Tuesday, February 24, 2026

 

New technology reveals hidden DNA scaffolding built before life ‘switches on’





Medical Research Council (MRC) Laboratory of Medical Sciences

An early Drosophila embryo captured during a wave of nuclear division. 

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An early Drosophila embryo captured during a wave of nuclear division. Dividing nuclei (blue) and non-dividing nuclei (pink) illustrate the rapid, highly organised nature of early development and the substantial regulation of genome organisation needed to enable proper gene activation despite repeated disruption as nuclei divide.

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Credit: Clemens Hug





For decades, scientists viewed the genome of a newly fertilised egg as a structural ‘blank slate’ – a disordered tangle of DNA waiting for the embryo to ‘wake up’ and start reading its own genetic instructions. 

In research published today in Nature GeneticsProfessor Juanma Vaquerizas and his team have found that a surprising level of structure is already in place. They’ve developed a breakthrough technology, called Pico-C, which enables scientists to see the 3D structure of the genome in unprecedented detail. Using this technique, they discovered that well before the genome fully awakens – a critical event known as Zygotic Genome Activation – a sophisticated 3D scaffold of DNA is already being built. Understanding how DNA folds in space matters because this controls which genes can be turned on during development, helping cells function properly and preventing developmental defects and disease. 

“We used to think of the time before the genome awakens as a period of chaos,” explains Noura Maziak, lead author of the study. “But by zooming in closer than ever before, we can see that it’s actually a highly disciplined construction site. The scaffolding of the genome is being erected in a precise, modular way, long before the ‘on’ switch is fully flipped.” 

Pico-C: Seeing More with Less 

The team’s discovery was made in the fruit fly (Drosophila). In the first few hours after fertilisation, a fly embryo undergoes a rapid series of nuclear divisions, creating thousands of cells. It’s this high-speed environment that makes the fruit fly perfect for studying the fundamentals of genetics. 

Using their ultra-sensitive Pico-C technology, they mapped the 3D structure of the fruit fly genome during these early developmental stages. They found that the 3D loops and folds of DNA follow a modular logic, allowing different inputs to regulate specific parts of the genome. It is a complex architectural programme that ensures the information encoded in the genome is ready for action the moment it is needed. 

As well as providing high-resolution detail about the shape of DNA, Pico-C only requires tiny amounts of sample – ten times less than standard methods. This opens the door to opportunities for studying how DNA folding shapes gene regulation and its implication in many diseases in greater detail than previously possible. 

From Fly Embryos to Human Health 

 While the ‘blueprint’ of this architecture was discovered in fruit flies, the importance of maintaining it applies directly to humans. In a companion study published today in Nature Cell Biology led by Professor Ulrike Kutay and collaborators at ETH Zürich in Switzerland, the team applied this high-resolution mapping to human cells. 

They investigated what happens when the ‘anchors’ that hold this 3D structure in place are removed. The results were striking: when the architecture collapses, the human cell mistakes the structural failure for a viral attack. This triggers the cell’s innate immune system, sounding a false alarm that can lead to inflammation and disease. 

“These two studies tell a complete story,” says Juanma. “The first shows us how the genome’s 3D structure is carefully built at the start of life. The second shows us the disastrous consequences for human health if that structure is allowed to collapse.” 

This study was funded by the Medical Research Council and the Academy of Medical Sciences (AMS) through an AMS Professorship award.  

 

How Japanese medical trainees view artificial intelligence in medicine



A multicenter Japanese study developed and validated a tool to measure attitudes toward artificial intelligence




Juntendo University Research Promotion Center

Attitudes Toward Artificial Intelligence in Medical Training: A Japanese Validation Study 

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A new psychometrically validated scale that helps understand and support the integration of artificial intelligence in Japanese medical education.

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Credit: Hirohisa Fujikawa from Juntendo University Faculty of Medicine, Japan





Artificial intelligence (AI) is rapidly transforming healthcare and medical education. From enhancing diagnostic accuracy and clinical decision-making to enabling virtual simulations and personalized learning, AI technologies are becoming embedded in the daily practice of clinicians and trainees. Despite these benefits, concerns remain regarding ethical responsibility, data privacy, the loss of human autonomy, and potential job displacement. As AI continues to expand across medical systems worldwide, understanding how future physicians perceive and engage with these technologies is increasingly important.

Attitudes toward AI play a critical role in determining whether AI tools are accepted, trusted, and effectively integrated into clinical practice and education. Positive attitudes promote openness and responsible use, whereas negative perceptions may lead to skepticism and underutilization. Accurate measurement of attitudes toward AI among medical students and residents is, therefore, essential for identifying barriers to adoption and designing effective educational interventions. In 2024, Stein and colleagues introduced the 12-item attitudes towards artificial intelligence (ATTARI-12) scale, a brief and reliable measure encompassing affective, cognitive, and behavioral dimensions. However, the absence of a validated Japanese version limited its applicability in Japan, where cultural factors—such as uncertainty avoidance and social norms—may influence responses to emerging technologies.

To address this gap, a team of researchers from Juntendo University, Japan—led by Project Assistant Professor Hirohisa Fujikawa and colleagues Dr. Hirotake Mori, Dr. Yuji Nishizaki, Dr. Yuichiro Yano, and Dr. Toshio Naito—collaborated with Dr. Kayo Kondo from Durham University, United Kingdom. Together, they developed and validated a Japanese version of the scale (J-ATTARI-12) for use among medical students and resident physicians. Dr. Fujikawa explained the motivation behind the study: “We observed wide variation in how learners responded to AI, yet no validated tool existed in Japan to measure these differences. This scale helps educators understand learners’ attitudes and better prepare future physicians for AI-enabled practice.” The results of their study were published in Volume 12, Issue e81986 of the journal JMIR Medical Education on January 14, 2026.

The study followed internationally recognized guidelines for translation and cross-cultural adaptation to ensure linguistic accuracy and cultural relevance. A nationwide online survey was conducted between June and July 2025, recruiting medical students and residents from multiple universities and hospitals across Japan. A total of 326 participants were included in the analysis. Psychometric evaluation employed a split-half validation approach: exploratory factor analysis (EFA) was conducted on one-half of the sample to identify the underlying factor structure, and confirmatory factor analysis (CFA) was performed on the other half to assess model fit. Convergent validity was examined by correlating J-ATTARI-12 scores with attitudes toward robots—a related construct—while internal consistency reliability was assessed using Cronbach’s α.

The analyses yielded several key findings. EFA identified a 2-factor structure reflecting "AI anxiety and aversion" and "AI optimism and acceptance." CFA demonstrated that this 2-factor model showed good model fit and outperformed a one-factor model. Convergent validity was supported by a moderate positive correlation between J-ATTARI-12 scores and attitudes toward robots, and internal consistency reliability was high, indicating that the scale reliably measures attitudes toward AI among Japanese medical trainees.

The study offers important educational and research implications. Dr. Fujikawa noted, “Educators can use this scale to evaluate AI-related training and identify learners who may feel uncertain or hesitant about using AI. It also allows researchers to track how attitudes evolve as AI becomes more integrated into healthcare.” By providing a culturally adapted and psychometrically sound instrument, the J-ATTARI-12 supports data-driven curriculum development and informed decision-making in medical education.

Reflecting on the broader significance, Dr. Fujikawa emphasized, “The successful adoption of AI in healthcare depends on clinicians’ acceptance as much as on technological performance. Making these attitudes visible enables better education and more responsible implementation.” He added that the scale will be used in a “Medicine and AI” program launching at Juntendo University in 2026 and is expected to facilitate future cross-national research.

In conclusion, this study successfully developed and validated the J-ATTARI-12—the first Japanese instrument for assessing attitudes toward AI among medical students and residents. By providing a reliable and valid measure, it lays a strong foundation for advancing AI education, research, and integration within Japan’s medical training systems.

 

Reference

DOI: https://doi.org/10.2196/81986

Authors: Hirohisa Fujikawa1,2,3, Hirotake Mori1, Kayo Kondo4, Yuji Nishizaki1,5, Yuichiro Yano1, and Toshio Naito1

Affiliations:

Department of General Medicine, Juntendo University Faculty of Medicine, Japan

Department of Medical Education Studies, International Research Center for Medical Education, Graduate School of Medicine, The University of Tokyo, Japan

Center for General Medicine Education, School of Medicine, Keio University, Japan

School of Modern Languages and Cultures, Durham University, United Kingdom

Division of Medical Education, Juntendo University School of Medicine, Japan

 

About Project Assistant Professor Hirohisa Fujikawa

Hirohisa Fujikawa is a Project Assistant Professor in the Department of General Medicine, Juntendo University Faculty of Medicine, Japan. He holds an M.D. and a Ph.D. (2023) from The University of Tokyo and is an expert in health professions education. With over 10 years of academic and clinical experience, Dr. Fujikawa has published more than 90 peer-reviewed articles in international journals. His research focuses on ambiguity tolerance, working-hour restrictions for physicians, patient care ownership, workplace social capital, and the psychometric evaluation of educational instruments. He has served as the corresponding author on multiple multicenter studies, and has received competitive research funding and academic recognition for his contributions to the field of health professions education research.

 

History of Juntendo University

Juntendo was originally founded in 1838 as a Dutch School of Medicine at a time when Western medical education was not yet embedded as a normal part of Japanese society. With the creation of Juntendo, the founders hoped to create a place where people could come together with the shared goal of helping society through the powers of medical education and practices. Their aspirations led to the establishment of Juntendo Hospital, the first private hospital in Japan. Through the years the institution's experience and perspective as an institution of higher education and a place of clinical practice has enabled Juntendo University to play an integral role in the shaping of Japanese medical education and practices. Along the way the focus of the institution has also expanded, now consisting of nine undergraduate programs and six graduate programs, the university specializes in the fields of health science, health and sports science, nursing health care and sciences, and international liberal arts, as well as medicine. Today, Juntendo University continues to pursue innovative approaches to international level education and research with the goal of applying the results to society.

 

Mission Statement

The mission of Juntendo University is to strive for advances in society through education, research, and healthcare, guided by the motto “Jin – I exist as you exist” and the principle of “Fudan Zenshin - Continuously Moving Forward”. The spirit of “Jin”, which is the ideal of all those who gather at Juntendo University, entails being kind and considerate of others. The principle of “Fudan Zenshin” conveys the belief of the founders that education and research activities will only flourish in an environment of free competition. Our academic environment enables us to educate outstanding students to become healthcare professionals patients can believe in, scientists capable of innovative discoveries and inventions, and global citizens ready to serve society.

 

MambaAlign fusion framework for detecting defects missed by inspection systems




Researchers develop an efficient system that detects subtle defects missed by existing industrial visual inspection systems




Shibaura Institute of Technology

MambaAlign framework for multimodal industrial anomaly detection 

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Researchers propose a new alignment-aware state-space fusion framework called MambaAlign that produces tighter, less fragmented anomaly maps, and is substantially more robust to modest misalignment than prior fusion or attention-heavy approaches

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Credit: Dr. Phan Xuan Tan from Shibaura Institute of Technology, Japan, and Dr. Dinh-Cuong Hoang from FPT University, Vietnam Source link: https://academic.oup.com/jcde/article/13/1/514/8405688





Industrial quality inspection plays a critical role in manufacturing, from ensuring the reliability of electronics and vehicles to preventing costly failures in aerospace and energy systems. Traditional vision-based inspection systems typically rely on Red, Green, Blue (RGB) cameras, which are fast and inexpensive but often miss defects related to geometry (scratches or dents), material structure, or heat dissipation. While additional sensors, such as thermal cameras or depth scanners, can reveal these hidden anomalies, effectively combining information from multiple sensors remains a major technical challenge. Many existing fusion approaches either lose fine spatial detail, require heavy computation, or fail when sensors are not perfectly aligned—common issues in factory settings.

To address these, a research team led by Associate Professor Phan Xuan Tan from the Innovative Global Program, College of Engineering, Shibaura Institute of Technology, Japan, along with Dr. Dinh-Cuong Hoang from the FPT University, Vietnam, has proposed a new framework, termed MambaAlign, which enables computationally efficient fusion of multimodal sensor data while remaining robust to modest sensor misregistration. The study was made available online on December 27, 2025, and was published in Volume 13, Issue 1 of the Journal of Computational Design and Engineering on January 01, 2026.

“Existing systems miss geometric and material/thermal defects, amplify sensor artifacts, lose localization, or are brittle to modest misregistration. In addition, efficiently capturing long-range, orientation-sensitive context (important for thin/oblique defects) without the quadratic cost of dense attention remained unresolved. These challenges of existing systems motivated us to develop a fusion approach that is alignment aware, uses state-space recurrences to collect long-range directional context, and exchanges semantic guidance at deep stages via lightweight cross-recurrence (Cross Mamba Interaction), and then reconstitutes low-level channels top-down to preserve precise localization,” says Dr. Tan.

MambaAlign introduces an alignment-aware state-space fusion framework for multimodal industrial anomaly detection. The method captures long-range and orientation-aware context using state-space refinement, which is particularly effective for detecting thin or oblique defects such as scratches and cracks. Instead of relying on computationally expensive global attention, MambaAlign exchanges semantic guidance between sensors only at high-level feature stages, keeping the computational cost close to linear. A top-down reconstruction mechanism then reconstitutes low-level feature channels, allowing the system to tolerate modest sensor misalignment while preserving precise pixel-level localization.

Extensive experiments demonstrate the effectiveness of the approach. Averaged across three RGB-plus-auxiliary-modality (RGB-X) datasets, MambaAlign improves image-level area under the receiver operating characteristic curve (AUROC) by approximately 4.8%, pixel-level AUROC by about 5.0%, and area under the per-region overlap curve by roughly 6.5% compared with prior methods. Importantly, these gains come without excessive computational overhead. The model sustains close to 30 frames per second at moderate resolutions, with controlled memory usage, making it practical for deployment in real production lines.

MambaAlign achieves state-of-the-art localization with parameters and runtime suitable for real-time inspection. It not only provides higher detection accuracy but also tighter and less fragmented anomaly maps. This translates directly into fewer false alarms, fewer missed defects, and more actionable outputs for engineers on the factory floor,” says Dr. Tan.

Overall, the study highlights wide-ranging industrial relevance. In electronics and printed circuit board inspection, MambaAlign can detect micro-cracks or missing components that subtly alter thermal or geometric patterns. In aerospace and composite manufacturing, fusing RGB and thermal data helps reveal subsurface delamination invisible to standard cameras. Automotive body inspection benefits from improved detection of dents, scratches, and seam defects, while the system’s real-time performance enables inline inspection on conveyor belts or robotic vision stations. By reducing manual inspection effort, minimizing scrap, and improving reliability under realistic sensor conditions, MambaAlign addresses a long-standing bottleneck in industrial quality assurance.

 

Reference

DOI: https://doi.org/10.1093/jcde/qwaf143

 

About Shibaura Institute of Technology (SIT), Japan

Shibaura Institute of Technology (SIT) is a private university with campuses in Tokyo and Saitama. Since the establishment of its predecessor, Tokyo Higher School of Industry and Commerce, in 1927, it has maintained “learning through practice” as its philosophy in the education of engineers. SIT was the only private science and engineering university selected for the Top Global University Project sponsored by the Ministry of Education, Culture, Sports, Science and Technology and had received support from the ministry for 10 years starting from the 2014 academic year. Its motto, “Nurturing engineers who learn from society and contribute to society,” reflects its mission of fostering scientists and engineers who can contribute to the sustainable growth of the world by exposing their over 9,500 students to culturally diverse environments, where they learn to cope, collaborate, and relate with fellow students from around the world.

Website: https://www.shibaura-it.ac.jp/en/

 

About Associate Professor Phan Xuan Tan from SIT, Japan

Dr. Phan Xuan Tan is an Associate Professor in the Innovative Global Program, College of Engineering, Shibaura Institute of Technology (SIT), Japan. He earned a B.E. in Electrical-Electronic Engineering from Le Quy Don Technical University, and an M.S. in Computer and Communication Engineering from Hanoi University of Science & Technology, Vietnam. He received his Ph.D. in Functional Control Systems from SIT in 2018. His academic work bridges engineering and artificial intelligence (AI), with research centered on computer vision, image processing, generative AI, and AI safety.