Science Bulletin最新文献

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models (LLMs) provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: ChatGPT-3.5, ChatGPT-4.0, Google Bard, LlaMA-7B, LlaMA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, MedGPT, HuatuoGPT, and Chinese LlaMA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China (NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians (PCPs) without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that ChatGPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, LlaMA-7B, and LlaMA2-7B. For the NCE-CPFC examination, ChatGPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, MedGPT, and ChatGPT-3.5 successfully passed, whereas LlaMA2-7B, HuatuoGPT, Chinese LLaMA2-7B, and LlaMA-7B failed. ChatGPT-4.0 (84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination (improving by 1 %-6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal tropical atmospheric variability with far-reaching influences on global weather systems. During boreal winter, the MJO typically exhibits regular eastward propagation, whereas in summer, it displays more complex propagation characteristics, such as frequent stalling over the Indian Ocean. Here we show a threefold increase in the MJO residence time over the Indian Ocean during the Asian summer monsoon in recent four decades. This drastic increase is closely associated with the rapid warming of the Indian Ocean, which contributes to increased extreme climate events across Indian Ocean rim countries. This peculiar warming pattern is projected to persist under anthropogenic warming, potentially facilitating prolonged MJO activity over the Indian Ocean with higher climatic disaster risks in the future.

Photoactivable fluorescent probes (photocages) are powerful tools for studying biological processes in living cells. We report a novel class of photodegradable thioketals that serve as photo-responsive elements and apply them to xanthene dyes to design photocages for live cell imaging. Compared with traditional thioketals, these compounds demonstrate the ability to undergo deprotection upon exposure to ultraviolet-visible light, independent of photosensitizers or external oxidants and relying solely on dissolved oxygen within the solvent. This photoreaction results in a remarkable 68-fold increase in fluorescence intensity. We verify that the uncaged product is the corresponding ketone, with high-performance liquid chromatography analysis, which indicates a yield of up to 80%. Furthermore, we extend this modification strategy to xanthene dyes substituted with various heteroatoms and confirm the universal applicability of this photoactivable strategy. These dyes exhibit good stability against reducing agents and metal ions, with carbon and silicon xanthene photocages also demonstrating commendable dark stability against reactive oxygen species. We apply these photocages for bioimaging and further modify them for selective labeling, activation, and imaging of specific organelles and intracellular proteins within living cells. This modification strategy offers high spatiotemporal selectivity and holds promise as a powerful tool for advanced biological studies.

Asian monsoon systems influence billions of people and understanding past monsoon variability and dynamics is instructive in predicting future trajectories. Recent studies have demonstrated that insolation, plus interrelated changes in ice-sheet extent and volume, particularly in the Northern Hemisphere (NH), have controlled the magnitude of millennial-scale East Asian summer monsoon (EASM) variability during the Quaternary. However, it is unclear how the EASM varied at both orbital and millennial timescales, and whether orbital-scale variations impacted millennial-scale variations, during intervals lacking large permanent NH ice sheets at times when CO₂ levels were close to the present-day value of approximately 400 ppm. Here, we present high-resolution (∼1-kyr) dry-wet variation records from late Miocene eolian sediments in the Jianzha Basin of the northeastern Tibetan Plateau, spanning an interval when CO₂ levels were persistently close to the modern level and the NH lacked large permanent ice sheets. Our results reveal orbital-scale forcing interwoven with millennial cycles. In contrast to dominant precession and eccentricity forcing of the EASM at orbital scales, the amplitude variations of millennial-scale EASM cycles exhibit strong obliquity (and its modulating cycle) forcing and weak precession forcing. This pattern is different from the pattern observed in either the Quaternary or early Miocene, which we attribute to the effects of different boundary conditions.