Nobel Laureate Konstantin Novoselov Visit

National Central University (NCU) held a Nobel Bench Dedication Ceremony on September 22 to welcome Prof. Sir Konstantin Novoselov, recipient of the 2010 Nobel Prize in Physics, to campus. NCU President Hsiau, Shu-San joined Professor Novoselov in unveiling the Nobel Bench, with faculty members, students, and distinguished guests gathering to witness the occasion.

 

In addition to attending the dedication ceremony, Professor Novoselov delivered a Nobel Laureate Lecture titled “From 2D Materials to Industrial Futures: How Cross-Disciplinary Innovation Moves Beyond the Lab,” sharing insights into advances in graphene and two-dimensional materials as well as their potential future applications. The event was jointly organized by National Central University and the University Alliance in Talent Education Development (UAiTED), with support from the Sayling Wen Cultural & Educational Foundation.

 

President Hsiau noted that Professor Novoselov’s pioneering research on graphene ushered in a new era of two-dimensional materials, transforming the direction of materials science while exerting a profound influence on emerging fields including electronics, energy, nanotechnology, and quantum materials.

 

Professor Novoselov’s visit offered NCU faculty members and students a rare opportunity to engage closely with a world-class scientist and gain insight into his research journey and approach to scientific inquiry. Through the Nobel Bench and the Nobel Laureate Lecture, NCU hopes to embed the spirit of knowledge, exploration, and innovation more deeply into campus life, inspiring younger generations to ask bold questions and venture into the unknown—and perhaps allowing the next scientific idea that changes the world to take root right here on campus.

NTU Decodes Plant Symbiosis to Fuel Farming

As global agriculture confronts severe environmental stress and the imperative to reduce synthetic fertilizer dependency, unlocking the natural mutualisms of crop ecosystems has emerged as a paramount scientific pursuit. Arbuscular mycorrhizal (AM) symbiosis—a pervasive mutualistic relationship between plants and soil fungi—enables crops to trade carbon for vital subterranean nutrients, particularly phosphate. Following an eight-year investigation, a research team led by Associate Professor Shu-Yi Yang from the Institute of Plant Biology at National Taiwan University (NTU) has published a breakthrough study in Nature Communications. Titled “OsIDD7 integrates signaling networks for arbuscular mycorrhizal symbiosis,” the research uncovers how the transcription factor IDD7 serves as a master molecular nexus governing this ancient partnership in rice.

While AM fungi develop branched arbuscules within root cells to facilitate reciprocal nutrient delivery, how host plants coordinate internal hormone cascades with phosphate signals to sustain these structures has long remained elusive. The NTU team demonstrated that IDD7 expression surges in arbuscule-containing root cells; without it, fungal colonization collapses, disrupting lipid synthesis and nutrient exchange. Mechanistically, IDD7 collaborates with SLR1—a DELLA protein that relays gibberellin hormone signals—and PHR2, a primary transcription factor governing phosphate starvation responses. Together, this tri-protein complex efficiently activates key symbiotic genes, with IDD7 simultaneously modulating PHR2 expression, confirming its indispensable role at the epicenter of the symbiotic transcriptional network.

By delineating how crops integrate hormonal and nutrient cues to foster microbial symbiosis, NTU’s discovery offers profound implications for food security and climate-resilient farming. Deepening the understanding of these symbiotic pathways provides an actionable genetic roadmap to enhance nutrient-use efficiency and stress tolerance in essential cereal crops. As the research team explores IDD7’s broader epigenetic mechanisms, NTU continues to translate fundamental plant science into scalable agricultural solutions, reinforcing the institution’s commitment to advancing the United Nations Sustainable Development Goals through pioneering ecological innovation.

NTU Reveals How Bacterial Promoters Tune RNA

In molecular biology, the prevailing paradigm of gene regulation has long placed transcription factors at the helm, treating promoters on DNA sequences as passive docking sites. Challenging this conventional view, an interdisciplinary research team led by Associate Professor Hsin-Hung David Chou from the Department of Life Science at National Taiwan University (NTU)—in collaboration with Distinguished Professor Nei-Li Chan (NTU College of Medicine) and Associate Professor I-Ren Lee (National Taiwan Normal University)—has published landmark findings in Nature Communications. Their study establishes that bacterial promoters intrinsically dictate the dynamic range and evolutionary trajectory of gene transcription, operating with the precision of an integrated audio system.

Utilizing Escherichia coli as a model organism, the team systematically synthesized and evaluated a comprehensive library of 16.8 million promoter sequence variants, integrating massive empirical datasets with biophysical modeling and single-molecule kinetics. The analysis uncovered a distinct operational dichotomy: the “–10 element” acts as an ON/OFF power switch essential for basal activation, while the “–35 element” functions as a fine-tuning volume knob that scales expression magnitude. Crucially, transcription factors dynamically modulate this volume knob’s engagement with RNA polymerase. The researchers also identified a vital trade-off rule—only promoters with intermediate basal expression attain maximal fold-change regulation—resolving an ongoing academic controversy stemming from prior conflicting hypotheses in Science.

By reaffirming the promoter’s active, autonomous role at the center of transcriptional control, the NTU-led breakthrough provides foundational design principles for synthetic biology and metabolic engineering. The discovery underscores the indispensable value of holistic, high-throughput empirical data in systems biology, avoiding fragmented conclusions. Supported by NTU’s advanced core instrumentation, this research exemplifies how high-impact basic science translates into precise genetic blueprints, driving forward next-generation biotechnology and sustainable biomanufacturing solutions.

NTU Finds GLP-1 Drugs Slash Cancer Risk

As obesity continues to be a major risk factor for various malignancies, integrating innovative medical approaches has become crucial for public health. A multinational research team led by National Taiwan University (NTU) Hospital recently achieved a significant breakthrough, publishing their findings in the top-tier oncology journal Annals of Oncology. The study reveals that non-diabetic obese adults using GLP-1 receptor agonists—commonly known as weight-loss injections like Semaglutide and Tirzepatide—experience a remarkable 41% reduction in the risk of developing obesity-associated cancers (OACs) compared to those receiving only diet and exercise counseling.

The research’s advancement is anchored in a comprehensive analysis using the TriNetX global health research network. Spearheaded by Dr. Heng-Cheng Hsu from NTU Hospital and Dr. Ying-Cheng Chiang from the Hsinchu NTU Branch, in collaboration with National Yang Ming Chiao Tung University and Houston Methodist Hospital, the team analyzed over 160,000 matched individuals from 2014 to 2025. By employing advanced statistical models like Propensity Score Matching (PSM), the researchers successfully transformed complex electronic medical records into a high-quality “emulated clinical trial.” Their findings demonstrated broad protective benefits against 13 types of cancers, regardless of gender, a BMI over 40, or the specific GLP-1 drug used.

Looking toward the future of preventive care, this breakthrough holds profound implications, particularly for highly obesity-linked diseases such as endometrial cancer. While the study highlights the massive cancer-preventive potential of weight management in non-diabetic populations, researchers caution that clinical evaluation is still required, as these drugs do not yet have an official indication for cancer prevention. By leveraging real-world data and advanced biostatistics, NTU continues to provide critical scientific evidence to address complex medical challenges, establishing a solid foundation for future precision medicine and public health policies.

TMU AI Improves CT Decisions in the ER

In busy emergency departments, every minute and every medical resource matters. An AI-driven prediction model developed by Taipei Medical University may help physicians determine more efficiently whether patients need CT scans, supporting faster decision-making, reducing unnecessary imaging, and improving hospital resource use.

The model learns from real-world emergency department records, including mixed languages notes, abbreviations, and fragmented symptom descriptions. By turning unstructured medical language into practical decision support, the system demonstrates how artificial intelligence can assist frontline clinicians in complex and fast-paced care settings.

The study was led by Professor Yung-Chun Chang from the Graduate Institute of Data Science at Taipei Medical University (TMU), in collaboration with Dr. Ting-Yun Huang from the Emergency Department of Shuang-Ho Hospital.

Addressing Emergency Department Overcrowding

Emergency department overcrowding remains a critical global healthcare challenge affecting treatment efficiency, patient flow, and medical safety. Computed tomography (CT) imaging is essential for diagnosing serious and potentially life-threatening conditions, but previous studies have estimated that 20–40% of CT scans may be unnecessary. Overuse of CT imaging can increase healthcare costs, place pressure on hospital resources, and expose patients to avoidable radiation.

To address this issue, the research team developed an AI-based predictive system designed to support early clinical decision-making and optimize medical resource allocation.

Turning Clinical Language into AI Decision Support

A key innovation of this study lies in its ability to tackle the complexities of real-world emergency departments. Clinical records in Taiwanese hospitals often include mixed Chinese–English terminology, domain-specific abbreviations, and institution-specific expressions. In emergency settings, these notes are often brief, fragmented, and highly unstructured due to time constraints and urgent clinical workflows.

To overcome these challenges, the research team developed a comprehensive clinical language engineering pipeline capable of processing multilingual and highly heterogeneous medical text. The system performs automated language normalization, terminology standardization, correction of inconsistent clinical expressions, and semantic representation learning from noisy narrative data

This enables the model to extract meaningful diagnostic information from incomplete or irregular documentation and improves its robustness in real-world clinical settings.

The proposed framework integrates large-scale clinical text processing, multimodal feature fusion, prompt-based feature representation, and data augmentation using large language models. Unlike conventional prediction methods that rely mainly on structured numerical data or laboratory results, the system leverages unstructured clinical narrative text—such as chief complaints, symptom descriptions, medical history, and pain severity—as the primary source of predictive information. This design allows the model to capture subtle clinical patterns and contextual relationships often overlooked by traditional machine learning approaches.

Using 165,391 emergency department records from Shuang-Ho Hospital, the model demonstrated strong predictive performance, achieving an AUROC of 0.88. It showed high reliability in identifying patients who were unlikely to require CT imaging and outperformed traditional machine learning methods as well as existing biomedical language models.

The system was also developed with practical clinical implementation in mind. After training, the model can operate on standard hospital computing infrastructure and has the potential to be integrated with electronic health record systems, enabling real-time clinical decision support in diverse healthcare environments.

Advancing AI-Enabled Healthcare Innovation

This research highlights the importance of interdisciplinary collaboration between AI engineers and frontline clinicians. It also demonstrates the transformative potential of large language models in healthcare system optimization.

By addressing the complexities of multilingual and unstructured clinical data, the study provides a foundation for future AI-driven medical decision support systems. The research was supported by Taiwan’s National Science and Technology Council and published in Engineering Applications of Artificial Intelligence, an international journal with a 2024 impact factor of 8.0 and a ranking in the top 2.5% of the Engineering, Multidisciplinary category. These achievements reflect TMU’s growing impact in AI-enabled healthcare innovation and its commitment to advancing precision medicine and intelligent healthcare.

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AI EEG Predicts Depression Drug Response

Major depressive disorder (MDD) remains one of the world’s most disabling conditions, yet finding an effective antidepressant treatment often still relies on a prolong trial-and-error process. A research team lead by Professor Syu-Jyun Peng has developed an EEG-based, machine-learning drug-response prediction system designed to help clinicians identify whether a patient is likely to respond to medication within the first weeks of treatment.

The project, “AI EEG Drug-Response Prediction System: Longitudinal Tracking and Personalized Recommendations for Depression Treatment,” received the 2025 IIP International Inventor Prize – International Innovation and Invention Elite Award, highlighting its potential contribution to precision psychiatry and personalized mental healthcare.

Turning Early EEG Signals into Actionable Predictions

Although antidepressants are widely used, patients often need to try multiple medications before finding an effective treatment. This delay can prolong symptoms, increase relapse risk, and place additional burden on patients, families, and healthcare systems.

Associate Professor Peng’s team developed the system based on a key insight: brain network activity may begin to change soon after medication starts, before clinical improvement becomes clearly visible. By capturing these early neurophysiological changes through electroencephalography, or EEG, the system aims to provide objective biomarkers that can support earlier and more individualized treatment decisions.

The study collected EEG recordings from 77 patients with major depressive disorder at baseline (Week 0) and after one week of medication (Week 1). These data were then used to predict treatment response at Weeks 4, 6, and 8 through machine-learning models combining EEG-derived features and clinical variables.

A Practical Pipeline for Real-World Clinics

The system was designed with real-world clinical application in mind. EEG is non-invasive, relatively affordable, and suitable for repeated monitoring, making it a promising tool for psychiatric care.

In the research pipeline, EEG signals are preprocessed and divided into canonical frequency bands (delta, theta, alpha, and beta) and converted into interpretable features.

The team focused on two complementary domains:

  1. Power-based metrics (absolute and relative power), capturing spectral characteristics of brain activity.
  2. Functional connectivity and phase synchronization metrics—including PLV, PLI, and wPLI, which measure how different brain regions coordinate with one another.

The team also computed a change index, comparing Week 1 and Week 0 EEG patterns, to identify early brain changes that may predict later treatment outcomes.

Promising Prediction Performance

The system achieved strong predictive results, with accuracy reaching 83.1% for Week 4, 73.3% for Week 6, and 80.0% for Week 8 treatment response.

Functional connectivity and phase synchronization features consistently emerged as major contributors to predictive performance. These findings support the idea that early changes in brain network dynamics may serve as meaningful biomarkers for antidepressant response.

From Research Prototype to Precision Psychiatry

The “heart” of this project was never just algorithm tuning—it was learning to build something that clinicians can confidently trust. Early on, EEG data can be complex and affected by artifacts, individual differences, and recording conditions. To address these challenges, the team refined preprocessing methods, strengthened artifact handling, and selected interpretable features with clinical relevance.

Following recognition at the 2025 IIP International Inventor Prize, the project will move toward larger-scale validation and clinical translation. Future development will focus on multi-site studies, clinician-friendly decision-support interface, multimodal data integration, and broader healthcare applications.

By helping clinicians identify likely treatment responders earlier, the system has the potential to reduce ineffective medication trials, shorten time to remission, and support more personalized depression care.

Advancing Personalized Mental Healthcare

This award-winning AI EEG drug-response prediction system represents an important step toward precision psychiatry. By combining EEG, machine learning, and longitudinal treatment tracking, Associate Professor Peng’s team aims to transform early brain responses into actionable clinical insights, which is helping patients move more quickly toward effective care.

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Original Article: Predicting the longitudinal efficacy of medication for depression using electroencephalography and machine learning

Keywords: Depression; Electroencephalography; Functional connectivity; Machine learning

Author Profile: Syu-Jyun Peng, Professor, In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine

NTU Team Unlocks Ovarian Cancer Spatial Code

Ovarian clear cell carcinoma (OCCC) presents a significant clinical challenge due to its high prevalence in East Asian populations and notorious resistance to chemotherapy. To break through these therapeutic bottlenecks, an interdisciplinary team from National Taiwan University (NTU) and NTU Hospital, led by Professor Ruby Yun-Ju Huang, recently published groundbreaking research in the prestigious journal Nature Communications. By integrating multi-platform spatial transcriptomics with advanced functional models, the team successfully created the world’s first “spatial atlas of OCCC”, precisely detailing how cancer cells adapt across various tumor microenvironments.

The study revealed that OCCC is not a homogeneous mass but exhibits immense spatial heterogeneity. Researchers discovered distinct differences in metabolism and epithelial-mesenchymal transition (EMT) between the tumor’s center and its periphery. Cancer cells at the core showed higher oxidative phosphorylation (OXPHOS) and epithelial traits, which correlated with better patient prognoses driven by the LCN2 gene. Conversely, cells near invasive or necrotic edges displayed lower metabolic activity and higher EMT. The team further identified the SOX9-LCN2 axis as a crucial regulatory mechanism governing this cellular plasticity, proving that re-inducing SOX9 can restore epithelial functions and offering a promising target for precision treatments.

Beyond scientific breakthroughs, this milestone underscores NTU’s exceptional capacity for fostering inclusive, interdisciplinary talent. Co-first authored by international doctoral candidate Le Truong Thang and NTU medical graduate Dr. Yi-Te Wang, the collaboration seamlessly bridged smart medical technology with clinical expertise. By institutionalizing diverse academic partnerships across high-impact research platforms, NTU continues to leverage its capacity to address complex health challenges, paving the way for next-generation clinical solutions in global oncology.

NTU Maps Dopamine Role in Brain Choices

As individuals make daily decisions, past experiences significantly influence their future choices. However, when the same action yields both positive and negative outcomes across different contexts, how does the brain integrate these conflicting signals? A research team led by Associate Professor Ming-Tsung Tseng at National Taiwan University’s Graduate Institute of Brain and Mind Sciences has recently uncovered the answer. Published in the prestigious journal PLOS Biology, their study reveals that past negative experiences tied to a specific choice increase exploratory behaviors, a mechanism directly regulated by the brain’s dopamine system.

While previous neuroscience research has typically examined reward and punishment learning independently, real-world decisions often involve accumulating mixed results. To address this, the NTU team designed a specialized learning task and combined it with functional magnetic resonance imaging (fMRI), computational modeling, and pharmacological validation. They discovered that previous punishment experiences prompt individuals to deviate from optimal, high-reward options in favor of exploring alternatives. Interestingly, this interaction is asymmetric; past reward experiences do not disrupt punishment learning in the same way. Further neuroimaging tests confirmed that blocking dopamine receptors eliminates this punishment-induced exploration, highlighting dopamine’s critical role in evaluating competing value signals within the prefrontal cortex.

This breakthrough not only maps the neural mechanisms behind complex human decision-making but also paves the way for clinical advancements. By decoding how the brain processes loss sensitivity under conflicting information, the findings provide a foundational framework for understanding learning and decision-making deficits in dopamine-related conditions, such as Parkinson’s disease, depression, and addiction. Supported by the National Science and Technology Council, NTU continues to leverage interdisciplinary research to unravel the complexities of the human brain, offering scalable insights for global health and medicine.

NTU Shows Biodiversity Sustains Stability

As extreme rainfall, heat waves, and droughts grow more frequent, the question of what keeps ecosystems from collapsing has moved from academic interest to policy urgency. Scientists have long known that biodiversity supports ecosystem function, but how it confers stability under severe environmental fluctuation has remained unresolved. Researchers at the Institute of Fisheries Science at National Taiwan University (NTU), working with Academia Sinica and international partners including Ryukoku University and Yokohama National University in Japan, have addressed the question through a pairing of long-term field observation and new theoretical modeling.

The first study drew on nine years of monitoring data from Taiwan’s Feitsui Reservoir, integrating 31 ecosystem functions related to the carbon cycle to assess how biodiversity shapes ecosystem multifunctionality. Microbial diversity was found to enhance ecosystem function consistently across multiple timescales—through typhoons, seasonal shifts, and interannual environmental change—whereas environmental variables such as rainfall, temperature, and nutrients influenced function only at particular timescales, identifying biodiversity as the more persistent driver. The second study built a theoretical model that departs from the conventional treatment of biodiversity as a fixed background condition, instead framing it as an ecological variable that changes dynamically over time. The model revealed a resource-diversity feedback linking species diversity with nutrients and food web structure, through which dynamic regulation of diversity alters nutrient use efficiency and predation, lowering the risk of ecosystem collapse. The team then validated the framework against 30 years of phytoplankton monitoring data from Lake Inba in Japan, confirming that both the average level of species diversity and its variation over time causally affect ecosystem stability.

The two studies are complementary: one demonstrates empirically that biodiversity sustains aquatic ecosystem function over the long term, while the other explains the mechanism by which it does so. The central insight—that stability depends not simply on how much biodiversity an ecosystem holds but on its capacity for dynamic self-regulation—provides a new theoretical foundation for conservation policy and reinforces biodiversity protection as a key lever for ecological resilience amid global environmental change. The first study appeared in Ecology Letters in 2025 with postdoctoral researcher Wan-Hsuan Cheng as lead author; the second was published in Ecology in 2026 with Assistant Professor Chun-Wei Chang as lead author.

γ-Tocotrienol Helps Prevent Muscle Atrophy

A research team led by Distinguished Professor Shih-Min Hsia from the School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, has uncovered the protective effects and underlying mechanisms of γ-tocotrienol—a member of the natural vitamin E family—in combating muscle atrophy. Their research was published in Redox Biology, a leading international journal (impact factor 11.9 in 2024; ranked in the top 4.7% of the field of Biochemistry & Molecular Biology). The study offers new scientific insights into inflammation-induced muscle atrophy and highlights promising directions for nutritional intervention.

muscle atrophyVisual summary of the study

The researchers demonstrated that γ-tocotrienol, a compound found in rice-bran oil, palm oil, and other natural sources, can effectively slow muscle loss through multiple molecular mechanisms. These findings provide a strong scientific basis for preventing aging- and sarcopenia-related diseases.

According to the study, γ-tocotrienol exerts its protective effects through two key pathways. First, it inhibits the generation of reactive oxygen species (ROS), thereby minimizing cellular damage caused by oxidative stress; second, it enhances mitochondrial biogenesis, helping to restore and maintain cellular energy production. Together, these actions help preserve muscle cell structure and function, thereby reducing the risk of inflammation-induced muscle atrophy.

Importantly, experimental results indicated that, compared with traditional α-tocopherol, γ-tocotrienol was more effective at inhibiting MuRF-1 and Atrogin-1, key factors associated with muscle atrophy. Animal models also revealed a significant preservation of muscle mass and strength, demonstrating the superior efficacy of γ-tocotrienol in preventing muscular atrophy.

Given its natural origin and strong safety profile, γ-tocotrienol holds great potential for applications in nutritional intervention and preventative medicine. It may be developed into health foods and specialized medical nutrition products to support muscle health in older adults, postoperative patients, and individuals experiencing prolonged immobilization.

The research team pointed out that this research not only opens new venues for understanding the mechanisms underlying muscle atrophy but also highlights the translational potential of natural nutrients in medical applications. In the future, the team will further investigate the synergistic of γ-tocotrienol with other nutrients and assess its applicability across different muscle atrophy models, continuing to provide a scientific foundation for healthy aging and chronic disease management.

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