Scientists from the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) and Nanjing University of Information Science and Technology have successfully constructed an inference and prediction system of the Indonesian Throughflow (ITF) using the deep-learning method and realized the valid prediction of the ITF transport. The study was published in Frontiers in Marine Science on Jan. 16. 

The Indonesian seas are the only ocean channel connecting the tropical ocean basins. The ITF is a crucial ocean dynamic factor for the inter-basin exchange between the Indian Ocean basin and the Pacific Ocean basin. The ITF has a strong material and energy transport and hence plays a significant role in the material and energy balance of the Indo-Pacific Ocean and regional and global climate change. However, the prediction of ITF mainly relies on numerical simulation systems, which often have significant model biases and great uncertainty.

Given this, the researchers led by Prof. HU Shijian put forward the idea of combining satellite observations with artificial intelligence methods to construct the inference and prediction system of ITF and conducted experiments with various deep-learning models. 

The Indo-Pacific pressure gradient is the main driving factor of ITF, so researchers used sea surface heights between the Indian and Pacific Ocean basins to infer and predict the transport of ITF. They trained the convolutional neural network (CNN) using the massive data provided by the Coupled Model Intercomparison Project Phase 6 model and Simple Ocean Data Assimilation data sets and reconstructed a time series of ITF transport.

The training results showed that the system based on the CNN model reproduces about 90% of the total variance of ITF transport, indicating that the system can achieve valid inference of ITF transport. 

The researchers further combined the system with the satellite data from 1993 to 2021 to infer and construct the time series of ITF and found that the time series was in good agreement with the internationally renowned field observation data of ITF. They explored the possibility of predicting ITF with this AI system, and the results show that the system can make a valid prediction with a leading time of seven months.  

"The ITF AI inference and prediction system provides an important tool for researching ocean circulation and climate change in the Indo-Pacific Ocean, which may alleviate the pressure of field ocean observation to some extent," said Prof. HU.  

This work was supported by the Natural Science Foundation of Shandong Province and the Strategic Pilot Science and Technology Special Project of CAS. 

A team of German researchers at the MPQ has for the first time monitored in an experiment how holes (positive charge carriers) in a solid-state model combined to form pairs. This process could play an important role in understanding high-temperature superconductivity. 

Using a quantum simulator, researchers at the Max Planck Institute of Quantum Optics (MPQ) have observed pairs of charge carriers that may be responsible for the resistance-free transport of electric current in high-temperature superconductors. So far, the exact physical mechanisms in these complex materials are still largely unknown. Theories assume that the cause for the pair formation and thus for the phenomenon of superconductivity lies in magnetic forces. The team in Garching has now for the first time been able to demonstrate pairs that are formed this way. Their experiment was based on a lattice-like arrangement of cold atoms, as well as on a tricky suppression of the movement of free charge carriers. 

Since the discovery of high-temperature superconductors almost 40 years ago, scientists have been trying to track down their fundamental quantum-physical mechanisms. But the complex materials still pose mysteries. The new findings of a team in the Quantum Many-Body Systems Department at MPQ in Garching now provide new microscopic insight into processes that may underlie these so-called unconventional superconductors.

Crucial to any kind of superconductivity is the formation of tightly linked pairs of charge carriers - electrons or holes, as electrons vacancies are called. "The reason for this lies in quantum mechanics," explains MPQ physicist Sarah Hirthe: each electron or hole carries a half-integer spin – a quantum physical quantity that can be imagined as a measure of a particle's internal rotation. Atoms also have a spin. For quantum statistical reasons, however, only particles with an integer spin can move through a crystal lattice without resistance under certain conditions. "Therefore, electrons or holes have to pair up to do this," says Hirthe. In conventional superconductors, lattice vibrations called phonons help with pairing. In non-conventional superconductors, on the other hand, a different mechanism is at work – but the question of which one it is has remained unanswered until now. "In a widely accepted theory, indirect magnetic forces play a crucial role," Sarah Hirthe reports. "But this could not be confirmed in experiments so far."

Solid state model spiked with holes 

To better understand the processes in such materials, the researchers used a quantum simulator: a kind of quantum computer that recreates physical systems. To do this, they arranged ultracold atoms in a vacuum with laser light in such a way that they simulate the electrons in a simplified solid-state model. In the process, the spins of the atoms arranged themselves with alternating directions: an antiferromagnetic structure was created, which is characteristic of many high-temperature superconductors – and stabilized by magnetic interactions. The team then "doped" this model by reducing the number of atoms in the system. In that way, holes emerged into the lattice-like structure.

The team at MPQ now could show that the magnetic forces indeed lead to pairs. To achieve this, they used an experimental trick. "Moving charge carriers in a material like high-temperature superconductors are subject to a competition of different forces," explains Hirthe. On the one hand, they have the urge to spread out, i.e. to be everywhere at the same time. This gives them an energetic advantage.

On the other hand, magnetic interactions ensure a regular arrangement of the spin states of atoms, electrons, and holes – and presumably the formation of charge carrier pairs. However: "The competition of forces has so far prevented us from observing such pairs microscopically," says Timon Hilker, leader of the research group. "That's why we had the idea of preventing the disruptive movement of the charge carriers in one spatial direction."

A close look through the quantum gas microscope

This way, the magnetic forces were, to a large extent, undisturbed. The result: holes that came close to each other formed the expected pairs. To observe such pairing, the team used a quantum gas microscope – a device with which quantum mechanical processes can be followed in detail. Not only were the hole pairs revealed, but the relative arrangement of the pairs was also observed, suggesting repulsive forces between them. The team reports on their work in the scientific journal "Nature". "The results underline the idea that the loss of electrical resistance in non-conventional superconductors is caused by magnetic forces," emphasizes Prof Immanuel Bloch, Director at MPQ and Head of the Quantum Many-Body Systems Division. "This leads to a better understanding of these extraordinary materials and shows a new way of how stable hole pairs can form even at very high temperatures, potentially significantly increasing the critical temperature of superconductors".

The researchers at the Max Planck Institute of Quantum Optics now plan new experiments on more complex models in which large two-dimensional arrays of atoms are connected. Such larger systems will hopefully create more hole pairs and allow for observing their motion through the lattice: the transport of electric current without resistance due to superconductivity.

This way, the magnetic forces were, to a large extent, undisturbed. The result: holes that came close to each other formed the expected pairs. To observe such pairing, the team used a quantum gas microscope – a device with which quantum mechanical processes can be followed in detail. Not only were the hole pairs revealed, but the relative arrangement of the pairs was also observed, suggesting repulsive forces between them. The team reports on their work in the scientific journal "Nature". "The results underline the idea that the loss of electrical resistance in non-conventional superconductors is caused by magnetic forces," emphasizes Prof Immanuel Bloch, Director at MPQ and Head of the Quantum Many-Body Systems Division. "This leads to a better understanding of these extraordinary materials and shows a new way of how stable hole pairs can form even at very high temperatures, potentially significantly increasing the critical temperature of superconductors".

The researchers at the Max Planck Institute of Quantum Optics now plan new experiments on more complex models in which large two-dimensional arrays of atoms are connected. Such larger systems will hopefully create more hole pairs and allow for observing their motion through the lattice: the transport of electric current without resistance due to superconductivity.

A new astronomical survey is a portrait of gargantuan proportions. It shows the staggering number of stars bristling among the wispy bands of dust in our home galaxy, the Milky Way. The heart of our galaxy — the central bulge of bright blue stars that also contains the supermassive black hole Sagittarius A* — is at the left side of this panorama.

This galactic panorama was captured by the Dark Energy Camera (DECam) instrument on the Víctor M. Blanco 4-meter Telescope at Cerro Tololo Inter-American Observatory (CTIO), a Program of NSF's NOIRLab. CTIO is a constellation of international astronomical telescopes perched atop Cerro Tololo in Chile at an altitude of 2,200 meters (7,200 feet). CTIO's lofty vantage point gives astronomers an unrivaled view of the southern celestial hemisphere, which allowed DECam to capture the southern Galactic plane in such detail.

The data used to create this survey originate from the second release of the Dark Energy Camera Plane Survey (DECaPS2), a survey of the plane of the Milky Way as seen from the southern sky taken at optical and near-infrared wavelengths. The new data is described today in The Astrophysical Journal Supplement.

"One of the main reasons for the success of DECaPS2 is that we simply pointed at a region with an extraordinarily high density of stars and were careful about identifying sources that appear nearly on top of each other," says Andrew Saydjari, a graduate student at Harvard University, a researcher at the Center for Astrophysics | Harvard & Smithsonian, and lead author of the paper. "Doing so allowed us to produce the largest catalog ever from a single camera, in terms of the number of objects observed."

The first trove of data from DECaPS was released in 2017. With the addition of the new data, the survey now covers 6.5 percent of the night sky and spans a staggering 130 degrees in length. While it might sound modest, this equates to 13,000 times the angular area of the full Moon.

"When combined with images from Pan-STARRS 1, DECaPS2 completes a 360-degree panoramic view of the Milky Way's disk and additionally reaches much fainter stars," says Edward Schlafly, a researcher at the AURA-managed Space Telescope Science Institute and a co-author-of-the-paper-describing-DECaPS2-published in The Astrophysical Journal Supplement. "With this new survey, we can map the three-dimensional structure of the Milky Way's stars and dust in unprecedented detail."

Gathering the data required to cover this much of the night sky was a Herculean task; the DECaPS2 survey identified 3.32 billion objects from over 21,400 individual exposures. Its two-year run, which involved about 260 hours of observations, produced more than 10 terabytes of data.

Most of the stars and dust in the Milky Way are located in its spiral disk — the bright band stretching across this image. While this profusion of stars and dust makes for beautiful images, it also makes the galactic plane challenging to observe. The dark tendrils of dust seen threading through this image absorb starlight and blot out fainter stars entirely, and the light from diffuse nebulae interferes with any attempts to measure the brightness of individual objects. Another challenge arises from the sheer number of stars, which can overlap in the image and make it difficult to disentangle individual stars from their neighbors.

Despite the challenges, astronomers delved into the galactic plane to gain a better understanding of our Milky Way. By observing near-infrared wavelengths, they were able to peer past much of the light-absorbing dust. The researchers also used an innovative data-processing approach, which allowed them to better predict the background behind each star. This helped to mitigate the effects of nebulae and crowded star fields on such large astronomical images, ensuring that the final catalog of processed data is more accurate.

"Since my work on the Sloan Digital Sky Survey two decades ago, I have been looking for a way to make better measurements on top of complex backgrounds," said Douglas Finkbeiner, a professor at the Center for Astrophysics, co-author of the paper, and principal investigator behind the project. "This work has achieved that and more!"

"This is quite a technical feat. Imagine a group photo of over three billion people and every single individual is recognizable!" says Debra Fischer, division director of Astronomical Sciences at NSF. "Astronomers will be poring over this detailed portrait of more than three billion stars in the Milky Way for decades to come. This is a fantastic example of what partnerships across federal agencies can achieve."

Interactive access to the imaging with panning/zooming inside of a web browser is available from the LegacySurveyViewer, the World Wide Telescope, and Aladin.

The DECaPS2 dataset is available to the entire scientific community and is hosted by NOIRLab's Astro Data Lab, which is part of the Community Science and Data Center.

DECam was originally built to carry out the Dark Energy Survey, which was conducted by the Department of Energy and the U.S. National Science Foundation between 2013 and 2019.

The DECaPS2 team is composed of A. K. Saydjari (Harvard University and the Center for Astrophysics | Harvard & Smithsonian), E. F. Schlafly (Space Telescope Science Institute), D. Lang (Perimeter Institute for Theoretical Physics and the University of Waterloo), A. M. Meisner (NSF's NOIRLab), G. M. Green (Max Planck Institute for Astronomy), C. Zucker (Space Telescope Science Institute and the Center for Astrophysics | Harvard & Smithsonian), I. Zelko (Canadian Institute of Theoretical Astrophysics - University of Toronto), J. S. Speagle (University of Toronto), T. Daylan (Princeton University), A. Lee (Bill & Melinda Gates Foundation), F. Valdes (NSF’s NOIRLab), D. Schlegel (Lawrence Berkeley National Laboratory), and D. P. Finkbeiner (Harvard University and the Center for Astrophysics | Harvard & Smithsonian).

Analyzing a person’s gene expression requires mapping their RNA landscape to a standard reference to gain insight into the degree to which genes are “turned on” and perform functions in the body. But researchers can run into issues when the reference does not provide enough information to allow for accurate mapping, an issue known as reference bias. 

Researchers at UC Santa Cruz introduce the first-ever method for analyzing RNA sequencing data genome-wide using a “pantranscriptome,” which combines a transcriptome and a pangenome – a reference that contains genetic material from a cohort of diverse individuals, rather than just a single linear strand. A group of scientists led by UCSC Associate Professor of Biomolecular Engineering Benedict Paten has released a toolkit that allows researchers to map an individual's RNA data to a much richer reference, addressing reference bias and leading to much more accurate mapping. 

“This is pangenome plus transcriptome – that combination has never really been done before until now,” said Jordan Eizenga, the paper’s co-first author and a postdoctoral scholar in the UCSC Computational Genomics Lab. “This is the first time anyone has attempted to incorporate the pangenome as a standard feature of the RNA sequencing mapping.”

This tool will aid researchers around the world who are working to understand gene expression through RNA sequencing analysis. The tools are publicly available and can be accessed via GitHub.

“With this toolkit, we are employing this more diverse data that we can now get from the pangenome to improve the measurement of gene expression data, something that can widely vary between individuals,” Paten said. “The aim is to make the impact of this more diverse data felt on studies that are looking at gene expression, resulting in better analysis for cell models, organoid models, and other research applications.”

RNA’s most commonly recognized function is to translate DNA into proteins, but scientists now understand that the vast majority of RNA is noncoding and does not make proteins, but instead can play roles such as influencing cell structure or regulating genes. The entire RNA landscape is known collectively as the transcriptome, and mapping this allows researchers to better understand an individual’s gene expression.

The pantranscriptome builds on the emerging concept of “pangenomics” in the genomics field. Typically when evaluating an individual’s genomic data for variation, scientists compare the individual’s genome to that of a reference made up of a single linear strand of DNA bases. Using a pangenome allows researchers to compare an individual’s genome to that of a genetically diverse cohort of reference sequences all at once, sourced from individuals representing a diversity of biogeographic ancestry. This gives the scientists more points of comparison for which to better understand an individual’s genomic variation. 

Mapping RNA sequencing data to understand gene expression can be difficult because the RNA sequences are spliced by cellular mechanisms, meaning one set of RNA data can come from non-connected areas of the genome, making it challenging to correctly align them to a reference. These splicing sites are not uniform across the human population but vary between individuals. It is also difficult to know which haplotype the RNA comes from – whether the group of genes comes specifically from the set of chromosomes inherited from the individual’s mother or the set inherited from the father. 

But with the new pipeline of open source tools, the researchers can take the spliced segments of an individual’s RNA, map where they align on a pangenome, identify which haplotype the data belongs to, and analyze gene expression. 

First, the pipeline identifies which areas of the genome the RNA sequencing data comes from, including the splice sites, and marks those points on the pangenome reference. Those marked points are then compared to a pantranscriptome consisting of haplotype-specific transcripts generated from the reference data contained within the pangenome. This step requires specialized, challenging algorithmic methods.

Finally, it generates estimates of levels of gene expression based on this comparison between the mapped data and the transcripts in the pantranscriptome and identifies which haplotypes the genes come from.

“It's definitely a very forward-looking study in that other genome-wide expression methods are not yet really utilizing pangenomes and haplotype information,” said Jonas Sibbesen, co-first author on the study and a former postdoctoral scholar in the UCSC Computational Genomics Lab who is now an assistant professor at the University of Copenhagen. “We're now thinking ahead as to what pangenomics might additionally bring to the table in transcriptomic analyses.” 

Going forward, the researchers are interested in further developing these tools to be useful for downstream informatics analysis, and tailoring the tools for the particularities of research on single-cell data. For now, the group hopes their new toolkit will serve to show how useful using pangenomics-derived analysis can be.

“We need to be able to explain to some researchers how a pangenome reference will benefit them,” Paten said. “This pipeline is really a first go at doing this for RNA, for functional data, for expression data.”