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6,708 results on '"Yan, Ning"'

1. Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning

Author

Zhang, Tao, Yan, Ning, Mortazavi, Masood, Nguyen, Hoang H., Deng, Zhongfen, and Yu, Philip S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning capability on many NLP tasks. Recasting an NLP task into a text-to-text generation task is a common practice so that generative LLMs can be prompted to resolve it. However, performing document-level relation extraction (DocRE) tasks with generative LLM models is still challenging due to the structured output format of DocRE, which complicates the conversion to plain text. Limited information available in few-shot samples and prompt instructions induce further difficulties and challenges in relation extraction for mentioned entities in a document. In this paper, we represent the structured output as a graph-style triplet rather than natural language expressions and leverage generative LLMs for the DocRE task. Our approach, the Graph-DPEP framework is grounded in the reasoning behind triplet explanation thoughts presented in natural language. In this framework, we first introduce a ``decomposed-plug" method for performing the generation from LLMs over prompts with type-space decomposition to alleviate the burden of distinguishing all relation types. Second, we employ a verifier for calibrating the generation and identifying overlooked query entity pairs. Third, we develop "ensemble-play", reapplying generation on the entire type list by leveraging the reasoning thoughts embedded in a sub-graph associated with the missing query pair to address the missingness issue. Through extensive comparisons with existing prompt techniques and alternative Language Models (LLMs), our framework demonstrates superior performance on publicly available benchmarks in experiments.

Published
2024

2. Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism

Author

Tang, Yimin, Xu, Yurong, Yan, Ning, and Mortazavi, Masood

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Transformers have a quadratic scaling of computational complexity with input size, which limits the input context window size of large language models (LLMs) in both training and inference. Meanwhile, retrieval-augmented generation (RAG) besed models can better handle longer contexts by using a retrieval system to filter out unnecessary information. However, most RAG methods only perform retrieval based on the initial query, which may not work well with complex questions that require deeper reasoning. We introduce a novel approach, Inner Loop Memory Augmented Tree Retrieval (ILM-TR), involving inner-loop queries, based not only on the query question itself but also on intermediate findings. At inference time, our model retrieves information from the RAG system, integrating data from lengthy documents at various levels of abstraction. Based on the information retrieved, the LLM generates texts stored in an area named Short-Term Memory (STM) which is then used to formulate the next query. This retrieval process is repeated until the text in STM converged. Our experiments demonstrate that retrieval with STM offers improvements over traditional retrieval-augmented LLMs, particularly in long context tests such as Multi-Needle In A Haystack (M-NIAH) and BABILong.

Published
2024

3. Multi-modal Fusion based Q-distribution Prediction for Controlled Nuclear Fusion

Author

Wang, Shiao, Wang, Yifeng, Ma, Qingchuan, Wang, Xiao, Yan, Ning, Yang, Qingquan, Xu, Guosheng, and Tang, Jin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Q-distribution prediction is a crucial research direction in controlled nuclear fusion, with deep learning emerging as a key approach to solving prediction challenges. In this paper, we leverage deep learning techniques to tackle the complexities of Q-distribution prediction. Specifically, we explore multimodal fusion methods in computer vision, integrating 2D line image data with the original 1D data to form a bimodal input. Additionally, we employ the Transformer's attention mechanism for feature extraction and the interactive fusion of bimodal information. Extensive experiments validate the effectiveness of our approach, significantly reducing prediction errors in Q-distribution.

Published
2024

4. A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports

Author

Sushil, Madhumita, Zack, Travis, Mandair, Divneet, Zheng, Zhiwei, Wali, Ahmed, Yu, Yan-Ning, Quan, Yuwei, Lituiev, Dmytro, and Butte, Atul J

Subjects
Information and Computing Sciences, Machine Learning, Bioengineering, Cancer, Women's Health, Networking and Information Technology R&D (NITRD), Breast Cancer, 2.5 Research design and methodologies (aetiology), Humans, Breast Neoplasms, Female, Supervised Machine Learning, Natural Language Processing, Datasets as Topic, Electronic Health Records, Data Mining, electronic health records, large language models, breast cancer, pathology, natural language processing, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences
Abstract

ObjectiveAlthough supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs could reduce the need for large-scale data annotations.Materials and methodsWe curated a dataset of 769 breast cancer pathology reports, manually labeled with 12 categories, to compare zero-shot classification capability of the following LLMs: GPT-4, GPT-3.5, Starling, and ClinicalCamel, with task-specific supervised classification performance of 3 models: random forests, long short-term memory networks with attention (LSTM-Att), and the UCSF-BERT model.ResultsAcross all 12 tasks, the GPT-4 model performed either significantly better than or as well as the best supervised model, LSTM-Att (average macro F1-score of 0.86 vs 0.75), with advantage on tasks with high label imbalance. Other LLMs demonstrated poor performance. Frequent GPT-4 error categories included incorrect inferences from multiple samples and from history, and complex task design, and several LSTM-Att errors were related to poor generalization to the test set.DiscussionOn tasks where large annotated datasets cannot be easily collected, LLMs can reduce the burden of data labeling. However, if the use of LLMs is prohibitive, the use of simpler models with large annotated datasets can provide comparable results.ConclusionsGPT-4 demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for large annotated datasets. This may increase the utilization of NLP-based variables and outcomes in clinical studies.

Published
2024

5. On the Cyclostationary Linear Inverse Models: A Mathematical Insight and Implication

Author

Lien, Justin, Kuo, Yan-Ning, and Ando, Hiroyasu

Subjects
Mathematics - Numerical Analysis
Abstract

Cyclostationary linear inverse models (CS-LIMs), generalized versions of the classical (stationary) LIM, are advanced data-driven techniques for extracting the first-order time-dependent dynamics and random forcing relevant information from complex non-linear stochastic processes. Though CS-LIMs lead to a breakthrough in climate sciences, their mathematical background and properties are worth further exploration. This study focuses on the mathematical perspective of CS-LIMs and introduces two variants: e-CS-LIM and l-CS-LIM. The former refines the original CS-LIM using the interval-wise linear Markov approximation, while the latter serves as an analytic inverse model for the linear periodic stochastic systems. Although relying on approximation, e-CS-LIM converges to l-CS-LIM under specific conditions and shows noise-robust performance. Numerical experiments demonstrate that each CS-LIM reveals the temporal structure of the system. The e-CS-LIM optimizes the original model for better dynamics performance, while l-CS-LIM excels in diffusion estimation due to reduced approximation reliance. Moreover, CS-LIMs are applied to real-world ENSO data, yielding a consistent result aligning with observations and current ENSO understanding.

Published
2024

6. Fusion of regional and sparse attention in Vision Transformers

Author

Ibtehaz, Nabil, Yan, Ning, Mortazavi, Masood, and Kihara, Daisuke

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Modern vision transformers leverage visually inspired local interaction between pixels through attention computed within window or grid regions, in contrast to the global attention employed in the original ViT. Regional attention restricts pixel interactions within specific regions, while sparse attention disperses them across sparse grids. These differing approaches pose a challenge between maintaining hierarchical relationships vs. capturing a global context. In this study, drawing inspiration from atrous convolution, we propose Atrous Attention, a blend of regional and sparse attention that dynamically integrates both local and global information while preserving hierarchical structures. Based on this, we introduce a versatile, hybrid vision transformer backbone called ACC-ViT, tailored for standard vision tasks. Our compact model achieves approximately 84% accuracy on ImageNet-1K with fewer than 28.5 million parameters, outperforming the state-of-the-art MaxViT by 0.42% while requiring 8.4% fewer parameters., Comment: Accepted as a Workshop Paper at T4V@CVPR2024. arXiv admin note: substantial text overlap with arXiv:2403.04200

Published
2024

7. ACC-ViT : Atrous Convolution's Comeback in Vision Transformers

Author

Ibtehaz, Nabil, Yan, Ning, Mortazavi, Masood, and Kihara, Daisuke

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Transformers have elevated to the state-of-the-art vision architectures through innovations in attention mechanism inspired from visual perception. At present two classes of attentions prevail in vision transformers, regional and sparse attention. The former bounds the pixel interactions within a region; the latter spreads them across sparse grids. The opposing natures of them have resulted in a dilemma between either preserving hierarchical relation or attaining a global context. In this work, taking inspiration from atrous convolution, we introduce Atrous Attention, a fusion of regional and sparse attention, which can adaptively consolidate both local and global information, while maintaining hierarchical relations. As a further tribute to atrous convolution, we redesign the ubiquitous inverted residual convolution blocks with atrous convolution. Finally, we propose a generalized, hybrid vision transformer backbone, named ACC-ViT, following conventional practices for standard vision tasks. Our tiny version model achieves $\sim 84 \%$ accuracy on ImageNet-1K, with less than $28.5$ million parameters, which is $0.42\%$ improvement over state-of-the-art MaxViT while having $8.4\%$ less parameters. In addition, we have investigated the efficacy of ACC-ViT backbone under different evaluation settings, such as finetuning, linear probing, and zero-shot learning on tasks involving medical image analysis, object detection, and language-image contrastive learning. ACC-ViT is therefore a strong vision backbone, which is also competitive in mobile-scale versions, ideal for niche applications with small datasets.

Published
2024

9. Colored-LIM: A Data-Driven Method for Studying Dynamical Systems with Temporally Correlated Stochasticity

Author

Lien, Justin, Kuo, Yan-Ning, Ando, Hiroyasu, and Kido, Shoichiro

Subjects
Mathematics - Numerical Analysis, 76-10, 86A10, 86A22
Abstract

In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The Linear Inverse Model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. In this study, we introduce a new variant of LIM, called Colored-LIM, which models stochasticity using Ornstein-Uhlenbeck colored noise. Despite the non-trivial correlation between observable and colored noise, we show that Colored-LIM unveils the desired information merely from the correlation function of the observable. Therefore, this approach not only accounts for the memory effects of environmental noise, traditionally represented by time-uncorrelated white noise in the Classical LIM framework, but does so using the same observation dataset without requiring additional data. Furthermore, we show that Colored-LIM does not reduce to Classical LIM in the white noise limit, underscoring the importance of temporal dependencies in stochastic systems. In this paper, we rigorously develop the Colored-LIM, explore its connections with the Classical LIM and Dynamic Mode Decomposition, and validate its effectiveness on both ideal linear and nonlinear systems. In addition, we illustrate the potential applications and implications of Colored-LIM for real-world problems, including the El Ni\~no-Southern Oscillation and the electricity network of Tohoku University., Comment: 16 pages

Published
2024

10. A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification

Author

Sushil, Madhumita, Zack, Travis, Mandair, Divneet, Zheng, Zhiwei, Wali, Ahmed, Yu, Yan-Ning, Quan, Yuwei, and Butte, Atul J.

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Although supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs can reduce the need for large-scale data annotations. We curated a manually-labeled dataset of 769 breast cancer pathology reports, labeled with 13 categories, to compare zero-shot classification capability of the GPT-4 model and the GPT-3.5 model with supervised classification performance of three model architectures: random forests classifier, long short-term memory networks with attention (LSTM-Att), and the UCSF-BERT model. Across all 13 tasks, the GPT-4 model performed either significantly better than or as well as the best supervised model, the LSTM-Att model (average macro F1 score of 0.83 vs. 0.75). On tasks with high imbalance between labels, the differences were more prominent. Frequent sources of GPT-4 errors included inferences from multiple samples and complex task design. On complex tasks where large annotated datasets cannot be easily collected, LLMs can reduce the burden of large-scale data labeling. However, if the use of LLMs is prohibitive, the use of simpler supervised models with large annotated datasets can provide comparable results. LLMs demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for curating large annotated datasets. This may result in an increase in the utilization of NLP-based variables and outcomes in observational clinical studies.

Published
2024

15. HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks

Author

Ma, Yihong, Yan, Ning, Li, Jiayu, Mortazavi, Masood, and Chawla, Nitesh V.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Graphs have emerged as a natural choice to represent and analyze the intricate patterns and rich information of the Web, enabling applications such as online page classification and social recommendation. The prevailing "pre-train, fine-tune" paradigm has been widely adopted in graph machine learning tasks, particularly in scenarios with limited labeled nodes. However, this approach often exhibits a misalignment between the training objectives of pretext tasks and those of downstream tasks. This gap can result in the "negative transfer" problem, wherein the knowledge gained from pre-training adversely affects performance in the downstream tasks. The surge in prompt-based learning within Natural Language Processing (NLP) suggests the potential of adapting a "pre-train, prompt" paradigm to graphs as an alternative. However, existing graph prompting techniques are tailored to homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To bridge this gap, we propose HetGPT, a general post-training prompting framework to improve the predictive performance of pre-trained heterogeneous graph neural networks (HGNNs). The key is the design of a novel prompting function that integrates a virtual class prompt and a heterogeneous feature prompt, with the aim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPT introduces a multi-view neighborhood aggregation mechanism, capturing the complex neighborhood structure in heterogeneous graphs. Extensive experiments on three benchmark datasets demonstrate HetGPT's capability to enhance the performance of state-of-the-art HGNNs on semi-supervised node classification., Comment: Accepted to WWW 2024 as research paper

Published
2023

16. Research on the Impact of Executive Shareholding on New Investment in Enterprises Based on Multivariable Linear Regression Model

Author

Zhou, Shanyi, Yan, Ning, Li, Zhijun, Geng, Mo, Zhang, Xulong, Si, Hongbiao, Tang, Lihua, Sun, Wenyuan, Zhang, Longda, and Cao, Yi

Subjects
Mathematics - Numerical Analysis, Economics - General Economics
Abstract

Based on principal-agent theory and optimal contract theory, companies use the method of increasing executives' shareholding to stimulate collaborative innovation. However, from the aspect of agency costs between management and shareholders (i.e. the first type) and between major shareholders and minority shareholders (i.e. the second type), the interests of management, shareholders and creditors will be unbalanced with the change of the marginal utility of executive equity incentives.In order to establish the correlation between the proportion of shares held by executives and investments in corporate innovation, we have chosen a range of publicly listed companies within China's A-share market as the focus of our study. Employing a multi-variable linear regression model, we aim to analyze this relationship thoroughly.The following models were developed: (1) the impact model of executive shareholding on corporate innovation investment; (2) the impact model of executive shareholding on two types of agency costs; (3)The model is employed to examine the mediating influence of the two categories of agency costs. Following both correlation and regression analyses, the findings confirm a meaningful and positive correlation between executives' shareholding and the augmentation of corporate innovation investments. Additionally, the results indicate that executive shareholding contributes to the reduction of the first type of agency cost, thereby fostering corporate innovation investment. However, simultaneously, it leads to an escalation in the second type of agency cost, thus impeding corporate innovation investment., Comment: Accepted by the 7th APWeb-WAIM International Joint Conference on Web and Big Data. (APWeb 2023)

Published
2023

17. MRI grading for informed clinical decision-making in Peutz–Jeghers syndrome patients with cervical lesions

Author

Anqi Jiang, Yiqing Chen, Yan Ning, Bing Yu, Hui Wang, Fenghua Ma, Congjian Xu, and Yu Kang

Subjects
Cervical lesions, Gastric-type endocervical adenocarcinoma, Lobular endocervical glandular hyperplasia, Magnetic resonance imaging, Peutz–Jeghers syndrome, Medicine, Science
Abstract

Abstract The preoperative diagnosis and management of Peutz–Jeghers syndrome (PJS) patients with cervical lesions remain problematic. This study analysed the associations between pathological types of cervical lesions in PJS patients and their MRI features. A total of 34 PJS patients were included and two experienced radiologists reviewed the MRIs independently. Based on the pathological diagnosis, the patients were categorized into four groups: normal (n = 4), lobular endocervical glandular hyperplasia (LEGH, n = 11), atypical LEGH (aLEGH, n = 8), and gastric-type endocervical adenocarcinoma (G-EAC, n = 11). By observing the MRI features, we found statistically significant differences in the extent of lesions (P = 0 .001), distribution of microcysts (P = 0 .001), proportion of microcysts (P

Published
2024
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18. The efficacy of socially assistive robots in improving children’s pain and negative affectivity during needle-based invasive treatment: A systematic review and meta-analysis

Author

Xin-Yun Pan, Xuan-Yi Bi, Yan-Ning Nong, Xu-Chun Ye, Yan Yan, Jing Shang, Yi-Min Zhou, and Yu-Zhe Yao

Subjects
Pediatrics, Acute pain, Emotions, Meta-analysis, Systematic review, Robotics, RJ1-570
Abstract

Abstract Background The ability of socially assistive robots (SARs) to treat dementia and Alzheimer’s disease has been verified. Currently, to increase the range of their application, there is an increasing amount of interest in using SARs to relieve pain and negative emotions among children in routine medical settings. However, there is little consensus regarding the use of these robots. Objective This study aimed to evaluate the effect of SARs on pain and negative affectivity among children undergoing invasive needle-based procedures. Design This study was a systematic review and meta-analysis of randomized controlled trials that was conducted in accordance with the Cochrane Handbook guidelines. Methods The PubMed, CINAHL, Web of Science, Cochrane Library, Embase, CNKI, and WanFang databases were searched from inception to January 2024 to identify relevant randomized controlled trials (RCTs). We used the Cochrane Risk of Bias tool 2.0 (RoB2.0) to assess the risk of bias among the included studies, and we used RevMan 5.4 software to conduct the meta-analysis. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework was used to assess the quality of the evidence. Results Ten RCTs involving 815 pediatric subjects were selected for this review and reported outcomes related to pain and emotions during IV placement, port needle insertion, flu vaccination, blood sampling, and dental treatment. Children undergoing needle-related procedures with SARs reported less anxiety (SMD= -0.36; 95% CI= -0.64, -0.09) and fewer distressed avoidance behaviors (SMD= -0.67; 95% CI= -1.04, -0.30) than did those receiving typical care. There were nonsignificant differences between these groups in terms of in pain (SMD = -0.02; 95% CI = − 0.81, 0.78) and fear (SMD = 0.38; 95% CI= -0.06, 0.82). The results of exploratory subgroup analyses revealed no statistically significant differences based on the intervention type of robots or anesthetic use. Conclusions The use of SARs is a promising intervention method for alleviating anxiety and distress among children undergoing needle-related procedures. However, additional high-quality randomized controlled trials are needed to further validate these conclusions. Trial registration The protocol of this study has been registered in the database PROSPERO (registration ID: CRD42023413279).

Published
2024
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19. How fluctuation intensity flux drives SOL expansion

Author

Li, Nami, Xu, Xueqiao, Diamond, Patrick H, Zhang, Tao, Liu, Xiang, Wang, Yifeng, Yan, Ning, and Xu, Guo Sheng

Subjects
Atomic, Molecular, Nuclear, Particle and Plasma Physics, Fluids & Plasmas, Nuclear and plasma physics
Abstract

Abstract: Predictions of heat load widths λ_q based on particle orbits alone are very pessimistic. This paper shows that pedestal peeling-ballooning (P-B) MHD turbulence broadens the stable SOL by the transport, or spreading, of fluctuation energy from the pedestal. λ_q is seen to increase with Γ_ε, the fluctuation energy density flux. We elucidate the fundamental physics of the spreading process. Γ_ε increases with pressure fluctuation correlation length. P-B turbulence is seen to be especially effective at spreading, on account of its large effective mixing length. Spreading is shown to be a multiscale process, which is enhanced by the synergy of large and small-scale modes. Pressure fluctuation skewness correlates well with the spreading flux – with the zero crossing of skewness and Γ_ε spatially coincident – suggesting the role of coherent fluctuation structures and the presence of intermittency in λ_q broadening. λ_q~B_p^(-1) scaling persists for the broadened SOL. We show that the spreading flux increases for increasing pedestal pressure gradient ∇P_0 and for decreasing pedestal collisionality υ_ped^*. This trend is due to the dominance of peeling modes for large ∇P_0 and low υ_ped^*. Ultimately, we see that a state of weak MHD turbulence, as for small ELMs, is very attractive for heat load management. Our findings have transformative implications for future fusion reactor designs and call for experimental investigations to validate the observed trends.

Published
2023

33. The association between preoperative serum cholinesterase and all-cause mortality in geriatric patients with hip fractures: a cohort study of 2387 patients

Author

Yan-Ning Zhang, Peng Xiao, and Bin-Fei Zhang

Subjects
Cholinesterase, Mortality, Elderly patients, Hip fracture, Regression, Surgery, RD1-811
Abstract

Abstract Objective This study is to evaluate the association between preoperative cholinesterase levels and all-cause mortality in geriatric hip fractures. Methods Elderly patients with hip fractures were screened between Jan 2015 and Sep 2019. Demographic and clinical characteristics of patients were collected. Linear and nonlinear multivariate Cox regression models were used to identify the association between preoperative cholinesterase levels and mortality in these patients. Analyses were performed using EmpowerStats and the R software. Results Two thousand three hundred eighty-seven patients were included in this study. The mean follow-up period was 37.64 months. Seven hundred eighty-seven (33.0%) patients died due to all-cause mortality. Preoperative cholinesterase levels were 5910 ± 1700 U/L. Linear multivariate Cox regression models showed that preoperative cholinesterase level was associated with mortality (HR = 0.83, 95% CI: 0.78–0.88), P 5940 U/L, the mortality was no longer decreased with the rise of cholinesterase (HR = 1.01, 95%CI: 0.91–1.11, P = 0.9157). We found the nonlinear association was very stable in the propensity score-matching sensitive analysis. Conclusions Preoperative cholinesterase levels were nonlinearly associated with mortality in elderly hip fractures, and cholinesterase was a risk indicator of all-cause mortality. Trial registration This study is registered on the website of the Chinese Clinical Trial Registry (ChiCTR: ChiCTR2200057323) (08/03/2022).

Published
2024
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34. Individualized responses to acupuncture in premature ovarian insufficiency: A study protocol for a nested case-control trial with transcriptome analysis

Author

Shiyu Feng, Yu Luo, Yan Chen, Haimin Zhu, Tianqi Zhao, Fei Ma, Yanting Lin, Yan Ning, and Jiaman Wu

Subjects
Acupuncture, Premature ovarian insufficiency, Individual curative effect, Transcriptome, Protocol, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Background: Premature ovarian insufficiency (POI), a modifiable cause of infertility with substantial implications for women's well-being, prompts the exploration of efficacious adjunctive treatments. Acupuncture emerges as a promising therapeutic avenue; however, the nuanced effects of acupuncture in POI warrant more comprehensive investigation. The intricate mechanisms dictating individualized responses remain elusive. This trial seeks to assess the effectiveness of acupuncture as an adjunctive treatment for POI, concurrently delving into the impact of transcriptome analysis on peripheral blood to unravel the underpinnings of these individual variations. The overarching objective is to enrich our comprehension of acupuncture's therapeutic potential in the context of POI, with a view to advancing holistic patient care. Methods/design: Constituting an open-label, nested case-control study, this research endeavors to enroll 108 women diagnosed with POI. Participants will be randomly assigned in a 1:1 ratio to either the study group or the control group, each comprising 54 subjects. Ten patients from each group meeting specific criteria will partake in transcriptome analysis. An additional 10 subjects meeting the study criteria will form a healthy control group. The study group will exclusively undergo acupuncture treatment, while the control group will solely receive Fenmutong. Acupuncture sessions, administered thrice weekly across three menstrual cycles from the fifth day of menstruation, constitute the intervention. Primary outcome measurement rests on Follicle-Stimulating Hormone (FSH) levels, supplemented by secondary assessments encompassing biometric features, hormone biomarkers, anxiety and depression scores, and transcriptome analysis. Baseline measurements precede intervention, with post-intervention evaluations following. The study endeavors to discern specific genes linked to individualized responses to acupuncture. Data analysis, employing SPSS 25.0 software, incorporates a meticulous examination of peripheral blood samples for transcriptome analysis. The investigation aspires to shed light on genetic determinants influencing the effects of acupuncture on women with POI, thereby fostering elevated standards in patient care and management. Discussion: This study pivots on the principal objective of scrutinizing the efficacy of acupuncture as an adjunctive treatment for POI. Beyond effectiveness, it undertakes an exploration of the intricate mechanisms underlying the diverse responses exhibited by individuals in the context of acupuncture, augmenting the depth of understanding in this therapeutic domain.

Published
2024
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35. Research on the Impact of Executive Shareholding on New Investment in Enterprises Based on Multivariable Linear Regression Model

Author

Zhou, Shanyi, Yan, Ning, Li, Zhijun, Geng, Mo, Zhang, Xulong, Si, Hongbiao, Tang, Lihua, Sun, Wenyuan, Zhang, Longda, Cao, Yi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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36. Online Teaching and Learning - An Investigation into the Satisfaction of Learners and Teachers - Interaction and Perception

Author

Yan, Ning, Czanner, Gabriela, Zhang, Aiping, Batako, Andre D. L., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Janmanee, Pichai, editor, Chujuarjeen, Saichol, editor, Butdee, Suthep, editor, Srikhumsuk, Phatchani, editor, Batako, Andre D. L., editor, Burduk, Anna, editor, and Xavior, M. Anthony, editor

Published
2024
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37. Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration

Author

Mortazavi, Masood S., Qin, Tiancheng, and Yan, Ning

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, A.1, C.3, C.4, G.3, H.1, I.2, I.6, J.6
Abstract

Given an environment (e.g., a simulator) for evaluating samples in a specified design space and a set of weighted evaluation metrics -- one can use Theta-Resonance, a single-step Markov Decision Process (MDP), to train an intelligent agent producing progressively more optimal samples. In Theta-Resonance, a neural network consumes a constant input tensor and produces a policy as a set of conditional probability density functions (PDFs) for sampling each design dimension. We specialize existing policy gradient algorithms in deep reinforcement learning (D-RL) in order to use evaluation feedback (in terms of cost, penalty or reward) to update our policy network with robust algorithmic stability and minimal design evaluations. We study multiple neural architectures (for our policy network) within the context of a simple SoC design space and propose a method of constructing synthetic space exploration problems to compare and improve design space exploration (DSE) algorithms. Although we only present categorical design spaces, we also outline how to use Theta-Resonance in order to explore continuous and mixed continuous-discrete design spaces.

Published
2022

39. Quantitative evaluation of vertical control in orthodontic camouflage treatment for skeletal class II with hyperdivergent facial type

Author

Yan-Ning Guo, Sheng-Jie Cui, Ye Liu, Yu Fu, Jie-Ni Zhang, Yan-Heng Zhou, and Xue-Dong Wang

Subjects
Skeletal class II, Vertical control, Profile improvement, Specialties of internal medicine, RC581-951
Abstract

Abstract Background In this study, we sought to quantify the influence of vertical control assisted by a temporary anchorage device (TAD) on orthodontic treatment efficacy for skeletal class II patients with a hyperdivergent facial type and probe into the critical factors of profile improvement. Methods A total of 36 adult patients with skeletal class II and a hyperdivergent facial type were included in this retrospective case–control study. To exclude the effect of sagittal anchorage reinforcement, the patients were divided into two groups: a maxillary maximum anchorage (MMA) group (N = 17), in which TADs were only used to help with anterior tooth retraction, and the MMA with vertical control (MMA + VC) group (N = 19), for which TADs were also used to intrude the maxillary molars and incisors. The treatment outcome was evaluated using dental, skeletal, and soft-tissue-related parameters via a cephalometric analysis and cast superimposition. Results A significant decrease in ANB (P

Published
2024
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40. Diagnosis of single umbilical artery and risk of foetal congenital malformations by prenatal ultrasound: a retrospective study

Author

Junjin Yu, Qingqin Wu, Fanbin Kong, and Yan Ning

Subjects
Single umbilical artery, Foetal malformation, Prenatal diagnosis, Ultrasound, Gynecology and obstetrics, RG1-991
Abstract

Abstract Background Single umbilical artery (SUA) is strongly associated with foetal structural abnormalities; however, the exact pattern of this association has not been described. We aimed to investigate the occurrence of malformations in singleton pregnancies with SUA in China and to study the association between the absent side of the umbilical artery and foetal malformations. Methods This was a retrospective study of singleton pregnancies for which routine first-trimester anatomical screening was performed at 11+ 0-13+ 6 gestational weeks and, if the pregnancy continued, a second-trimester scan was performed at 20+ 0-24+ 0 weeks. Data were extracted from records at the referral centre, the Obstetrics and Gynecology Hospital of Fudan University, between January 2011 and April 2019 (n = 47,894). Using logistic regression, the odds ratios (OR) with 95% confidence intervals (CIs) were calculated for malformations associated with SUA. Results The incidence of SUA in our study was 2.0% (970/47,894). Of all foetuses with SUA, 387 (39.9%) had structural malformations. The malformation type varied, with cardiovascular complications being the most common. A robust association was observed between SUA and oesophageal stenosis or atresia (OR: 25.33), followed by cardiovascular (OR: 9.98–24.02), scoliosis (OR: 18.62), genitourinary (OR: 2.45–15.66), and brain malformations (OR: 4.73–9.12). The absence of the left umbilical artery (n = 445, 45.9%) was consistent with that of the right umbilical artery (n = 431, 44.4%). Furthermore, a significantly higher rate of an absent right than the left umbilical artery (p

Published
2024
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41. Non-pharmacological interventions of traditional Chinese medicine in treating polycystic ovary syndrome: a group consensus

Author

Tianyi Zhou, Fangfang Wang, Xinfen Xu, Yuhang Zhu, Rong Zhang, Hye Won Lee, Xiao-Yang Mio Hu, Fan Qu, Minchen Dai, Yijing He, Xinyue Li, Zhichun Jin, Yan Yin, Ziming Huang, Kaiqing Lin, Bianba Zhuoma, Xiaoyong Chen, Mali Chen, Longlong Fan, Lanzhong Guo, Junqin He, Lili Hou, Furui Jin, Hongying Kuang, Yuqin Lai, Yunbo Li, Yan Ning, Ziting Ouyang, Mingwo Pan, Zhengao Sun, Jinbang Xu, Dongxia Yang, Pingchun Yang, Qinhua Zhang, Chunmei Zhao, Rui Zhao, Xiaoli Zhao, and Dan Pan

Subjects
Polycystic ovary syndrome, Non-pharmacological interventions, Traditional Chinese medicine, Group consensus, Miscellaneous systems and treatments, RZ409.7-999
Abstract

Background: To make a group consensus about non-pharmacological interventions of traditional Chinese medicine in treating polycystic ovary syndrome based on the previous guidelines, literature, and expert viewpoints. Methods: Organized by Chinese Integrative Medicine & Traditional Chinese Medicine Academy, Chinese Maternal and Child Health Association, China, 29 experts from 18 Chinese provinces and 2 international experts, who specialize in gynecology, obstetrics, pediatrics, endocrinology, cardiovascular, psychology, reproductive genetics, nursing, acupuncture and tuina, traditional Chinese medicine, integrative medicine, and other disciplines, discussed and revised the recommendations one by one through in-person or online communication. Each recommendation was approved by ≥90% of the experts before it could be established. The main outcome measure is an optimal clinical regimen for addressing the requirements of women with PCOS and improving their quality of life. Result(s): The writing panel drafted the initial report, following a consensus process via adequate communication, which was then reviewed and revised by the consensus panel. This consensus provides 12 non-pharmacological interventions (including acupuncture, thumbtack needle, thread-embedding therapy, TEAS, AA, acupoint hot compress, cupping, acupressure, moxibustion, five elements music, aromatherapy, traditional Chinese exercises) for 8 phenotypes of PCOS, resulting in 34 items of clinical practice recommendations. Conclusion(s): The consensus provides 12 non-pharmacological interventions of traditional Chinese medicine for 8 phenotypes of PCOS, resulting in 34 items of clinical practice recommendations, which may be improved by more high-quality multicenter clinical trials.

Published
2024
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42. Clinical immunological characteristics of anti-interferon-γ autoantibodies syndrome: a 3 year prospective cohort study

Author

Siqiao Liang, Hanlin Liang, Xuemei Huang, Xiaona Liang, Ni Chen, Rong Xiao, Zengtao Luo, Quanfang Chen, Xinxin Zhong, Jingmin Deng, Jie Huang, Meihua Li, Meiling Yang, Wen Zeng, Haijuan Tang, Jing Jiang, Shouming Qin, Zhen Wei, Siyao Wu, Yan Ning, Ke Wang, Fu Cao, Jiujin Zhang, Qing Wei, Chengqiong Xu, Honglin Luo, Jian Song, Pei Li, Xiaokai Feng, Chenlu Yang, Jieping Lei, Hongwei Wang, Bin Cao, and Zhiyi He

Subjects
AIGAs, infection, immunological characteristics, glucocorticoid, Infectious and parasitic diseases, RC109-216, Microbiology, QR1-502
Abstract

Anti-interferon-γ autoantibodies (AIGAs) syndrome is susceptible to disseminated opportunistic infections due to increased AIGAs, but its clinical immunological characteristics remain unrecognized. We conducted a prospective cohort study between January 2021 and December 2023, recruiting patients with opportunistic infections who were categorized into AIGAs-positive and AIGAs-negative groups. Clinical immunological data and outcomes were documented. A subset of AIGAs-positive patients received glucocorticoid treatment, and its effectiveness was evaluated. A total of 238 patients were enrolled, with 135 AIGAs-positive and 103 AIGAs-negative patients. AIGAs-positive patients showed higher rates of multiple pathogen dissemination, shorter progression-free survival (PFS), and increased exacerbation frequency. They also showed elevated erythrocyte sedimentation rate (ESR), globulin (GLB), immunoglobulin (Ig)G, IgE, and IgG4 levels. Among the 70 AIGAs-positive patients monitored for at least six months, three subtypes were identified: high AIGAs titer with immune damage, high AIGAs titer without immune damage, and low AIGAs titer without immune damage. Of the 55 patients followed for 1 year, decreasing AIGAs titer and immune indices (GLB, IgG, IgE, IgG4) were observed. Among the 31 patients with high AIGAs titer and immune damage treated with low-dose glucocorticoids at the stable phase, reductions were observed in immune indices and AIGAs titer in 67.74% of cases. In summary, AIGAs-positive patients exhibit infectious and immunological characteristics. Elevated AIGAs, IgG, IgG4, and IgE indicate abnormal immune damages. AIGAs titer generally decrease over time. Stable-phase AIGAs-positive patients can be categorized into three subtypes, with those having high AIGAs titer and increased immune indices potentially benefitting from glucocorticoid treatment.

Published
2024
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44. Piperazine ferulate inhibits diabetic nephropathy by suppressing AGE/RAGE-mediated inflammatory signaling in rats and podocytes

Author

Xiu-Meng Zhang, Xin-Ran Min, Hong-Xiao Xie, Yan-Ning Jiang, Yi-Xin Rui, Bo Li, Nan Zeng, and Rong Liu

Subjects
AGE-RAGE, diabetic nephropathy, inflammations, piperazine ferulate, podocyte, Therapeutics. Pharmacology, RM1-950
Abstract

Objective: Diabetic nephropathy (DN) is a serious complication that may occur during the later stages of diabetes, and can be further exacerbated by podocyte damage. Piperazine ferulate (PF) has well-defined nephroprotective effects and is used clinically in the treatment of chronic nephritis and other kidney diseases. However, the renoprotective effects and mechanisms of PF on DN are not clear. This study aims to investigate the protective effect of PF on DN and its mechanism of action, to inform the clinical application of PF in DN treatment.Methods: Network pharmacology was performed to predict the mechanism of action of PF in DN. Male Sprague Dawley rats were intraperitoneally injected with STZ (60 mg/kg) to establish a DN model, and then assessed for renal injury after 12 weeks of administration. In vitro, rat podocytes were treated with 25 mmol/L glucose and cultured for 24 h, followed by an assessment of cell injury.Results: Our results showed that PF significantly improved renal function, reduced renal pathological changes, decreased inflammatory response, and alleviated podocyte damage in DN rats. PF also attenuated glucose-induced podocyte injury in vitro. Regarding molecular mechanisms, our study demonstrated that PF downregulated the expression of genes and proteins related to AGE-RAGE-mediated inflammatory signaling.Conclusion: In summary, PF exerts its renoprotective effects by decreasing inflammation and protecting against podocyte injury through the inhibition of the AGE/RAGE/NF-κB/NLRP3 pathway. Overall, these data support the clinical potential of PF as a renoprotective agent in DN.

Published
2024
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45. The association between the dietary inflammatory index and allergic rhinitis: a case–control study

Author

Qian Wang, Niuniu Dong, Yan Feng, Yan Ning, Ruifang Zhu, and Shifan Han

Subjects
allergic rhinitis, dietary inflammatory index (DII), diet, inflammation, case–control study, Nutrition. Foods and food supply, TX341-641
Abstract

Background and objectiveAllergic rhinitis (AR) is a common chronic inflammatory disease that significantly impacts the quality of life of patients. However, there is limited research on the relationship between the Dietary Inflammatory Index (DII) and the risk of AR. Our study aimed to assess the association between DII and AR in a sample of adults from North China.MethodsIn a case–control study, we selected 166 cases of AR and 166 age- and gender-matched controls. Dietary intake was assessed using a validated food frequency questionnaire. The energy-adjusted DII (E-DII) scores were calculated based on the quantity of diet components with inflammatory or anti-inflammatory potential. We used conditional logistic regression models to examine the association between E-DII and AR.ResultsOur findings indicate a positive correlation between E-DII and AR risk. After controlling for confounders, individuals in the highest E-DII tertile exhibited a 4.41-fold increased risk of AR compared to those in the lowest tertile (OR 4.41, 95% CI 2.31–8.41). Additionally, stratified analysis showed that E-DII was positively associated with AR subtype (seasonal vs. perennial), duration (≤6 years vs. >6 years), severity (mild vs. moderate–severe), and onset time (intermittent vs. persistent). Furthermore, individuals in the highest E-DII tertile had higher intake of total fat, SFA, PUFAs, and n-6 PUFAs.ConclusionIn conclusion, we realized that there is a positive association between the E-DII score and AR. The consumption of diets abundant in anti-inflammatory nutrients and low in pro-inflammatory nutrient contents is recommended as a preventative strategy against AR.

Published
2024
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