variabilities." continued Zhao. "Having more than 100 simulations and their realizations helps us study rare events and improve our understanding of the impact of internal variability on forced climate changes."

Waseda University researchers develop RaptGen computational model that can be used for novel aptamer generation

Tyler O'Neal, Staff Editor DEVELOPER TOOLS

Oligonucleotides are short, single strands of synthetic DNA or RNA. Albeit small, these molecules play an important role in molecular and synthetic biology applications. One type of oligonucleotide—aptamers—can selectively bind to specific targets such as proteins, peptides, carbohydrates, viruses, toxins, metal ions, and even live cells. As they are similar to antibodies, they have a variety of uses in the fields of biosensors, therapeutics, and diagnostics. However, compared to antibodies, aptamers do not induce an immune reaction in our bodies and are easy to synthesize and modify. Moreover, an aptamer’s three-dimensional folding structure allows it to bind to a wider range of targets. Scientists at Waseda University develop a computational model that can generate novel aptamer sequences

Aptamers are usually generated by an in vitro selection and amplification technology called systematic evolution of ligands by exponential enrichment, or SELEX. Briefly, SELEX is based on repeated cycles of binding, separation, and amplification of nucleotides. This process results in an enriched pool of nucleotide sequences that are then analyzed for candidate selection. High-throughput SELEX (HT-SELEX) can generate a vast number of aptamer candidates, but current practically-applicable sequencing only allows us to evaluate a limited number of these candidates (approximately 106). Therefore, computational processes are essential to optimize the discovery of new aptamers.

Variational autoencoder (VAE, a type of machine learning approach)-based compound designs have been reported to be beneficial in the discovery of other small molecules. Now, a team of researchers led by Professor Michiaki Hamada of the Graduate School of Advanced Science and Engineering in Waseda University, Japan, introduced RaptGen, a VAE that can be used for aptamer generation. In their paper, they describe how RaptGen uses a VAE with a profile hidden Markov Model decoder to create latent spaces in which sequences can form clusters. By using this latent representation, RaptGen was able to generate aptamers that were not included even in the original sequencing data or HT-SELEX dataset.

When asked how exactly RaptGen could boost aptamer discovery, Professor Hamada states, “RaptGen first visualizes a latent space with a sequence motif, then generates multiple new aptamer sequences via this latent space. For example, it searches for optimized aptamer sequences in the latent space by considering additional information after analyzing the activity of a subset of sequences. Additionally, RaptGen enables the design of shortened (or truncated) aptamer sequences.”

The team also successfully evaluated RaptGen’s performance using real-world data, by subjecting it to data from two independent HT-SELEX datasets. RaptGen could generate aptamer derivatives in an activity-guided manner and provide opportunities to optimize their activities. “This is important as it means that RaptGen can generate sequences having desired properties, such as the inhibition of certain enzymes or protein-protein interactions,” Professor Hamada explains. The application of these molecules could open many doors in the future.

Moving forward, the team plans to conduct extensive studies evaluating if alternative models can improve the performance of RaptGen and whether RaptGen could advance RNA aptamer generation by using RNA sequences. The only drawbacks of using RaptGen are the high computational cost and increased training time, both of which can be improved in further studies.

Professor Hamada summarizes by saying, “To the best of our knowledge, RaptGen is the only data-driven method that can design and optimize truncated aptamers directly from HT-SELEX data. We believe that in due time, RaptGen will be recognized as a key tool for efficient aptamer discovery.”

Here’s to their vision of a healthy and long-lived society with better therapeutics!

Japanese researchers discover a common vibrational mechanism in amorphous solids including glasses

Tyler O'Neal, Staff Editor DEVELOPER TOOLS

Scientists from the Institute of Industrial Science at The University of Tokyo have used molecular dynamics simulations to better understand the unusual properties of amorphous solids, such as glass. They found that certain dynamical defects help explain the allowed vibrational modes inside the material. This work may lead to controlling the properties of amorphous materials. Researchers at the Institute of Industrial Science, The University of Tokyo studied the anomalous properties of amorphous solids, including glasses, using computer simulations, and found a common vibrational mechanism underlying them, which may help control the glass properties

Sometimes the expensive glass is advertised as “crystal”, but to material scientists, this could not be further from the truth. Crystals are formed by atoms arranged in orderly, repeating patterns, while glass is a disordered, amorphous solid. Scientists know that, at low temperatures, many disordered materials have properties that are very similar to each other, including specific heat and thermal conductivity. Additionally, these properties differ significantly from those of materials made from ordered crystals. Furthermore, at a certain frequency range, glassy materials have a larger number of available vibration modes than crystals, known in the field as the “boson peak”. While various theories have been proposed, the underlying physical mechanisms for these observations have remained a question of active research.

Now, scientists from The University of Tokyo have used sophisticated molecular dynamics supercomputer simulations to numerically calculate the transverse and longitudinal dynamic structure factors of model glasses over a wide range of frequencies. They found that string-like vibrational motions, in which curved lines of particles packed into a “C” shape inside the material can move together, were found to be important drivers of the anomalous effects. “These dynamical defects provide a common explanation for the origin of the most fundamental dynamic modes of glassy systems,” first author Yuan-Chao Hu says. In addition to the boson peak, these string-like dynamic defects may commit the types of fast and slow relaxation observed in the particles making up the glass.

This research has many important implications for both basic science and industrial applications because the boson peak is found in many systems, not just glasses. “We show that the boson peak originates from quasi-localized vibrations of string-like dynamical defects,” senior author Hajime Tanaka says. Being about to explain this feature will shed light on many other types of disordered materials. It will also benefit the many users of smart devices because almost all smartphones, tablets, and touchscreen laptops rely on glass materials that the findings of this study may improve.