Tiny pores in the cell nucleus play an essential role in healthy aging by protecting and preserving the genetic material. A team from the Department of Theoretical Biophysics at the Max Planck Institute of Biophysics in Frankfurt am Main and the Synthetic Biophysics of Protein Disorder group at Johannes Gutenberg University Mainz (JGU) has filled a hole in the understanding of the structure and function of these nuclear pores. The scientists found out how intrinsically disordered proteins in the center of the pore can form a spaghetti-like mobile barrier that is permeable to important cellular factors but blocks viruses or other pathogens. 

Human cells shield their genetic material inside the cell nucleus, protected by the nuclear membrane. As the control center of the cell, the nucleus must be able to exchange important messenger molecules, metabolites, or proteins with the rest of the cell. About 2,000 pores are therefore built into the nuclear membrane, each consisting of about 1,000 proteins.

For decades, researchers have been fascinated by the three-dimensional structure and function of these nuclear pores, which act as guardians of the genome: substances that are required for controlling the cell are allowed to pass, while pathogens or other DNA-damaging substances are blocked from entry. The nuclear pores can therefore be thought of as molecular bouncers, each checking many thousands of visitors per second. Only those who have an entrance ticket are allowed to pass.

How do the nuclear pores manage this enormous task? About 300 proteins attached to the pore scaffold protrude deep into the central opening like tentacles. Until now, researchers did not know how these tentacles are arranged and how they repel intruders. This is because these proteins are intrinsically disordered and lack a defined three-dimensional structure. They are flexible and continuously moving – like spaghetti in boiling water.

Combination of microscopy and supercomputer simulations

As these intrinsically disordered proteins (IDPs) are constantly changing their structure, it is difficult for scientists to decipher their three-dimensional architecture and their function. Most experimental techniques that researchers use to image proteins only work with a defined 3D structure. So far, the central region of the nuclear pore has been represented as a hole because it was not possible to determine the organization of the IDPs in the opening.

The team led by Gerhard Hummer, Director at the Max Planck Institute of Biophysics, and Edward Lemke, Professor of Synthetic Biophysics at Johannes Gutenberg University Mainz and Adjunct Director at the Institute of Molecular Biology Mainz (IMB) has now used a novel combination of synthetic biology, multidimensional fluorescence microscopy and supercomputer-based simulations to study nuclear pore IDPs in living cells.

"We used modern precision tools to mark several points of the spaghetti-like proteins with fluorescent dyes that we excite by light and visualize in the microscope," explained Lemke. "Based on the glow patterns and duration, we were able to deduce how the proteins must be arranged." And Hummer added: "We then used molecular dynamics simulations to calculate how the IDPs are spatially organized in the pore, how they interact with each other, and how they move. For the first time, we could visualize the gate to the control center of human cells."

Dynamic protein network as a transport barrier

 The tentacles in the transport pore take on a completely different behavior compared to what we knew before because they interact with each other and with the cargo. They move permanently like the aforementioned spaghetti in boiling water. So, in the center of the pore, there is no hole, but a shield of wiggly, spaghetti-like molecules. Viruses or bacteria are too big to get through this sieve. However, other large cellular molecules needed in the nucleus can pass as they carry very specific signals. Such molecules have an entry ticket, whereas pathogens usually do not. "By disentangling the pore filling, we enter a new phase in nuclear transport research," added Dr. Martin Beck, collaborator, and colleague at the Max Planck Institute of Biophysics.

"Understanding how the pores transport or block cargo will help us identify errors. After all, some viruses manage to enter the cell nucleus despite the barrier," Hummer summed up. "With our combination of methods, we can now study IDPs in more detail to find why they are indispensable for certain cellular functions, despite being error-prone. IDPs are found in almost all species, although they carry the risk of forming aggregates during the aging process which can lead to neurodegenerative diseases such as Alzheimer's," said Lemke. By learning how IDPs function, researchers aim to develop new drugs or vaccines that prevent viral infections and help healthy aging.

Researchers provide direct evidence that the magnetic properties of the novel icosahedral quasicrystals depend on the electrons-per-atom ratio

Quasicrystals (QCs) exhibit long-range order with crystallographically forbidden symmetries in their atomic structures. Among the discovered QCs, icosahedral QCs (i QCs) with unique geometry demonstrate interesting physical and magnetic properties. Previously synthesized i QCs with long-range magnetic order were unsuitable for further study due to the inclusion of an approximant crystal phase. Now, researchers at the Tokyo University of Science have successfully synthesized gold-gallium-dysprosium i QC with high phase purity and tunable magnetic properties.

Quasicrystals (QCs) have peculiar structures with interesting atomic arrangements. Although they are similar to crystals from the exterior, at the atomic scale, they lack periodicity despite being ordered. Such structural arrangements confer quasicrystals with symmetries and other special properties that are missing in crystals. In particular, icosahedral QCs (i QCs), which have a special geometric structure, show interesting magnetic properties. In a recent breakthrough, a research team led by Professor Ryuji Tamura from the Tokyo University of Science (TUS) in Japan has discovered ferromagnetic order in gold-gallium-gadolinium and gold-gallium-terbium i QCs. However, these i QCs have not been suitable for the further study of ferromagnetism in i QCs because they have also contained a large fraction of the approximant crystal (AC) phase. ACs have a similar structure to QCs, but as they are also magnetic, this interferes with studies on the magnetism of the QC phase alone. 

To bridge this gap, Professor Tamura's team has now synthesized a novel gold-gallium-dysprosium (Au-Ga-Dy) i QC. According to Professor Tamura, "The Au-Ga-Dy i QC is ferromagnetic, highly tunable, and has high phase purity". The research team, which included Mr. Ryo Takeuchi and Dr. Farid Labib from TUS, has published their findings in Physical Review Letters. This paper has been selected as Editor's Suggestion.

The new i QCs were prepared using mother alloys containing 15% Dy, 62-68% Au, and 23-17% Ga. The mother alloys were synthesized via arc-melting followed by rapid quenching. The resultant i QCs were studied using powder X-ray diffraction, electron microscopy, electron diffraction, and magnetic susceptibility measurements.

The researchers found that the synthesized i QC was polycrystalline with a highly pure ferromagnetic phase. They were further able to describe the mean-field-like nature of the ferromagnetic transition.

The researchers also discovered that the new i QCs exhibit a maximum Weiss temperature, a significant parameter in ferromagnetic transition, at an electrons-per-atom (e/a) ratio of 1.70, which aligns with previous findings for ACs. This discovery demonstrates that the magnetic properties of i QCs can be well-tuned using the Weiss temperature and e/a ratio (a parameter that indicates the variations in the Fermi energy of the i QC). Furthermore, these findings reveal that the balance of ferromagnetic and antiferromagnetic interactions, as well as the presence of exotic magnetic orders, can be tuned in i QCs by shifting the Fermi energy or adjusting the e/a ratio.

"The discovery of pure tunable ferromagnetic quasicrystals has the potential to revolutionize and expand the academic system based on crystals. Applying our findings to current theoretical work in the field, for example, in the realm of non-coplanar spin configurations such as hedgehog and whirling configurations, can lead to various nontrivial physical properties in i QCs, including anomalous and topological Hall effects," concludes Prof. Tamura.

These findings pave the way toward new frontiers of magnetic materials and advance the development of technologies such as magnetic data storage, spintronics, and magnetic sensors. 

In cereal crops, the number of new leaves each plant produces is used to study the periodic events that constitute the biological life cycle of the crop. The conventional method of determining leaf numbers involves manual counting, which is slow, labor-intensive, and usually associated with large uncertainties because of the small sample sizes involved. It is thus difficult to get accurate estimates of some traits by manually counting leaves.

Conventional methods have, however, been improved upon with technology. Deep learning has enabled the use of object detection and segmentation algorithms to estimate the number of plants (and leaves on these plants) in an area. There is, however, a roadblock to using these algorithms. They count leaf tips, which appear tiny in images, proving difficult to detect. Consequently, deep learning methods often fail to perform in actual field conditions. Aiming to solve this problem, a multinational research team developed a self-supervised leaf-tip counting method based on deep learning techniques, which yielded wheat leaf count with high accuracy. The study was led by Professor Shouyang Liu of the Nanjing Agricultural University located in Nanjing, Jiangsu Province, China. and was published online in Plant Phenomics on March 20, 2023.

Speaking about their work, Prof. Liu says, “We developed a high-throughput method to count the number of leaves on wheat plants by detecting leaf tips in RGB (red-green-blue) images. The Digital Plant Phenotyping platform (D3P) was used to simulate a large, diverse dataset of RGB images and corresponding leaf-tip labels of wheat plant seedlings. Over 150,000 images were generated, with over 2 million labels.”         

The researchers used domain adaptation—in which a neural network trained on a “source” dataset is applied to a “test” dataset, also referred to as a “target” dataset. This was achieved through deep learning techniques that mimic neural processes used by the human brain and use algorithms inspired by its structure and function.

Next, the researchers collected 2,763 RGB images of juvenile wheat fields from 11 locations spread across five countries. A variety of measures were used to create a robust and reliable source dataset—different types of cameras, varying imaging angles, and images with diverse soil backgrounds/light conditions were used. Besides capturing field images, the team also generated simulated wheat images, which were automatically annotated using the D3P. Domain adaptation was used to improve the realism of these images, which were then used to train the deep-learning models.

Six combinations of deep learning models and domain adaptation techniques were used in this study; the Faster-RCNN model with the CycleGAN adaptation technique demonstrated the best performance. This was evident from its high coefficient of determination (R2 = 0.94)—a measure that determines the goodness of fit of a model—and optimal root mean square error (RMSE = 8.7)—a standard way to measure the error of a model in predicting quantitative data.

Moreover, of the three factors evaluated for the performance of the leaf counting models, the light condition was found to be of utmost importance. On the other hand, leaf texture and soil brightness were found to be less important for performance, but the combination of all three factors was found to significantly improve the realism of the images. The results also revealed that a spatial resolution higher than 0.6 mm per pixel was required to ensure accurate identification of leaf tips.

Explaining the implications of their study, Prof. Liu says, “The resulting proposed deep learning method appears very attractive since it eliminates the tedious, expensive, and sometimes inaccurate manual labeling task by simulating images for which the labels are automatically generated. The images were also made more realistic using domain adaptation techniques.”           

The research team has made the trained networks available at https://github.com/YinglunLi/Wheat-leaf-tip-detection to facilitate further research in this area.  

It has been a dream for Earth scientists to have a numerical model that can better represent compound multiple-scale processes in the real-world Earth system. Apart from requiring a deep understanding of the physics of geo-fluid motions, developing such a model demands interdisciplinary advancement in the Earth sciences and high-performance supercomputing as well as software engineering. "The successful development of Earth system models with clouds and ocean submesoscale eddies permitted is a milestone in the advancement of understanding the Earth system," said Dr. Lixin Wu. 

Based on the newly-developed “Sunway” heterogeneous architecture supercomputer which has high-performance computing capability, Shaoqing Zhang, Lixin Wu, and Yang Gao, a group of scientists at the Ocean University of China, together with Shiming Xu, Haohuan Fu, and Zhao Liu, a group of professors and engineers at Tsinghua University and National Wuxi Supercomputing Center, organize a large cross-field group of scientists and engineers to take the challenges and develop new high-resolution Earth system models.

After resolving plenty of physical and engineering issues, the large group successfully developed a series of high-resolution coupled Earth system models consisting of 12, 9, and 5 km resolution atmosphere-land models and 15, 10, 5, and 3 km resolution ocean-ice models. “These models can meet the needs of multiscale interaction studies with different computational costs," said Dr. Shaoqing Zhang.

These high-resolution models can simulate cloud cells and ocean submesoscale vortex filaments to some degree. Therefore, they can bring a new understanding of weather-climate mechanisms from the perspective of cross-scale interactions. "The most exciting results from these new high-resolution models are that the major weather-climate extremes in the atmosphere and ocean are captured, stressing the importance of permitted clouds and ocean submesoscale eddies in modeling tropical cyclones and eddy-mean flow interactions,” said Drs. Shiming Xu and Yang Gao.

“The new heterogeneous many-core architecture high-performance supercomputer brings new opportunities for climate modeling once the optimization of heterogeneous architecture computing is efficiently implemented. The low work consumption of heterogeneous architecture computing complies with the ‘green’ future of the world,” said Drs. Haohuan Fu and Zhao Liu. 

The new high-resolution Earth system models lay the foundation for future efforts to sustain the advancement of the Earth sciences through modeling more complex biogeochemical processes and carbon cycling. “These models pave for further model development to resolve finer scales with even higher resolution and more realistic physics. For example, based on these results, development of a non-hydrostatic, cloud and ocean submesoscale resolving Earth system model has been undergoing,” said Drs. Shaoqing Zhang and Yang Gao.