Peculiar icy objects in outer reaches of the Milky Way perplex astronomers

A trio of astronomers from the University of Tokyo and Niigata University have found what they describe as "peculiar embedded icy objects" thousands of light years from Earth. Takashi Shimonishi, Itsuki Sakon and Takashi Onaka have posted a paper describing their discoveries and offering possible ideas regarding their nature on the arXiv preprint server.


In 2021, two objects were discovered in data from the AKARI space telescope over the years 2006 to 2011. At the time, neither could be identified, leaving the team to wait for additional data from the ALMA array in Chile. Now, the new data have only made the nature of the two objects more mysterious.


The research team has found that both objects, which are near to each other in the night sky but far apart in distance, seem to be ice balls of some sort. Both also reside in an outer part of the Milky Way galaxy. They note that either or both could be dense clouds of gas or a ty

A trio of astronomers from the University of Tokyo and Niigata University have found what they describe as "peculiar embedded icy objects" thousands of light years from Earth. Takashi Shimonishi, Itsuki Sakon and Takashi Onaka have posted a paper describing their discoveries and offering possible ideas regarding their nature on the arXiv preprint server.


In 2021, two objects were discovered in data from the AKARI space telescope over the years 2006 to 2011. At the time, neither could be identified, leaving the team to wait for additional data from the ALMA array in Chile. Now, the new data have only made the nature of the two objects more mysterious.

The research team has found that both objects, which are near to each other in the night sky but far apart in distance, seem to be ice balls of some sort. Both also reside in an outer part of the pe of star that has not been seen before. The latter possibility seems unlikely though, they note, because both are far from regions in the galaxy where stars typically form.


The research trio explains that infrared data has shown that both objects have absorbed dust and ice, which is typically something seen in young stellar objects—or in background stars positioned behind dense clouds. They note that there is something else odd about the pair—the data from the telescopes do not match regarding their distance apart and from Earth.


Data from one source show one of the objects as 6,500 lightyears from Earth, while data from another source show it as 30,000 lightyears away. Both sources agree that the other object is approximately 43,700 lightyears away. Also, both objects appear to be approximately 10 times the size of the solar system, which the researchers describe as very small for a gas cloud.


The researchers say that other data show that the gas surrounding both objects is made up mostly of silicon dioxide, with a little carbon dioxide mixed in. The ratio, they note, is similar to that seen for young stars. They conclude by suggesting that the available data cannot be used to identify either object. They hope that once the James Webb Space Telescope can be aimed at them, their nature will become more clear.


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Nvidia Scientist Praises Ex-Intern Who Refused Full-Time Offer To Join DeepSeek

Nvidia’s principal research scientist, Zhiding Yu, has praised former intern Zizheng Pan for his decision to join DeepSeek, a rising Chinese AI startup that has created havoc in the US tech market after becoming the most downloaded free app in the country. In a recent post on X, Yu talked about Pan’s bold choice to leave a potential full-time offer at Nvidia and take a chance on DeepSeek when it was still a small startup with just three members. This gamble has clearly paid off as DeepSeek is now being touted as a major competitor in the AI space.

Pan, who interned at Nvidia in the summer of 2023, has played a key role in DeepSeek’s breakthrough achievements including the development of DeepSeek-VL2, DeepSeek-V3 and DeepSeek-R1. The latter, which researchers claim was developed for just $6 million, has achieved performance comparable to OpenAI’s o1 model which involved billions of dollars in investment.

“Zizheng was one of our interns at NVIDIA back in summer 2023. Later, when we were considering making him a FT offer, he chose to join DeepSeek without much hesitance. Back then, the DeepSeek multimodal team only had 3 people. I am still very much impressed by Zizheng’s decision at that time. He has been an important contributor of several important works at DeepSeek, including DeepSeek-VL2, DeepSeek-V3, and DeepSeek-R1. I am personally very happy for his decision and the great achievements," Yu wrote on X after resharing Pan’s post which featured a screenshot showing DeepSeek app surpassing OpenAI’s ChatGPT on the Apple Store.

Yu further highlighted how talent from China is making significant contributions to the global tech industry.

“Many of our best talents come from China, and these talents don’t have to succeed only in a US company. Instead, we learn a lot from them. The same Sputnik Moment has already happened in AV back in 2022, and it will continue to happen in the Robotics and LLM industry as well," he wrote.

Yu also warned against geo-political agendas in the tech world suggesting that focus should be on talent and innovation rather than political narratives.

“I love NVIDIA and want to see her as a continued major contributor to the path of AGI and general autonomy. But if we keep cooking up geo-political agendas and creating hostile opinions to Chinese researchers, we will shoot ourselves in the foot and lose even more competitiveness. We need more talent density, professionalism, learning, creativity and stronger execution. We don’t need political narratives and clowns like Alexandr Wang," he concluded.




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EURAMET – driving excellence in spintronics research

EURAMET explains how European collaborative spintronics research is supporting future developments in electronics.


EURAMET European research projects are addressing the needs of industry through important developments in measurement science. These technical projects are accelerating the uptake of research results in many diverse areas, including the field of spintronics research. Spintronics is the science behind the magnetic technology that, for example, reads and writes data on the hard disk in your laptop. Smaller, faster, and more efficient electronic devices are a vital part of Europe’s economic growth and industrial innovation and could significantly contribute to aims for a reduction in CO2 emissions.


Spintronics, which uses a fundamental property, the intrinsic spin, of electrons to process information in a way that is analogous to charge in traditional electronics, holds potential to help meet such aims. From spin-based nanodevices that can combine computation with logic and non-volatile magnetic memory, to topologically protected spin structures that can effectively carry information; spintronics research offers scope for the development of energy efficient, next-generation electronic devices.

The European Association of National Metrology Institutes (EURAMET)


Researchers need robust measurement methods for reliably characterising fundamental quantum material properties as a precursor to introducing electron spin technologies into new applications. EURAMET’s collaborative research projects, each involving teams of scientists from multiple countries, are working in diverse fields of measurement science, or metrology, including spintronics and other quantum technologies.


EURAMET, the European Association of National Metrology Institutes, develops and disseminates an integrated, cost effective and internationally competitive measurement infrastructure for Europe – responding to the needs of industry, academic research groups, business and governments. To enhance the benefits of measurement to society, to enable a tangible impact that really makes a difference, is one of the highest priorities for EURAMET and its members. Measurement researchers collaborate to provide the high accuracy, low uncertainty measurements needed both now and in the future.


EURAMET’s European Metrology Research Programmes (EMRP and EMPIR) foster international collaboration, drive research excellence and address society’s grand challenges in areas including health, energy and the environment. So far, more than 300 joint research projects have brought together world-class measurement expertise in targeted projects to strengthen Europe’s position at the forefront of innovation and contribute to our economic prosperity.

Spintronics and quantum technologies at EURAMET


As an important field of applied physics that sits between conventional electronics and quantum mechanics, spintronics research makes use of the fundamental spin property of an electron to process information (see above). Quantum physics dictates that an electron possesses an intrinsic magnetic moment, the spin, that can either point ‘up’ or ‘down’. Spintronics utilises these two spin states (‘up’ or ‘down’) to represent zeros and ones in the same way that traditional electronics uses charge to relay binary data.


Notably, spintronics is considered to have key advantages compared to its conventional electronics counterpart, from the ability to store spin information robustly and permanently without the need of a continuous power supply, to the need for less energy to change a spin state versus generating an electric charge. These aspects make spintronic devices a particularly ‘green’ technology for data storage and data transfer applications.


Advancements in the field of spintronics have continually inspired the invention of ground-breaking new electronic devices for industry. In 2007, the discovery of giant magnetoresistance (GMR) was awarded the Nobel Prize in Physics – a significant milestone that is commonly regarded as the birth of modern spintronics. Soon after the discovery, scientists successfully realised the potential of electron spin for increasing the rate and density of information-processing from hard disk drives. As a direct result of this fundamental research, every hard disk and tape storage system available today uses a spintronic head to read data.


At present, innovative and radical new device applications continue to be developed by scientists working in spintronics, where interest is growing in magnetic technologies that can enable high-density, low-power data storage in highly localised fields. The collaborative power of European metrology institutions working under EURAMET’s research programmes have led to important contributions to magnetic technologies, as well as other areas like nanoscale electronics and material characterisation for spintronic devices. While magnetic technologies at the nanoscale offer promising applications for data storage, there is still a need to bridge existing knowledge gaps in the field. In particular, there is a need for reliable techniques to measure fundamental new phenomena like the spin-Seebeck effect – where the complex interactions of electron spin and thermal gradients have been only recently observed to generate spin currents, resulting from just a change in the temperature across a ferromagnet.


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Quantum Thermodynamics: Black Holes Might Not Be What We Thought

New research highlights that black holes should be viewed as dynamic systems, shedding light on their quantum thermodynamic properties. This study also extends these properties to Extremely Compact Objects (ECOs), potentially contributing to resolving the black hole information paradox and enriching our understanding of quantum gravity. Credit: SciTechDaily.com

A recent study underscores the dynamic nature of black holes and extends similar thermodynamic characteristics to Extremely Compact Objects, advancing our comprehension of their behavior in quantum gravity scenarios.

A paper titled “Universality of the thermodynamics of a quantum-mechanically radiating black hole departing from thermality,” published in Physics Letters B highlights the importance of considering black holes as dynamical systems, where variations in their geometry during radiation emissions are critical to accurately describing their thermodynamic behavior.

Bridging Black Holes and Extremely Compact Objects


The study also suggests that extremely compact objects (ECOs) share these thermodynamic properties with black holes, regardless of their event horizon status. The significance of this research lies in its contribution to the ongoing efforts to resolve the black hole information paradox, providing a more nuanced understanding of black hole thermodynamics in quantum gravity contexts.

Insights From Quantum Physics and Relativity


The research, conducted by Dr. Christian Corda, SUNY Poly Visiting Professor in the Department of Mathematics & Physics, and Dr. Carlo Cafaro (SUNY Poly Adjunct Professor in the Department of Mathematics & Physics and Associate Professor in the Department of Nanoscale Science & Engineering at the University at Albany), exploits elements of quantum physics, statistical mechanics, and general relativity.


One of the most important problems in contemporary theoretical physics is understanding what a black hole (BH) is. It is believed that classical general relativity implies that a BH is an object with a horizon, i.e. a limit surface beyond which no event can influence an external observer, and a singularity in its core, i.e. a point at which the presence of infinite implies that the laws of physics fail.


On the other hand, recent approaches, both classical and quantum, have shown that what we call BH could be an object without both horizons and singularities. Objects of this type are also called Extremely Compact Object (ECO), to distinguish them from the “traditional” concept of BH.


If, on the one hand, this approach solves some important problems, such as the removal of the singularity and the consequent restoration of physical laws, on the other it creates another: What do we do with all the BH thermodynamics, developed over the last 50 years and more years, starting from the pioneering and famous works of the late Bekenstein and Hawking, and based on an enormous number of research papers?

Universality of Black Hole Thermodynamics


In 2023, Samir Mathur and Madhur Mehta gave an important answer to this question by winning the third prize in the Gravity Research Foundation Essay Competition for proving the universality of BH thermodynamics.


Specifically, they demonstrated that any ECO must have the same BH thermodynamic properties regardless of whether the ECO possesses an event horizon.


The result is remarkable, but it was obtained under the approximation according to which the BH emission spectrum has an exact thermal character. In fact, strong arguments based on energy conservation and BH back reaction imply that the spectrum of the Hawking radiation cannot be exactly thermal.


In their work, Drs. Corda and Cafaro extended the result of Mathur and Mehta to the case where the radiation spectrum is not exactly thermal using the concept of BH dynamical state.

Black Hole Dynamic States and Effective Temperatures


The BH dynamic state is obtained by introducing an effective temperature. This is in analogy to several other fields of Science where the deviation from the thermal spectrum of the emitting body is usually considered via the introduction of an effective temperature which represents the temperature of a black body emitting the same exact amount of radiation as the non-thermal source.


In the BH case, the introduction of the effective temperature allows the introduction of other effective quantities, which characterize its “dynamic state,” i.e. the BH state “during” the quantum transition in which energy is emitted or absorbed. This paper therefore generalizes and completes the work of Mathur and Mehta.

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Determination of the hydrodynamic condition in artificial ground freezing based on multi-field coupling theory

Artificial Ground Freezing (AGF) represents a widely adopted auxiliary technology utilized to mitigate groundwater infiltration and ensure the stability of excavation faces in underground construction endeavors. Notably, the hydrodynamic condition stands as the primary contributor to the non-uniformity observed in the freezing curtain. However, directly assessing the hydrodynamic condition during the construction of AGF poses a formidable challenge. In this study an moisture-heat model was initially formulated, incorporating two boundary treatment methodologies, to quantify temperature variations throughout the AGF under different hydrodynamic conditions. Given the inherent uncertainties associated with hydrodynamic conditions, a novel approach grounded in optimization theory (MHO) was proposed and integrated with the moisture-heat model. This methodology aims to ascertain the hydrodynamic condition within AGF by minimizing the summation of squared differences between calculated and monitored temperatures at selected, typical measurement points throughout the entire freezing. The proposed method was numerically resolved and subsequently validated through rigorous laboratory tests conducted by fellow researchers. The results indicate that the methodology presented in this paper offers more accurate predictions of hydrodynamic conditions; the comparison between calculated and monitored temperatures under optimized hydrodynamic conditions exhibits a significantly closer alignment than that obtained when solely considering horizontal hydrodynamic conditions.


Introduction
AGF technique has been acknowledged for its dependable sealing ability, constructional flexibility, cost- effectiveness, and robust adaptability. Within the realm of geotechnical engineering, particularly for tunnels traversing water-rich strata, the AGF technique, primarily comprising brine freezing and liquid nitrogen freezing, has been effectively utilized in soil reinforcement for the main tunnels, cross passages, rescue projects, and end wells . Notably, the construction of the Gongbei Tunnel in China incorporated this technique .
Currently, the design of the freezing tube layout is predominantly carried out under hydrostatic conditions. However, soil, as a porous medium consisting of particles of varying sizes, poses particular challenges. The development of a significant hydraulic gradient within the soil, whether induced by artificial precipitation or natural phenomena, can result in heightened groundwater flow through the pores . This, in turn, may elevate the average temperature of the frozen soil mass and potentially lead to the persistence of an open freezing curtain. For example, in the former Soviet Union in 1955, the interval between Forest and Valor Square stations on Line 1 of the St. Petersburg metro was situated in proximity to an underground river, which had a replenishment effect on the groundwater layer. The high groundwater flow during the AGF construction could dissipate the majority of the cold energy, leading to the non-closure of the freezing curtain and subsequent sand inrush . Comparable engineering challenges attributed to hydrodynamic conditions were also encountered in Shenzhen in 2003, Tianjin in 2006, Shanghai in 2007, Xiamen in 2012, and the Nantong subway project in 2020. Detailed descriptions of these cases can be found in the relevant literature .
From a macroscopic perspective, the hydrodynamic factors influencing the distribution of the freezing curtain primarily encompass the velocity and direction of groundwater flow. Under these hydrodynamic conditions, an increase in groundwater velocities exacerbates the non-uniform distribution of the freezing curtain, leading to a thinner curtain upstream compared to downstream. This phenomenon can be attributed to the transfer of cold energy from upstream to downstream, a process driven by heat convection, which represents a highly complex multi-field coupling mechanism, especially when considering the thermal properties incorporated in the governing equations. Traditionally, groundwater flow has been presumed to be horizontal. In specific scenarios, such as when impermeable layers are present above and below the excavation site, this assumption holds true. However, it is important to note that not all flows are horizontal, and the direction of flow can be highly unpredictable. Recent research has highlighted the significant impact of flow direction on geotechnical engineering , yet the investigation into its influence on the formation of the freezing curtain remains inadequate. Additionally, the diameter and spacing of the freezing tubes have a substantial impact on the development of the freezing curtain , albeit this aspect lies outside the scope of the present study.
The formation of a freezing curtain under hydrodynamic conditions constitutes a sophisticated thermophysical process, encompassing internal heat sources, phase transitions, moving boundaries, and intricate boundary conditions. Currently, the primary methodologies employed to assess the evolution of the freezing curtain within such hydrodynamic contexts involve theoretical analysis, numerical modeling, and scaled experimental approaches. In the realm of theoretical research, considerable endeavors have been directed towards the development of analytical models that delineate the steady-state temperature field of typical freezing tube configurations under hydrostatic conditions . To the author's limited knowledge, only a handful of studies have taken into account the hydrodynamic conditions within a limited number of freezing tubes , primarily due to the highly nonlinear nature of the governing equations. Consequently, two numerical methodologies, which account for the hydrothermal properties of soil, have been harnessed to predict temperature variations: the apparent method and the enthalpy-porosity method . Both methods are proficient in simulating the formation of a freezing curtain under horizontal groundwater flow conditions. Nonetheless, the direction of groundwater flow has not been incorporated into the numerical models, as the boundary conditions pose a significant challenge in simulations. A comparable situation is evident in model test studies, which have only simulated conditions with horizontal groundwater flow .
In light of these limitations, it is crucial to propose a methodology for determining the hydrodynamic conditions. During AGF construction, it seems impractical to directly ascertain the magnitude and direction of groundwater flow within the frozen zones. Therefore, this study introduces an optimization approach to calculate these two variables. Presently, optimization techniques are primarily categorized into two groups: analytic gradient methods (such as SNOPT, IPOPT, and MMA) and approximate gradient algorithms (including Nelder Mead, COBYLA, and BOBYQA). In comparison to analytic gradient methods, the approximate gradient approach, renowned for its adaptability, has been extensively employed in geotechnical engineering . Additionally, the approximate gradient algorithm is particularly suited for problems involving variable geometries.
In this study, a moisture-heat coupling model, which takes into account heat conduction and convection under various hydrodynamic conditions, was first established in Section 2, based on the coupling model proposed by Li et al.. Subsequently, in Section 3, the formation process of the freezing curtain, achieved through the linear arrangement of three tubes, was investigated under different velocities and directions of groundwater flow. Furthermore, in Section 4, a MHO method was proposed to determine the hydrodynamic conditions during AGF and was validated through experiments conducted by Pimentel et al. Finally, some significant conclusions were drawn in Section 5.


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Uncovering the Mystery of the Human Brain with Computational Neuroscience

The human brain is a complex and unfathomable supercomputer. How it works is one of the ultimate mysteries of our time. Scientists working in the exciting field of computational neuroscience seek to unravel this mystery and, in the process, help solve problems in diverse research fields, from Artificial Intelligence (AI) to psychiatry.

Computational neuroscience is a highly interdisciplinary and thriving branch of neuroscience that uses computational simulations and mathematical models to develop our understanding of the brain. Here we look at: what computational neuroscience is, how it has grown over the last thirty years, what its applications are, and where it is going.

The evolution of computational neuroscience

The term Computational Neuroscience was first coined by Eric L. Schwartz at a conference held in 1985. The first graduate program titled the ‘Computational and Neural Systems (CNS) PhD Program’ was instituted at the California Institute of Technology that same year. The field is built on two disciplinary research traditions:Neurophysiology

The Hodgkin and Huxley model of action potentials is the cornerstone of this field. In 1952 Hodgkin and Huxley devised a mathematical model describing the underlying mechanism of how neuronal action potentials are initiated and propagated. After this influential model came mathematical models for neural population dynamics and learning and memory upon which our current understanding is builtExperimental psychology and neuroscience

Information processing and learning; artificial neural networks of the 1960s and learning algorithms that are at the heart of the modern revolution in AI

Computational neuroscience is nowadays and common component of the university curriculum, and since the 1980s, the field has grown over three decades into the thriving international research community that it is today.
Computational neuroscience in the twenty-first century

During the twentieth century, scientists working on the border of biology and physics became increasingly collaborative. These molecular biologists sought to unravel the mystery of life. They went in search of that elusive entity: the gene. The twenty-first century brings curious minds to that utmost of puzzles: the complexities and workings of the human mind.

Enter computational neuroscience. The human brain cannot be understood through experiments alone; this is where computational modeling comes in. Computational neuroscience employs mathematical models, statistical analyses, computerized simulations, theoretical analyses, and abstractions to understand the brain. This does not negate the need for an experiment. Rather it is a necessary but complementary pursuit. Experimentation is deployed in concert with computerized modeling in an iterative process whereby scientists work toward building our understanding of the human mind.
The interdisciplinary field of computational neuroscience draws upon approaches from electrical engineering, computer science, physics, mathematics, and neuroanatomy, in addition to the fields of neurophysiology and experimental psychology given above.

Some examples of computational neuroscience

What are some examples of computational neuroscience?Neuromorphic Technology as the basis of the ‘supercomputer’
International collaborative efforts in neuroscience

More specific examples include:The European Human Brain Project
The US Brain Initiative
The SpiNNaker supercomputer
Brainscales Computer

The SpiNNaker supercomputer

The term SpiNNaker pertains to “Spiking Neural Network Architecture” and the SpiNNaker supercomputer was invented by Steve Furber at the University of Manchester, UK. The SpiNNaker was first operational in 2018 and comprised an innovative computer architecture modeled on the human brain. A silicon brain, if you like.

The SpiNNaker supercomputer is a ground-breaking venture built up of a parallel computing platform that comprises three key areas of research:
Neuroscience –simulation of neuronal networks comprising billions of simple neural structures or millions of complex structural arrangements and dynamics
Robotics –the computer architecture can simulate a network of tens of thousands of spiking neurons (spiking neural networks or SNNs are artificial neural networks that mimic natural neural networks) in real time and at low power. It is this latter feature that holds attraction for researchers in robotics who require mobile, low-power computation
Computer Science – The brain is a highly dynamic, complex, and interconnected structure. Computational neuroscience parts with research tradition in computer science by making a break with determinism.

Frontiers in computational neuroscience

Computational science interacts with and advances cutting-edge research in AI and machine learning. The Second-generation SpiNNaker Neuromorphic Supercomputer at TU Dresden can now simulate brain-size network in real time. The model combines high-throughput machine learning and sensor processing at millisecond latency. It is designed to bridge the gap between realistic brain modeling and AI. According to Furber, there is “still a great deal to learn from biology if we are to realize the full potential of AI in the future.”

Meanwhile, in medical science and human psychology, the growing body of knowledge from this field will aid our understanding of mental diseases. Rather fittingly, a new field of computational psychiatry has sprung up. Also, thanks to computational neuroscience, there is a growing body of knowledge on the subcortical region of the brain regulating affective behaviors and the mechanisms of cognitive function. We know much more about working memory, decision-making, selective attention, and executive control of flexible behavior.

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Record-Breaking Efficiency and Longevity: New Research Eliminates Major Weakness in Organic Solar Cells

Researchers enhanced organic solar cell efficiency and stability by eliminating a loss mechanism using a SiOxNy passivation layer, achieving record-breaking performance and longevity.

Researchers at Åbo Akademi University in Finland have discovered and eliminated a previously unknown loss mechanism in organic solar cells, significantly improving their efficiency and extending their lifespan. This breakthrough offers valuable insights into enhancing the future performance and stability of organic solar technology.

The study was conducted by the Organic Electronics Research Group at Åbo Akademi University in collaboration with Professor Chang-Qi Ma’s team at the Suzhou Institute for Nano-Tech and Nano-Bionics. Key contributors from Åbo Akademi University include Ronald Österbacka, Sebastian Wilken, and Oskar Sandberg.
Record-Breaking Efficiency and Longevity

The study demonstrated an outstanding efficiency of over 18% in structure-inverted solar cells with a 1cm2 area. It also achieved the highest reported lifespan of organic solar cells to date, reaching 24,700 hours under white light illumination, which corresponds to a predicted operational life of more than 16 years.

Organic photovoltaics are interesting in terms of commercialisation because they are light, flexible and have an energy-efficient manufacturing process. The power conversion efficiency has increased dramatically over the last five years, with the best organic solar cells, which are based on a so-called conventional structure, reaching over 20% in the lab.

However, the employed materials are susceptible to degradation when exposed to sunlight and air, and the long-term stability of these cells still requires improvements to make them widely available.
Enhancing Stability Through Inverted Structures

In terms of lifetime, it is beneficial that the topmost contact layer of the solar cell is made from the most durable material. These structure-inverted, or n-i-p solar cells, are a more stable option, although their power conversion efficiency still lags behind that of conventional designs. The researchers’ discovery shows a promising way to improve both the performance and stability of these structurally inverted organic solar cells.

The work identified a previously unknown loss mechanism in organic solar cells and a way to overcome it. The bottom contact of these devices, made from metal oxides such as zinc oxide, creates a narrow recombination area leading to a loss of photocurrent. By applying a thin, solvent-processed silicon oxide nitrate (SiOxNy) passivation layer on the bottom contact, the recombination area is eliminated, resulting in improved efficiency. The work underlines the potential for using the method in the large-scale production of efficient and stable organic solar cells.

Reference: organic solar cells with an in situ-derived SiOxNy passivation layer and power conversion efficiency exceeding 18%” by Bowen Liu, Oskar J. Sandberg, Jian Qin, Yueying Liu, Sebastian Wilken, Na Wu, Xuelai Yu, Jin Fang, Zhiyun Li, Rong Huang, Wusong Zha, Qun Luo, Hongwei Tan, Ronald Österbacka and Chang-Qi Ma, 9 January 2025, Nature Photonics.

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Chinese scientists just dominated a top science journal. Trend or just a coincidence?

In the latest issue of Nature – one of the oldest and most prestigious scientific journals in the West – almost half of the published studies featured work by ethnic Chinese researchers.

Some researchers said the output reflected the rapid progress of China’s scientific work in recent years while others said more data was needed to determine if this was a trend.

Nature is a multidisciplinary publication founded in 1869 and based in London. On January 8, the weekly journal published 35 research papers, 17 of which had ethnic Chinese scientists as first or corresponding authors.

Among the papers, some were led by Chinese researchers working at overseas research institutes – such as Scripps Research and Ohio State University in the United States – and some were co-authored by Chinese scientists from domestic institutes and their international collaborators.

“This is not surprising,” said Deng Weiwei, a professor at Southern University of Science and Technology (SUSTech) in Shenzhen.

He noted that Chinese researchers were now scattered across universities and research institutes worldwide, and articles by scientists from China’s top research institutes had appeared more frequently in top journals.

At SUSTech, for example, about one research paper was published in Nature every two weeks last year, Deng said.

Yin Zongjun, a researcher at Nanjing Institute of Geology and Palaeontology at the Chinese Academy of Sciences (CAS) in the eastern province of Jiangsu, said the trend had been apparent for a few years.
“This shows that the Chinese contribution to the top scientific journals is getting closer to that of the Americans,” Yin said, adding that China’s progress in recent years had been all-encompassing – from the economy to defence technology to science.

Three international journals – Cell, Nature and Science (CNS) – are widely regarded as the pinnacle of academic journals, and many researchers aspire to publish in them.
“The fact that the number of CNS articles by Chinese scientists has significantly increased indicates that the quality and level of their research has also improved dramatically,” said Cao Yu, a researcher with the school of life sciences at Westlake University in Zhejiang province in eastern China.

According to a report released in 2023 by the Institute of Scientific and Technical Information of China (ISTIC) under the Ministry of Science and Technology, China contributed nearly one-third of the academic papers published in the most influential international journals in 2022. It was the first time that China had overtaken the US to secure the top position globally.

ISTIC’s latest report in September 2024 showed that China maintained its lead in 2023. In 161 high-impact journals covering 178 disciplines worldwide, Chinese researchers published more than 14,000 papers, accounting for 27.7 per cent of the global total, ranking it first in the world.
Meanwhile, the latest Nature Index, published by Nature in June 2024, showed that seven out of the top 10 institutions for the calendar year 2023 – ranked according to their contributions to natural and health sciences journals – were from China.

According to the annual ratings list, China was the only leading country to increase its number of institutions in the top 100 ranking. In 2022, China had 31 institutions in the top 100; in 2023, it had 38. By comparison, the US had 38 institutions in the top 100 in 2022 and 35 in 2023.

But Sun Yutao, a professor at Dalian University of Technology’s school of economics and management, said that the high percentage of Chinese contributions could simply be a coincidence, and that more data was needed to judge whether it reflected a trend. In Nature’s previous issue, published on January 1, six of its 23 papers were led by ethnic Chinese scientists.

Sun also cautioned that the scientific merit of these papers needed to be scrutinised, and that it could not simply be assumed that “a high number of papers implies a high level of research”.

“Although China is already a global leader in many scientific indicators, such as the number of highly cited papers, the contribution of Chinese scientists is not yet particularly prominent in some aspects, including original scientific discoveries and the world’s most influential scientific awards,”.

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Is there a renaissance in GPCR drug development?

GPCRs are the targets for around one third of all approved drugs1. Yet such medicines act on only a handful of the 800+ known GPCRs. A wave of biomedical progress in the early 21st century thrust these receptors into the spotlight, but many proved intractable for drugmakers and fell by the wayside.

“What makes them such attractive targets is their position right at the top of every cell’s communication pathway, and their involvement in pretty much every disease you can think of,” says Fiona Marshall, president of biomedical research at the Novartis Institute for Biomedical Research.

The ubiquitous presence and transmembrane location of GPCRs presented challenges, however. First, with obtaining a molecular structure around which an optimal drug could be designed, and second, avoiding off-target effects caused by hitting multiple GPCRs.

Now, thanks to advances in structural biology, several of which earned Nobel prizes, a new generation of more selective GPCR-targeted drugs is within reach. Not surprisingly, this has led to renewed interest from the drug industry: pharmaceutical companies, needing to replenish their pipelines; innovative biotechs using GPCR structure-based approaches to create differentiated and potentially first- or best-in-class drug candidates; and investors, who believe there are significant financial rewards from backing these businesses.



It would be fair to say that the GPCR business is big news again.

A structural revolution

One of the roots of this GPCR story can be traced back to advice given in 1970 to a physicist named Richard Henderson, who was working on bacterial rhodopsin at the MRC Laboratory of Molecular Biology in Cambridge. Henderson recalls that a colleague suggested he “move away from this boring bacterial protein”.

He broadened to studying human membrane proteins instead: “But it took ten years to even generate a crystal for X-ray crystallography, and a further seven to get a structure.”

The breakthrough in GPCR stability and crystallization came in 2005. This led Henderson and his colleague Chris Tate to co-found Heptares Therapeutics in 2007, along with Malcolm Weir who became CEO, based on the proprietary technology they developed.

Marshall, who had already spent much of her career working on GPCR drug discovery, joined the firm as a co-founder and CSO. “Those first few years we were refining the technology and we made some big steps forward.”

Other labs were also exploring ways to stabilize GPCRs, including the academic groups of Brian Kobilka, Ray Stevens and Ehud Landau. “These technologies came together and led to an explosion in crystal structures,” recalls Marshall.

Around 2014, Henderson worked out a way to combine thousands of electron cryomicroscopy (cryo-EM) pictures of single molecules to reveal complicated structures — work that earned him a share of the Nobel Prize in Chemistry in 2017. “It turned out to be especially productive for solving the structure of G-proteins coupled to their receptors,” he says.

Expanding markets for GPCRs

As well as the leap from X-ray crystallography to cryo-EM, “emerging biology was showing the importance of GPCRs across an increasing number of diseases,” says Marshall.

The range of diseases that involve GPCRs is extensive, including asthma, pain, schizophrenia, Parkinson’s disease, cancer, depression, hypertension and incontinence.

One current industry success story are the injectable peptides for diabetes and obesity, which target the GPCR for glucagon-like peptide-1 (GLP-1)2. In the third quarter of 2024 alone, semaglutide achieved sales of US$6.9 billion for Novo Nordisk, while tirzepatide revenue was US$4.4 billion for Eli Lilly. Heptares published the first crystal structure of the GLP-1 receptor in 20173, opening the possibility of structure-based drug design for this target.

Another area finally showing promise are the muscarinic receptors in neuropsychiatric disorders4. Drug developers have been trying to crack these receptors for decades, but the similarity between the five subtypes was a challenge.

“The M4 subtype was a risky one for us to pick, as not everybody believed in it,” recalls Marshall. Off-target effects killed most muscarinic agonist programmes. However, one strategy to offset the lack of selectivity was to combine a non-selective agonist with a peripheral antagonist, resulting in FDA approval for schizophrenia in 20245. Another approach taken by several companies, thanks in part to structural insights, is to develop selective M1 and/or M4 agonists for schizophrenia and other neurological diseases.


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This AI Scientist can conduct end-to-end scientific research autonomously

In collaboration with scientists from the University of Oxford and the University of British Columbia, Sakana AI has developed an artificial intelligence (AI) system that can conduct end-to-end scientific research autonomously. In the report introducing the model, the team proposed and ran a fully AI-driven system for automated scientific discovery applied to machine learning research. The AI Scientist automates the entire research lifecycle, from generating novel research ideas, writing any necessary code, and executing experiments to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript.

The team also introduced an automated peer review process to evaluate generated papers, write feedback, and improve results. It is capable of evaluating generated papers with near-human accuracy. The automated scientific discovery process is repeated to iteratively develop ideas in an open-ended fashion and add them to a growing archive of knowledge, thus imitating the human scientific community.

About AI scientist

The AI Scientist is designed to be compute efficient. Each idea is implemented and developed into a full paper at approximately $15 per paper. While there are still occasional flaws in the documents produced by this first version, this cost and the promise the system shows so far illustrate the potential of AI scientists to democratize research and significantly accelerate scientific progress.

According to Sakana AI, this work signifies the beginning of a new era in scientific discovery: bringing the transformative benefits of AI agents to the entire research process, including that of AI itself. The team believes that the AI Scientist takes us closer to a world where endless affordable creativity and innovation can be unleashed on the world’s most challenging problems.

The AI Scientist is a fully automated pipeline for end-to-end paper generation, enabled by recent advances in foundation models. Given a broad research direction starting from a simple initial codebase, such as an available open-source code base of prior research on GitHub, The AI Scientist can perform idea generation, literature search, experiment planning, experiment iterations, figure generation, manuscript writing, and reviewing to produce insightful papers. Furthermore, The AI Scientist can run in an open-ended loop, using its previous ideas and feedback to improve the next generation of ideas, thus emulating the human scientific community.


The AI scientist first “brainstorms” a diverse set of novel research directions. Given an idea and a template, the second phase of the AI Scientist executes the proposed experiments and then obtains and produces plots to visualize its results. Finally, the AI Scientist produces a concise and informative write-up of its progress in the style of a standard machine learning conference proceeding in LaTeX. A key aspect of this work is the development of an automated LLM-powered reviewer capable of evaluating generated papers with near-human accuracy.

Overcoming limitations

In its current form, The AI Scientist has several shortcomings. Its uses continue to radically improve in capability and affordability. The AI Scientist currently lacks vision capabilities, so it cannot fix visual issues with the paper or read plots.


For example, the generated plots are sometimes unreadable, tables sometimes exceed the width of the page, and the page layout is often suboptimal. Adding multi-modal foundation models can fix this. It can incorrectly implement its ideas or unfairly compare baselines, leading to misleading results. And the model occasionally makes critical errors when writing and evaluating results. The researchers expect that all these will likely improve dramatically in future versions with the inclusion of multi-modal models and as the underlying foundation models.


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Insights Into Protein Engineering: Methods and Applications

What Is Protein Engineering?


Protein engineering is a powerful biotechnological process that focuses on creating new enzymes or proteins and improving the functions of existing ones by manipulating their natural macromolecular architecture.1


Each protein contains a unique genetically encoded sequence of amino acids. Protein synthesis occurs through translation and is based on mRNA codons.2 Scientists use recombinant DNA technology to modify codons and develop diverse proteins with potentially enriched activities.3

Genetic engineering technologies that enable cloning of any gene found in nature and DNA chemical synthesis have immensely contributed to the protein engineering field. In addition, technological advancements such as x-ray crystallography and computer modeling help researchers design amino acid sequences that fold into precise 3D structures, synthesizing proteins with specific properties.4

Protein Engineering Methods


Protein engineering encompasses multiple strategies including rational design, directed evolution, semirational design, peptidomimetics, and de novo protein design. Scientists use these strategies to develop novel proteins or optimize existing protein properties that are relevant to medicine and biotechnology.5 Researchers then screen newly developed protein variants to identify those with desirable functions. For this, they have developed efficient screening methods such as fluorescence activated cell sorting (FACS) and phage display technology to examine large libraries of synthetic proteins and enzymes.6


Protein engineering strategies such as rational design, directed evolution, semirational design, de novo design, and peptidomimetics help scientists improve protein properties for a range of applications.
modified from © iStock, novielysa

Rational method

Rational design is the classical protein engineering method that involves site directed mutagenesis.7 Scientists perform specific point mutations via insertions or deletions in the coding sequence based on structural and functional knowledge of the target protein. Typically, they mutate coding regions that correspond to a protein’s activity.

A key limitation of the rational method is that researchers must know a protein’s structural, functional, and molecular information. Although the rational protein design approach offers an increased possibility of beneficial alterations, it is not easy to accurately predict the sequence-structure-function relationship, particularly at the single amino acid level.7 However, artificial intelligence (AI) has substantially improved protein structure prediction based on amino acid sequence, which is vital for rational design strategies and newer engineering methods, such as semirational and de novo protein design.

In comparison to other methods such as directed evolution, rational design is less time consuming as it does not require large library screening. Scientists use this strategy to engineer protein-based vaccines, antibodies, and enzymes with high thermal stability and catalytic efficiency to meet industrial demands.8
Directed evolution

In 2018, Frances H. Arnold won the Nobel Prize in Chemistry for the directed evolution of enzymes. The prize was shared with George P. Smith and Sir Gregory P. Winter for the phage display of peptides and antibodies. The directed evolution method is a robust protein engineering technique that generates random mutations in a gene of interest, followed by rapid protein variant selection based on favorable properties for specific applications.7

Scientists commonly use error-prone polymerase chain reaction (EP-PCR) to generate random mutations throughout a gene or gene region.7 This method does not require any prior information regarding the protein’s structure and mechanisms, as it mimics the process of natural evolution. The success of the directed evolution method lies in generating mutant libraries of significant size and diversity.
Semirational protein design

Semirational protein design is a combination of rational and directed evolution methods.9 Scientists consider this strategy more effective because they can use computational or bioinformatic modeling to obtain information on the protein’s function and structure and, therefore, select the most promising protein region to change.

This results in a small but high-quality library. The semirational protein design approach provides researchers with an increased opportunity to select biocatalysts with a wider substrate range, specificity, selectivity, and stability without compromising on their catalytic efficiency.
Peptidomimetics

Peptidomimetics is the design and synthesis of metabolically stable peptide analogs that mimic or block natural enzyme or peptide functions.5 This approach employs a variety of biological techniques including solid phase synthesis of nonpeptide libraries that extend the range of amino acid sequences incorporated into engineered proteins.10 Peptidomimetics also uses combinatorial approaches that employ multiple synthetic biology techniques and result in rapid protein variant generation.
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De novo protein design

In 2024, David Baker won the Nobel Prize in Chemistry for computational protein design and the prize was shared with Demis Hassabis, and John Jumper for protein structure prediction.

Scientists use de novo protein design to synthesize proteins with specific structural and functional properties from scratch.11 For example, researchers use this strategy to generate proteins that fold into a particular topology, bind to a specific target, or contain a particular catalytic site. Machine learning models such as denoising diffusion probabilistic models (DDPM) enable photorealistic image generation to visualize protein folding and support de novo protein design.12 Researchers have improved diffusion models by integrating powerful structure prediction methods such as RoseTTAFold (RF) and AlphaFold2. The RF-based diffusion model can design diverse functional proteins from simple molecular specifications.11
Autonomous Protein Engineering Systems

Because the creation of new proteins with improved or novel functions can be slow and labor intensive, scientists have developed autonomous platforms to accelerate the process.13 These platforms computationally estimate mutation effects, particularly when screening by combinatorial techniques is diffi
Rational method

Rational design is the classical protein engineering method that involves site directed mutagenesis.7 Scientists perform specific point mutations via insertions or deletions in the coding sequence based on structural and functional knowledge of the target protein. Typically, they mutate coding regions that correspond to a protein’s activity.

A key limitation of the rational method is that researchers must know a protein’s structural, functional, and molecular information. Although the rational protein design approach offers an increased possibility of beneficial alterations, it is not easy to accurately predict the sequence-structure-function relationship, particularly at the single amino acid level.7 However, artificial intelligence (AI) has substantially improved protein structure prediction based on amino acid sequence, which is vital for rational design strategies and newer engineering methods, such as semirational and de novo protein design.

In comparison to other methods such as directed evolution, rational design is less time consuming as it does not require large library screening. Scientists use this strategy to engineer protein-based vaccines, antibodies, and enzymes with high thermal stability and catalytic efficiency to meet industrial demands.8
Directed evolution

In 2018, Frances H. Arnold won the Nobel Prize in Chemistry for the directed evolution of enzymes. The prize was shared with George P. Smith and Sir Gregory P. Winter for the phage display of peptides and antibodies. The directed evolution method is a robust protein engineering technique that generates random mutations in a gene of interest, followed by rapid protein variant selection based on favorable properties for specific applications.7

Scientists commonly use error-prone polymerase chain reaction (EP-PCR) to generate random mutations throughout a gene or gene region.7 This method does not require any prior information regarding the protein’s structure and mechanisms, as it mimics the process of natural evolution. The success of the directed evolution method lies in generating mutant libraries of significant size and diversity.
Semirational protein design

Semirational protein design is a combination of rational and directed evolution methods.9 Scientists consider this strategy more effective because they can use computational or bioinformatic modeling to obtain information on the protein’s function and structure and, therefore, select the most promising protein region to change.

This results in a small but high-quality library. The semirational protein design approach provides researchers with an increased opportunity to select biocatalysts with a wider substrate range, specificity, selectivity, and stability without compromising on their catalytic efficiency.
Peptidomimetics

Peptidomimetics is the design and synthesis of metabolically stable peptide analogs that mimic or block natural enzyme or peptide functions.5 This approach employs a variety of biological techniques including solid phase synthesis of nonpeptide libraries that extend the range of amino acid sequences incorporated into engineered proteins.10 Peptidomimetics also uses combinatorial approaches that employ multiple synthetic biology techniques and result in rapid protein variant generation.

cult. Furthermore, screening methods such as mass spectrometry (MS), however efficient and specific they are, require time-consuming sample preparation steps. This shortcoming is overcome by recently developed autosamplers that use electrospray ionization (ESI) for fast sample preparation.14

Additionally, robot scientists and self-driving laboratories combine laboratory experiments and automated learning and reasoning to accelerate new biomolecule design. For example, the fully autonomous protein engineering platform Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) is equipped with AI programs that learn protein sequence–function relationships and design new proteins. Subsequently, a fully automated robotic system performs experiments to test the designed proteins and provide feedback.15
Protein Engineering Applications

As industrial enzymes are often sourced from mesophilic organisms, they are typically active in moderate reaction conditions.16 However, an ideal industrial enzyme must withstand harsh conditions such as extreme temperature, pH, and salinity. Scientists use protein engineering techniques to improve the properties of industrially important enzymes such as lipases, esterases, amylases, proteases, xylanases, and cellulases for high specificity, thermostability, and catalytic efficiency.17 There are numerous protein engineering applications, including biocatalysts for food and industry, medicine, and the environment. Additionally, remarkable progress in protein engineering over the past decade has improved therapeutics by enabling researchers to produce antivirals, vaccine antigens, and drug-delivery nanovehicles.18

Table 1: Protein engineering applications19-22



Application Area

Examples of Engineered Enzymes and Proteins

Mutagenesis Approach

Mutant Properties


Basic protein science

GroEL minichapherones

Semirational approach

Stability


Detergent industry

Alkaline proteases

Site directed mutagenesis and/or random mutagenesis

High activity at alkaline pH and low temperatures


Food industry

α-amylase

Site directed mutagenesis

Thermostability


Medicine

Insulin

Site directed mutagenesis

Fast acting monomeric insulin


Agriculture

5-enolpyruvyl-shikimate-3-phosphatesynthase

EP-PCR

Enhanced kinetic properties and confer herbicide tolerance (glyphosate)

Protein Engineering Challenges

Protein design not only offers multiple opportunities in terms of applications, but also presents challenges due to knowledge gaps around folding mechanisms, which are the physiochemical principles underlying protein stability and interactions with the environment. Computational methods allow scientists to generate 3D protein structures, which help elucidate the process of protein folding; however, it is not easy to manipulate the factors that determine protein conformation for targeted purposes.

Furthermore, it is difficult to accurately predict the protein conformational changes that happen during the process of binding with other molecules.18 This information is vital to determine how designed proteins respond to the environment. Researchers focus on overcoming these challenges by using machine learning tools and computational design methods to generate new proteins with favorable properties.


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