arXiv subjects

Huijing Zou

Publications and source records attributed to Huijing Zou.

4 recordsLinked to original sources

arXiv2026

Single-Scan Characterization of $^{14}$N Nuclei via $^1$H-Detected Rotating-Frame Relaxometry

$^{14}$N NMR is notoriously difficult to perform in liquids due to the very fast spin relaxation and the large quadrupolar couplings, which render many signals invisible. We show here how $^{14}$N nuclei of biomolecular constituents can be probed indirectly by reintroducing the scalar relaxation of the second kind contribution to the polarization lifetimes of J-coupled protons in double resonance spin-locking experiments. The enhanced $^1$H relaxation rates in the rotating-frame allow for direct evaluation of nitrogen chemical shift and polarization lifetimes, from which one- and even two-bond $^1$H-$^{14}$N scalar couplings as well as $^{14}$N quadrupolar interactions can be determined. We demonstrate the versatility of this method by characterizing $^1$H-$^{14}$N spin pairs in several molecules of biological importance, showing proton relaxation enhancements beyond one order of magnitude. We further observe a pronounced effect from intermolecular hydrogen bonding. Our approach can be readily integrated into existing biomolecular NMR methodologies, as demonstrated here for $^1$H-detected relaxation-editing experiments with water suppression. This method provides access to nitrogen's picosecond-modulated quadrupolar interaction via single-scan proton detection in systems that would otherwise yield almost no detectable direct $^{14}$N signal even after averaging over thousands of transients.

physics.chem-ph↗

arXiv2025

Over four minutes relaxation of pyruvate using chemically and physically induced deceleration of relaxation

[1-13C]pyruvate is the most widely used tracer for hyperpolarized metabolic magnetic resonance imaging, with profound applications in tumor and inflammation diagnosis as well as treatment monitoring. The most fundamental hurdle to broader application, however, remains the rapid polarization relaxation and the associated signal loss. Here, we report a method to address this challenge. Studying the nuclear spin relaxation dispersion of [1-13C]pyruvate across magnetic fields from 8 {\mu}T to 9.4 T, as a function of additives, solvents, and preparation methods, allowed us to achieve relaxation times of up to four minutes. Such a long time could enable reliable quality control and nearly polarization loss-free transport, further boosting the power of hyperpolarized metabolic MRI.

physics.chem-ph↗

arXiv2024

Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of these signs is critical for effective psychological intervention. In this paper, we introduce two novel datasets from Chinese social media: SOS-HL-1K for suicidal risk classification and SocialCD-3K for cognitive distortions detection. The SOS-HL-1K dataset contained 1,249 posts and SocialCD-3K dataset was a multi-label classification dataset that containing 3,407 posts. We propose a comprehensive evaluation using two supervised learning methods and eight large language models (LLMs) on the proposed datasets. From the prompt engineering perspective, we experimented with two types of prompt strategies, including four zero-shot and five few-shot strategies. We also evaluated the performance of the LLMs after fine-tuning on the proposed tasks. The experimental results show that there is still a huge gap between LLMs relying only on prompt engineering and supervised learning. In the suicide classification task, this gap is 6.95% points in F1-score, while in the cognitive distortion task, the gap is even more pronounced, reaching 31.53% points in F1-score. However, after fine-tuning, this difference is significantly reduced. In the suicide and cognitive distortion classification tasks, the gap decreases to 4.31% and 3.14%, respectively. This research highlights the potential of LLMs in psychological contexts, but supervised learning remains necessary for more challenging tasks. All datasets and code are made available.

cs.CL↗

arXiv2023

Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects

The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications.

cs.AI↗