Osteoarthritis （OA） is the most common degenerative joint disease and a major cause of pain and disability in adult individuals. The etiology of OA includes joint injury, obesity, aging, and heredity. However, the d...Osteoarthritis （OA） is the most common degenerative joint disease and a major cause of pain and disability in adult individuals. The etiology of OA includes joint injury, obesity, aging, and heredity. However, the detailed molecular mechanisms of OA initiation and progression remain poorly understood and, currently, there are no interventions available to restore degraded cartilage or decelerate disease progression. The diathrodial joint is a complicated organ and its function is to bear weight, perform physical activity and exhibit a joint-specific range of motion during movement. During OA development, the entire joint organ is affected, including articular cartilage, subchondral bone, synovial tissue and meniscus. A full understanding of the pathological mechanism of OA development relies on the discovery of the interplaying mechanisms among different OA symptoms, including articular cartilage degradation, osteophyte formation, subchondral sclerosis and synovial hyperplasia, and the signaling pathway（s） controlling these pathological processes.展开更多
Quantum algorithms have demonstrated provable speedups over classical counterparts,yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge.In this work,we de...Quantum algorithms have demonstrated provable speedups over classical counterparts,yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge.In this work,we decode the quantum search advantage by investigating the critical role of quantum state properties in random-walk-based algorithms.We propose three distinct variants of quantum random-walk search algorithms and derive exact analytical expressions for their success probabilities.These probabilities are fundamentally determined by specific initial state properties:the coherence fraction governs the first algorithm’s performance,while entanglement and coherence dominate the outcomes of the second and third algorithms,respectively.We show that increased coherence fraction enhances success probability,but greater entanglement and coherence reduce it in the latter two cases.These findings reveal fundamental insights into harnessing quantum properties for advantage and guide algorithm design.Our searches achieve Grover-like speedups and show significant potential for quantum-enhanced machine learning.展开更多
Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales.However,monitoring water bodies with highly seasona...Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales.However,monitoring water bodies with highly seasonal hydrological variability,particularly using medium-resolution satellite imagery such as Landsat 4-9,presents substantial challenges.This study introduces the Normalized Difference Water Fraction Index(NDWFI)based on spectral mixture analysis(SMA)to improve the detection of subtle and dynamically changing water bodies.First,the effectiveness of NDWFI is rigorously assessed across four challenging sites.The findings reveal that NDWFI achieves an average overall accuracy(OA)of 98.2%in water extraction across a range of water-covered scenarios,surpassing conventional water indices.Subsequently,using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine(GEE)platform,this study generates a high-resolution surface water(SW)map for Jiangsu Province,China,exhibiting an impressive OA of 95.91%±0.23%.We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps.This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring,contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.展开更多
基金supported by NIH grants AR055915 and AR054465 to DC
文摘Osteoarthritis （OA） is the most common degenerative joint disease and a major cause of pain and disability in adult individuals. The etiology of OA includes joint injury, obesity, aging, and heredity. However, the detailed molecular mechanisms of OA initiation and progression remain poorly understood and, currently, there are no interventions available to restore degraded cartilage or decelerate disease progression. The diathrodial joint is a complicated organ and its function is to bear weight, perform physical activity and exhibit a joint-specific range of motion during movement. During OA development, the entire joint organ is affected, including articular cartilage, subchondral bone, synovial tissue and meniscus. A full understanding of the pathological mechanism of OA development relies on the discovery of the interplaying mechanisms among different OA symptoms, including articular cartilage degradation, osteophyte formation, subchondral sclerosis and synovial hyperplasia, and the signaling pathway（s） controlling these pathological processes.
基金supported by the Fundamental Research Funds for the Central Universities,the National Natural Science Foundation of China(Grant Nos.12371132,12075159,12171044,12071179,and 12405006)the specific research fund of the Innovation Platform for Academicians of Hainan Province.
文摘Quantum algorithms have demonstrated provable speedups over classical counterparts,yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge.In this work,we decode the quantum search advantage by investigating the critical role of quantum state properties in random-walk-based algorithms.We propose three distinct variants of quantum random-walk search algorithms and derive exact analytical expressions for their success probabilities.These probabilities are fundamentally determined by specific initial state properties:the coherence fraction governs the first algorithm’s performance,while entanglement and coherence dominate the outcomes of the second and third algorithms,respectively.We show that increased coherence fraction enhances success probability,but greater entanglement and coherence reduce it in the latter two cases.These findings reveal fundamental insights into harnessing quantum properties for advantage and guide algorithm design.Our searches achieve Grover-like speedups and show significant potential for quantum-enhanced machine learning.
基金National Science Foundation for Distinguished Young Scholars of China under grant 42225107National Key Research and Development Program under grant 2022YFB3903402Natural Science Foundation of China under grants 61976234,42171409,and 42171410.
文摘Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales.However,monitoring water bodies with highly seasonal hydrological variability,particularly using medium-resolution satellite imagery such as Landsat 4-9,presents substantial challenges.This study introduces the Normalized Difference Water Fraction Index(NDWFI)based on spectral mixture analysis(SMA)to improve the detection of subtle and dynamically changing water bodies.First,the effectiveness of NDWFI is rigorously assessed across four challenging sites.The findings reveal that NDWFI achieves an average overall accuracy(OA)of 98.2%in water extraction across a range of water-covered scenarios,surpassing conventional water indices.Subsequently,using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine(GEE)platform,this study generates a high-resolution surface water(SW)map for Jiangsu Province,China,exhibiting an impressive OA of 95.91%±0.23%.We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps.This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring,contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.
