A neolithic warrior gets a face thanks to modern tech.In a groundbreaking advance that bridges molecular biology and evolutionary science, researchers at The University of Osaka have unveiled a novel methodology to resurrect ancestral proteins, specifically rhodopsins, enabling direct experimental investigation of these ancient molecules. This work, published in ACS Omega, outlines an innovative sequence reconstruction technique that integrates structural information and attentively accounts for insertions and deletions in protein sequences — a crucial improvement over conventional alignment methods that struggle with highly variable protein domains.
Microbial rhodopsins constitute a diverse family of membrane-embedded proteins with critical biological functions, ranging from ion transport to photoreception. Despite sharing a common structural feature of seven transmembrane helices, these proteins exhibit remarkable functional diversity, largely attributed to extensive variation in their extramembrane domains. Traditional sequence alignment algorithms fall short in tracing their evolutionary trajectories, as they typically do not handle complex insertions and deletions (indels) effectively, especially in variable regions outside of transmembrane segments.
Addressing these challenges, the team led by Haruto Ishikawa and Yasuhisa Mizutani developed ConsistASR, a structure-guided and indel-aware sequence reconstruction pipeline. This approach leverages three-dimensional structural insight to inform sequence alignment, thereby improving the accuracy of reconstructing full-length ancestral rhodopsin proteins. By carefully modeling sequence variability in extramembrane domains, the researchers were able to faithfully infer ancestral sequences that maintain key structural and functional attributes.
Focusing on two subfamilies of microbial rhodopsins—schizorhodopsins and heliorhodopsins—the study successfully resurrected their ancestral versions and expressed these proteins in Escherichia coli. The functional assays revealed that the ancestral schizorhodopsin exhibited light-driven proton pumping activity, consistent with its modern descendants, whereas the ancestral heliorhodopsin lacked ion transport activity, perfectly aligning with the known characteristics of extant heliorhodopsins.
Notably, the ancestral schizorhodopsin protein exhibited the expected spectral properties and stability, signifying correct folding within the bacterial membrane environment. This functional proof of concept not only validates the reconstruction pipeline but also opens a window into the evolutionary dynamics that have shaped rhodopsin diversification over time. The ability to experimentally produce and assay these ancient proteins allows scientists to study evolutionary adaptations at a molecular level with unprecedented precision.
The significance of this work extends beyond just the rhodopsin family. The ConsistASR platform provides a powerful toolkit that can be generalized to reconstruct other ancestral proteins characterized by variable length sequences and complex indels. Such reconstructions are invaluable for evolutionary biology, synthetic biology, and protein engineering, providing empirical insights into how proteins have evolved to acquire new functions and properties.
This innovative integration of sequence, structural, and functional analysis reflects a broader trend in molecular evolution research, where computational biology synergizes with experimental validation to overcome traditional obstacles. As the genomic and structural databases continue to expand, methods like ConsistASR could revolutionize our understanding of protein evolution and functionality by enabling direct interrogation of ancient biomolecules.
The study also tackles a fundamental evolutionary biology question—how functional diversity arises within a protein family while preserving core structural frameworks. By showing that ancestral proteins can be reconstructed in full length and retain distinct biophysical and functional profiles, the research provides direct empirical evidence for evolutionary trajectories that have resulted in contemporary diversity.
Moreover, the expression of these ancestral rhodopsins in bacterial hosts exemplifies how modern synthetic biology tools can resurrect and characterize long-extinct molecular functions. This not only informs evolutionary theory but also offers potential applications in biotechnology, where ancestral proteins might possess unique stability or functional features suitable for industrial or medical purposes.
In practical terms, illuminating the evolutionary path of microbial rhodopsins aids in comprehending microbial adaptation to various ecological niches, given these proteins’ pivotal role in light-driven energy conversion and environmental sensing. Ancestral protein resurrection thus provides a tangible bridge connecting molecular structure, evolution, and ecological function.
The research was supported by the Japan Society for the Promotion of Science and involved meticulous data analysis strategies, combining phylogenetics, structural biology, and bioinformatics. It underscores the multidisciplinary nature of modern biological discovery, illustrating that resolving complex biological problems often requires integrating diverse scientific perspectives and technologies.
Ultimately, this powerful approach reinvigorates the field of ancestral protein research by not only surmounting critical technical obstacles but also by delivering concrete functional data that validate hypotheses about ancient protein behavior. The availability of the ConsistASR workflow to the broader scientific community encourages collaborative exploration of protein evolution and engineering, heralding a new era of experimentally validated molecular paleobiology.
Subject of Research:
Not applicable
Article Title:
Resurrecting Full-length Ancestral Schizorhodopsins and Heliorhodopsins with Structure-guided, Indel-aware Sequence Reconstruction
News Publication Date:
8-Jun-2026
Web References:
https://doi.org/10.1021/acsomega.6c03010
Image Credits:
Haruto Ishikawa
Keywords:
Molecular biology, Biomolecules, Amino acids, Biomolecular structure, Domain organization, Bacterial proteins, Ancient proteins, Membrane proteins, Extracellular domains
In a groundbreaking development that promises to revolutionize the way artificial intelligence perceives the world, a collaborative research team from KAIST, MIT, and Microsoft has unveiled a novel technology named “Upsample Anything.” This pioneering innovation substantially enhances the visual acuity of AI systems while drastically reducing the memory demand on hardware, specifically GPU memory. The new method achieves up to a sixteenfold increase in memory efficiency, addressing one of the most critical bottlenecks in real-time AI vision applications for mobile and embedded devices.
The cornerstone of this advancement lies in overcoming the traditional compromise faced by AI-driven image processing systems. Historically, achieving higher resolution visual input required extensive GPU memory and computational power, creating a significant barrier to real-time implementation on resource-constrained devices such as smartphones and humanoid robots. Conversely, compressing images into low-resolution features, which is a common practice to conserve resources, often leads to the loss of crucial visual details like small objects and fine structural elements. This trade-off has long hindered the deployment of precise AI vision in dynamic and mobile environments.
The “Upsample Anything” technology ingeniously sidesteps this issue by introducing a training-free upsampling mechanism capable of reconstructing high-resolution features from low-resolution inputs without additional data training. Unlike conventional methods that necessitate retraining or complex optimization when exposed to new data or environmental conditions, this approach adapts instantly by leveraging the edge and structural information inherent within a single input image. This capability drastically simplifies deployment and customization across diverse AI vision applications.
At the heart of the method is a sophisticated process called test-time optimization (TTO), which refines pixel-wise anisotropic kernel parameters based on the specific low-resolution image. These learned kernels are then applied to foundational feature maps to generate high-resolution representations. The final phase involves pixel-wise anisotropic Joint Bilateral Upsampling, a technique that preserves edge details and texture fidelity while reconstructing the image in high resolution. This multi-stage pipeline ensures that essential visual information is restored with remarkable accuracy and efficiency.
Highlighted through an illustrative example, the research team demonstrated the restoration of a 224×224 pixel image— a common resolution in AI research— to a clarity nearly identical to the original, within approximately 0.4 seconds of computation. This substantial leap in processing speed and memory efficiency heralds a new era for real-time AI vision, where systems can now maintain high precision without the prohibitive cost of extensive hardware resources. The direct implication is that AI can better understand and interact with its environment, making it invaluable for applications requiring fine object detection and manipulation.
The utility of “Upsample Anything” stretches far beyond humanoid robots. Autonomous driving systems stand to benefit immensely, as they depend heavily on the rapid and accurate interpretation of complex visual scenes. With this technology, vehicles can process crucial visual cues in a more resource-efficient manner, enhancing safety and navigation capabilities. Additionally, on-device AI for smartphones and other portable gadgets can achieve superior visual comprehension while minimizing battery drain and thermal load— critical factors for consumer electronics.
The research team’s achievement was officially recognized at CVPR 2026, the most prestigious conference dedicated to computer vision and artificial intelligence. The work earned the “CVPR Compute Gold Star” award, a distinction granted to research that excels in computational efficiency. Furthermore, it was named the “Transparency Champion” for leading in research process transparency and reproducibility, emphasizing the team’s commitment to responsible and open scientific practices. This dual recognition highlights not only the technological ingenuity but also the ethical and procedural rigor of the research.
One of the most remarkable attributes of this technology is its universality and plug-and-play nature. Its training-free design means that no additional datasets or retraining cycles are required when deploying it in different settings. This contrasts sharply with existing upsampling methods that often demand labor-intensive tuning or environment-specific learning. Consequently, “Upsample Anything” can seamlessly integrate into a wide array of AI frameworks, simplifying engineering workflows and accelerating the timeline from development to deployment.
Professor Changick Kim, the project lead from KAIST’s School of Electrical Engineering, emphasized the transformative potential of this technology. He noted that by boosting visual precision with minimal resource demands, the algorithm can catalyze the commercial viability of complex AI entities like humanoid robots and enable more powerful on-device AI applications. The convergence of computational efficiency and high visual fidelity, he added, represents a critical step forward in the quest to bring sophisticated AI capabilities to everyday devices.
This advancement is particularly significant in the context of emerging AI models based on world models— frameworks that aim to understand and predict changes in physical environments. Effective deployment of such models often requires processing a large volume of visual data accurately and expediently. By enabling high-resolution feature upsampling without overwhelming hardware limitations, “Upsample Anything” paves the way for smarter, faster, and more adaptive AI models that truly comprehend their surroundings in detail.
The first author of the study, Minseok Seo, a doctoral student at KAIST, alongside co-authors from MIT and Microsoft, detailed their findings in a paper titled “Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling.” The work is publicly accessible via the arXiv repository (DOI: 10.48550/arXiv.2511.16301), facilitating further exploration and innovation by the wider research community. Their approach exemplifies a blend of elegance and practicality, challenging the assumption that high computational cost is a prerequisite for high-quality visual reconstruction in AI.
In conclusion, the “Upsample Anything” technology marks a significant leap in AI visual processing by resolving a longstanding dilemma between image resolution and hardware resource constraints. Its immediate applicability across various AI-driven fields— ranging from robotics to autonomous systems and portable devices— underscores its impact and longevity. As AI continues to entwine itself with daily human interaction and technological progress, innovations like this will form the backbone of smarter, more capable, and accessible intelligent systems.
Subject of Research: Not applicable
Article Title: Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling
News Publication Date: 17-June-2026
Web References: https://dx.doi.org/10.48550/arXiv.2511.16301
References: Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling, DOI:10.48550/arXiv.2511.16301 (Seo et al., 2026)
Image Credits: KAIST
AI vision, upsampling technology, GPU memory efficiency, real-time image processing, test-time optimization, pixel-wise anisotropic kernels, Joint Bilateral Upsampling, on-device AI, humanoid robots, autonomous driving, world models, computer vision, computational efficiency
As NASA prepares for humanity’s return to the Moon, researchers at Texas A&M University are exploring how the same material could become a key building block for the first permanent settlements beyond Earth.
The Moon’s surface is surprisingly unlike anything we would consider soil on Earth. Instead, it is covered by a fine powder of pulverized rock and jagged glass that slips into every gap, grinds through seals and gaskets, and exists in a vacuum exposed to intense radiation and extreme temperature swings capable of bending steel. Yet researchers at Texas A&M University see opportunity rather than obstacles in this harsh environment.
Scientists refer to this material as lunar regolith, and it is considered one of the greatest challenges to long-term human habitation beyond Earth.
As NASA advances plans for a permanent human presence on the Moon through its Lunar Innovation Park initiative, Texas A&M researchers are developing technologies designed to transform the lunar surface into a foundation for future settlements.
“We are moving past the era of ‘flags and footprints,’” said Dr. Patrick Suermann, professor of construction science and retired U.S. Air Force lieutenant colonel in a statement. “We have to stop thinking like explorers and start thinking like settlers.”
Arguably, the greatest challenge facing lunar construction may not be engineering, but cost. Transporting materials from Earth remains extraordinarily expensive, with estimates ranging from roughly $1 million to $1.3 million per kilogram delivered to the Moon. As a result, researchers are increasingly focused on utilizing resources already available on the lunar surface rather than transporting materials from Earth.
“The future depends on building infrastructure from resources already on the moon,” explained Suermann.
“The high cost of shipping to the moon is the million-dollar problem,” Suermann said. “Every time you can cut the mass of a payload, you save a fortune. That’s why the future depends on building infrastructure from resources already on the moon.”
Helping advance that vision is Dr. Robert Ambrose of the Texas A&M Space Institute, located near NASA’s Johnson Space Center in Houston. The institute has received $200 million in support from the Texas Legislature.
“One of the most exciting features of the 240-acre facility is it’s two-and-a-half acre testing areas: one replicating the surface of the moon, the other Mars,” says Suermann.
These facilities allow researchers to evaluate robotic systems, autonomous vehicles, and construction technologies under conditions similar to those expected during future lunar and Martian missions.
Meanwhile, the university’s Construction Automation, Safety and Education (CASE) Lab is exploring how humans and machines can collaborate in extreme environments. Led by Dr. Gilles Albeaino, the lab focuses on mixed-reality technologies and semi-autonomous robotic systems capable of performing construction tasks on the Moon.
“On the moon, construction operations will depend on semi-autonomous robotic systems,” Suermann said. “The CASE lab is leading research into how humans and machines can work together in environments where humans can’t safely do everything themselves.”
“Every tool matters. Every ounce of material you ship matters,” added Suermann. “So, the question becomes: how do you use the environment itself as your supply chain, and how can you augment machines to become your partner in austere environments?”
Suermann’s experience combines two worlds: military service and construction science. Before joining Texas A&M in 2017, he served in the U.S. Air Force, deploying to remote locations that included Afghanistan, Guam, and Greenland.
Those experiences operating in challenging and isolated environments continue to inform his thinking about future lunar settlements.
“My experiences in serving the U.S. Air Force were formative and transformative,” Suermann said. “It taught me a great deal about construction, and that what can go wrong will go wrong.”
It shows, to me, that lunar regolith isn’t too dissimilar from the terrain we have here on Earth,” Suermann adds. “At the end of the day, construction is construction.”
The broader question remains whether humanity should be expanding its footprint beyond Earth at all. While plans for lunar settlements continue to advance, critics point out that many environmental, political, and social challenges remain unresolved here at home.
As governments and private companies invest billions into space infrastructure, some argue that humanity must carefully consider how development beyond Earth aligns with priorities on our own planet. Others contend that technologies developed for lunar habitation could ultimately provide benefits back on Earth, helping address challenges ranging from resource management to sustainability.
Chrissy Newton is a PR professional and founder of VOCAB Communications. She hosts the Rebelliously Curious podcast, which can be found on The Debrief’s YouTube Channel. Follow her on X: @ChrissyNewton and at chrissynewton.com.
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