About

Ning Yu is a Professor of Applied Linguistics and Asian Studies at The Pennsylvania State University. Her academic work centers on cognitive linguistics, with a particular focus on the interplay between language, culture, and embodiment. She has made significant contributions to the study of metaphor, especially within the Chinese language, exploring how conceptual metaphors shape moral cognition, language, and cultural understanding. Ning Yu's research integrates cognitive semantic approaches to analyze linguistic phenomena such as metaphorical expressions, embodiment via body parts, and the cultural conceptualization of internal body organs across languages. She has authored and edited numerous influential books and articles that examine the cognitive and cultural dimensions of language, including works on the moral metaphor system, the Chinese heart in cognitive perspective, and the embodiment of language in cultural contexts. In addition to her research, Ning Yu holds several editorial positions, including co-editor of the book series "Cognitive Linguistic Studies in Cultural Contexts" and the International Journal of Chinese Linguistics, as well as membership on editorial boards of journals and book series related to cognitive semantics and cultural linguistics. Her scholarship is recognized internationally, with publications spanning major academic presses and journals, reflecting her expertise in cognitive linguistics, metaphor theory, and the linguistic embodiment of culture.

Research topics

• Sociology
• Computer Science
• Econometrics
• Demography
• Economics
• Biology
• Social psychology
• Internal medicine
• Medicine
• Demographic economics
• Pathology
• Environmental health
• Gerontology
• Geography
• Psychology
• Ecology

Selected publications

• Quantifying the Impact of Significant Wave Height on Mariculture Productivity: An Empirical Study in the Bohai and Yellow Seas

  Water · 2025-11-05

  articleOpen access

  Accurately understanding the impact of Significant Wave Height (SWH) on mariculture productivity is crucial for developing a sustainable blue economy and mitigating the effects of increasing marine extreme events under climate change. However, a significant research gap exists in macroscale empirical tools capable of quantifying the complex, non-linear, and spatially non-stationary relationships between SWH and mariculture yield. Addressing this, our study focused on the Bohai and Yellow Seas, a critical mariculture region in China. We developed five novel SWH indices (LSDI, MSDI, HSDI, RSI, NDSI) to statistically link SWH with the Unit Area Yield (UAY) using buoy-calibrated ERA5 reanalysis data and regional fishery statistics. Geographically Weighted Regression (GWR) was further employed to uncover the spatial heterogeneity of this relationship. Results demonstrated that the Normalized Difference SWH Index (NDSI) most effectively captured the SWH-UAY relationship (r = 0.61, R2 = 0.37), as its non-linear form integrates the positive effects of low SWH conditions and the negative effects of high SWH conditions. GWR analysis revealed significant spatial non-stationarity, with the SWH impact on yield being stronger in the eastern and southern open waters of the Yellow Sea and weaker in the northern semi-enclosed Bohai Sea. The index framework and spatial analysis method developed in this study provide a transferable tool for quantifying the impact of physical oceanographic processes on mariculture productivity at a macro scale, which can offer a scientific basis for climate-resilient mariculture zoning and adaptive management.

  PublisherOA PDFDOI

• Characterization of atmospheric cadmium deposition and its simulated combined toxicological effects on wheat (Triticum aestivum L.) seedlings with sulfamethoxazole in soil

  Environmental Pollution · 2025-11-20

  article
  PublisherDOI

• Exploiting Physics-Knowledge from Unlabeled Data to Enhance Battery Lifetime Prediction

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Mowing enhanced grassland belowground carbon stocks through species reordering

  Oikos · 2025-05-16

  article

  Grasslands are vital for global carbon (C) cycling, with belowground organs crucial for C sequestration. Mowing, a common practice in nutrient‐enriched grasslands, affects biodiversity positively but reduces aboveground biomass. Its impacts on belowground C stocks remain unresolved, posing knowledge gaps in understanding community assembly and belowground C responses due to limited species‐level belowground biomass data. With a long‐term field experiment in temperate steppe of northern China, we investigated the impacts of annual mowing on belowground biomass C stocks at both species and community levels under ambient and N addition conditions. Mowing significantly increased belowground C stocks but not aboveground C stocks. Mowing induced reordering of plant species in the community, and facilitated the growth of a sedge Carex duriuscula with the highest belowground:aboveground biomass ratio, which accounted for the positive responses of belowground C stocks. Under projected warmer and drier climate and more frequent fire across global grasslands, mowing would be one of the important nature‐based solutions to boost grassland C storage by moving C from vulnerable aboveground pools to the long‐lasting belowground pools.

  PublisherDOI

• VChain: Chain-of-Visual-Thought for Reasoning in Video Generation

  ArXiv.org · 2025-10-06

  preprintOpen access

  Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time visual-state adaptation of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.

  PublisherOA PDFDOI

• Developmental trajectory of voluntary alcohol consumption in adolescent mice using finite mixture modeling and Bayesian posterior probability analysis

  PLoS ONE · 2025-04-11

  articleOpen access1st authorCorresponding

  BACKGROUND: Alcohol use disorders (AUDs) pose a significant public health challenge, with adolescence representing a critical period of vulnerability for the initiation of alcohol consumption. Variability in drinking behaviors among individuals complicates efforts to characterize developmental trajectories, limiting our understanding of underlying biological mechanisms. OBJECTIVE: This study aimed to identify and characterize distinct patterns of voluntary alcohol consumption in adolescent mice, using advanced statistical methods to model behavioral heterogeneity. METHODS: Thirty-five male CD-1 outbred mice were monitored for alcohol consumption using a two-bottle free-choice paradigm from early adolescence to young adulthood (4-11 weeks of age). Finite mixture modeling, using the method implemented in the software SAS Proc Traj, was applied to categorize individual drinking behaviors into trajectory groups based on Bayesian Information Criterion (BIC) and Bayesian Posterior Probabilities (BPP). RESULTS: Three distinct drinking trajectory groups were identified: non-drinkers, late drinkers, and early drinkers. Non-drinkers exhibited consistently low alcohol consumption throughout the study, late drinkers showed a significant increase in alcohol intake during adolescence-to-adulthood transition, and early drinkers maintained high levels of consumption from the start. Notably, the late and early drinkers converged on similarly high consumption levels by the end of the observation period. These findings highlight the heterogeneity of drinking behaviors during adolescence and its developmental implications. CONCLUSIONS: This study demonstrates the utility of finite mixture modeling in characterizing developmental trajectories of voluntary alcohol consumption in adolescent mice. The identification of distinct behavioral trajectory patterns provides a foundation for future investigations into the genetic, molecular, and neural mechanisms underpinning susceptibility to alcohol use disorders.

  PublisherDOI

• Discordant Responses Between Imaging Examination and Surgical Pathology of Head and Heck Squamous Cell Carcinoma After Neoadjuvant Immunotherapy Combined With Chemotherapy

  World Journal of Oncology · 2025-01-14 · 7 citations

  articleOpen access1st authorCorresponding

  Background: We here investigated the value of imaging examination in evaluating tumor remission-based surgery in patients with head and neck squamous cell carcinoma (HNSCC), who had undergone neoadjuvant immunotherapy combined with chemotherapy (NICC). Methods: HNSCC patients who underwent NICC and surgery from May 2021 to September 2023 were retrospectively analyzed. All patients had to undergo imaging examination evaluation, including enhanced computed tomography (CT) and enhanced magnetic resonance (MR) imaging before and after NICC. Data related to clinical parameters, complete response of the primary site (PrCR), complete response of the primary site and the lymph node (PLCR), complete response of the lymph node (LCR), and tumor response (TR), were gathered. The paired Chi-square test and t-test were conducted to analyze the differences in responses between imaging examination and pathology. Binary logistic regression was applied to analyze the relevant clinical factors of differences in responses. Results: In total, data of 41 patients were included in this study. Significant discordant responses were observed between enhanced CT, magnetic resonance imaging (MRI), and pathology in PrCR (4.9%, 7.3% vs. 41.5%), LCR (12.2%, 7.3% vs. 53.7%), PLCR (0%, 0% vs. 31.7%), and TR (severe 29.3%,17.1% vs. 25.61%) (P < 0.05). Patients with hypopharyngeal cancer (odds ratio (OR): 7.04), oral cancer (OR: 3.64), higher neutrophil to lymphocyte ratio (NLR) (OR: 2.05), and earlier T stage (OR: 0.71) exhibited a larger response difference between enhanced CT and pathology. Patients with younger age (OR: 0.79) hypopharyngeal cancer (OR: 22.81), oral cancer (OR: 2.65), higher NLR (OR: 19.47), and earlier T stage (OR: 0.29) exhibited a larger response difference between enhanced MR and pathology. Conclusions: Discordant responses were noted between the imaging examination and surgical pathology of HNSCC after NICC. Hypopharyngeal cancer, higher NLR, and earlier T stage may predict a higher response difference.

  PublisherOA PDFDOI

• Long‐Term Dynamic Changes of Alanine Aminotransferase Levels Are Associated With Liver‐Related Events in Nucleos(t)ide Analogue‐Treated Chronic Hepatitis B Patients in China

  Alimentary Pharmacology & Therapeutics · 2025-05-19 · 4 citations

  article

  ABSTRACT Background The role of alanine aminotransferase (ALT) dynamics during nucleos(t)ide analogue (NA) therapy in chronic hepatitis B (CHB) is unclear. We aimed to evaluate the correlation between ALT dynamics and liver‐related events (LRE), and explore the optimal threshold of ALT during NA treatment. Methods We enrolled 18,129 NA‐treated patients, comprising 3104 patients from the Search‐B study (NCT02167503) and 15,025 patients from a real‐world cohort in Hong Kong. Latent‐class mixed model (LCMM) was adopted to identify trajectory patterns of ALT during treatment. ALT value at the 95 th percentile of the trajectory group with the lowest LRE risk was obtained as the optimal threshold. Results During a median follow‐up of 53.3 months, 1164 patients developed LRE with a 7‐year cumulative incidence of 9.9%. In the Search‐B cohort, LCMM recognised 3 trajectory groups with progressively increasing ALT levels, which were positively associated with LRE risk. Subsequently, the optimal thresholds for ALT were obtained as 23 U/L for men and 16 U/L for women. The 7‐year cumulative incidence of LRE was 5.5% for ALT ≤ 23 or 16 U/L, significantly lower than that for ALT > 23 or 16 U/L but ≤ 40 U/L (10.8%; aHR = 2.0, p < 0.001), and ALT > 40 U/L (15.1%; aHR = 3.4, p < 0.001). Similarly, in the Hong Kong cohort, ALT > 23 or 16 U/L but < 40 U/L and ALT > 40 U/L also increased the LRE risk, with aHRs of 2.0 ( p = 0.003) and 6.1 ( p < 0.001), respectively. Conclusion On‐treatment ALT levels were significantly correlated with the prognosis of CHB. ALT ≤ 23 U/L for men and ≤ 16 U/L for women were identified as the optimal thresholds during NA treatment, suggesting that CHB patients should strive for a lower ALT level beyond the traditional normal range.

  PublisherDOI

• Highway lane line edge extraction by incorporating improved <i>α</i>-shape and cubic B-spline curve fitting algorithms

  Engineering Research Express · 2025-07-28 · 1 citations

  article1st authorCorresponding

  Abstract With the rapid development of autonomous driving technology and high-accuracy maps, accurate detection of lane line edges has become critical. Traditional methods, especially those relying on visual sensors, are often limited by light variations and environmental factors. Mobile laser scanning (MLS) systems is capable of generating high-accuracy 3D point cloud, but it still faces challenges such as incomplete information extraction and lack of precision in the process of extracting lane line edges. To solve these problems, this study proposes a highway lane line edge extraction method that combines an improved α -shape with a cubic B-spline curve fitting algorithm. Firstly, statistical filtering and radius filtering algorithms are used to remove isolated points from the point cloud and obtain pure highway laser point cloud. Based on the geometric properties and spatial proximity of the point cloud, adaptive voxel filtering is used for downsampling, and a region-growing algorithm is used to accurately extract the road point cloud. Secondly, based on the reflection intensity information of the point cloud, the optimal threshold is automatically determined with the help of the OTSU (Maximum Between-Class Variance Method) algorithm to separate the lane line point cloud from the road surface point cloud; and the shape parameters are adjusted based on the improved α -shape algorithm to control the completeness of the lane edge extraction, and to accurately extract the edge contour of the lane line. Finally, the improved cubic B-spline curve fitting method is used to approximate the generated lane edge fitting curves, and the vectorized extraction of the curves is achieved by introducing specific constraints. In this paper, point cloud experiments are conducted on both straight and curved sections of the highway to verify the reliability of the algorithm. Experimental results show that the root mean square error of lane line edge extraction and fitting is 7.8 mm in the curved section and 19 mm in the straight section, demonstrating that the proposed method is able to accurately extract the complete lane line edges of highways.

  PublisherDOI

• An exploration-enhanced hybrid algorithm based on regularity evolution for multi-objective multi-UAV 3-D path planning

  Complex & Intelligent Systems · 2025-03-24 · 5 citations

  articleOpen access

  Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantial challenges within the multi-objective optimization community. Traditional modeling approaches primarily aim to minimize path length and collision risks, often overlooking the need for a quantitative assessment of communication quality among UAVs. This neglect causes an inadequate representation of their true cooperative capabilities. In addition, there is difficulty in achieving an optimal balance between convergence, diversity, and feasibility. Therefore, this study introduces a bi-objective, three-dimensional path planning model specifically designed for UAVs. This model features an objective function that quantitatively evaluates inter-UAV communication quality throughout their flights. To solve this problem, this study proposes the dual-population regularity evolution algorithm (DPREA), which incorporates an auto-switching regularity evolutionary reproduction operator known as autoRE. It conducts extensive experiments across six testing suites and three path-planning simulation cases to validate the effectiveness of DPREA. The simulation results showed that its performance in addressing constrained multi-objective problems is significantly superior or at least comparable to that of state-of-the-art algorithms in most instances.

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Frequent coauthors

• Peter T. Donnan

  University of Dundee

  19 shared

• Graham Leese

  Ninewells Hospital

  16 shared

• Riyaz Patel

  Barts Health NHS Trust

  10 shared

• Cong Ding

  Chinese Academy of Sciences

  8 shared

• Bin Li

  Shanghai Skin Disease Hospital

  8 shared

• Xiaosa Liang

  8 shared

• Spiros Denaxas

  University College London

  8 shared

• Harry Hemingway

  University College London

  8 shared

Education

• PhD, Program in Second Language Aquisition and Teaching

  The University of Arizona

  1996

Awards & honors

• Co-editor, book series “Cognitive Linguistic Studies in Cult…
• Co-editor, International Journal of Chinese Linguistics
• Review Editor, Cognitive Semantics
• Editorial board member, International Journal of Language an…
• Editorial board member, the scholarly book series Cultural L…