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7,995 results on '"Yuan, Xu"'

1. GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models

Author

Lou, Jiadong, Yuan, Xu, Zhang, Rui, Yuan, Xingliang, Gong, Neil, and Tzeng, Nian-Feng

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

Graph neural networks (GNNs) have exhibited superior performance in various classification tasks on graph-structured data. However, they encounter the potential vulnerability from the link stealing attacks, which can infer the presence of a link between two nodes via measuring the similarity of its incident nodes' prediction vectors produced by a GNN model. Such attacks pose severe security and privacy threats to the training graph used in GNN models. In this work, we propose a novel solution, called Graph Link Disguise (GRID), to defend against link stealing attacks with the formal guarantee of GNN model utility for retaining prediction accuracy. The key idea of GRID is to add carefully crafted noises to the nodes' prediction vectors for disguising adjacent nodes as n-hop indirect neighboring nodes. We take into account the graph topology and select only a subset of nodes (called core nodes) covering all links for adding noises, which can avert the noises offset and have the further advantages of reducing both the distortion loss and the computation cost. Our crafted noises can ensure 1) the noisy prediction vectors of any two adjacent nodes have their similarity level like that of two non-adjacent nodes and 2) the model prediction is unchanged to ensure zero utility loss. Extensive experiments on five datasets are conducted to show the effectiveness of our proposed GRID solution against different representative link-stealing attacks under transductive settings and inductive settings respectively, as well as two influence-based attacks. Meanwhile, it achieves a much better privacy-utility trade-off than existing methods when extended to GNNs.

Published
2025

2. Gender-Specific Effects of Bullying Victimization on Reactive Aggression among Chinese Early Adolescents: A Longitudinal Mediating Effect of Depressive Symptoms

Author

Defan Hong, Yuan Xu, Yingying Huang, Jing Lv, Yongrong Zhou, Zhaoming Guo, Wei Wang, Suo Jiang, and Panpan Yang

Abstract

In recent years, bullying victimization has received increasing attention from researchers worldwide. The link between bullying victimization and reactive aggression is well-established in numerous studies. However, longitudinal association and underlying mechanisms remain less explored. This study examined gender-specific effects of traditional and cyberbullying victimization on reactive aggression using longitudinal data from 486 Chinese primary and middle school students (51.44% boys, average age = 11.66 ± 1.66 years). Assessments occurred every six months across three waves. Structural equation modeling and multigroup analyses were used to test mediating effects and gender differences. Depressive symptoms mediated the association between traditional bullying victimization and reactive aggression, but no such mediation was observed for cyberbullying victimization. Gender differences were also found ([delta]CFI = 0.03, [delta]RESEA = 0.01). These findings underscore the importance of gender-specific interventions and supportive environments for addressing offline and online bullying.

Published
2025
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3. Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data

Author

Zhang, Yihe, Turney, Bryce, Sigdel, Purushottam, Yuan, Xu, Rappin, Eric, Lago, Adrian, Kimball, Sytske, Chen, Li, Darby, Paul, Peng, Lu, Aygun, Sercan, Tu, Yazhou, Najafi, M. Hassan, and Tzeng, Nian-Feng

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-surface observational data from Kentucky Mesonet stations (collected every five minutes, known as Micro data) and hourly atmospheric numerical outputs (termed as Macro data) for fine-resolution weather forecasting. The MiMa model employs an encoder-decoder transformer structure, with two encoders for processing multivariate data from both datasets and a decoder for forecasting weather variables over short time horizons. Each instance of the MiMa model, called a modelet, predicts the values of a specific weather parameter at an individual Mesonet station. The approach is extended with Re-MiMa modelets, which are designed to predict weather variables at ungauged locations by training on multivariate data from a few representative stations in a region, tagged with their elevations. Re-MiMa (short for Regional-MiMa) can provide highly accurate predictions across an entire region, even in areas without observational stations. Experimental results show that MiMa significantly outperforms current models, with Re-MiMa offering precise short-term forecasts for ungauged locations, marking a significant advancement in weather forecasting accuracy and applicability.

Published
2024

4. Nonexistence of minimal mass blow-up solution for the 2D cubic Zakharov-Kuznetsov equation

Author

Chen, Gong, Lan, Yang, and Yuan, Xu

Subjects
Mathematics - Analysis of PDEs
Abstract

For the 2D cubic (mass-critical) Zakharov-Kuznetsov equation, \begin{equation*} \partial_t\phi+\partial_{x_1}(\Delta \phi+\phi^3)=0,\quad (t,x)\in [0,\infty)\times \mathbb{R}^{2}, \end{equation*} we prove that there exist no finite/infinite time blow-up solution with minimal mass in the energy space. This nonexistence result is in contrast to the one obtained by Martel-Merle-Rapha\"el [17] for the mass-critical generalized Korteweg-de Vries (gKdV) equation. The proof relies on a refined ODE argument related to the modulation theory and a modified energy-virial Lyapunov functional with a monotonicity property., Comment: 20 pages

Published
2024

5. Universal parity and duality asymmetries-based optical force/torque framework

Author

Yuan, Xu, Zhao, Xiaoshu, Wen, Jiquan, Zheng, Hongxia, Li, Xiao, Chen, Huajin, Ng, Jack, and Lin, Zhifang

Subjects
Physics - Optics
Abstract

Understanding how the structured incident light interacts with the inherent properties of the manipulated particle and governs the optical force/torque exerted is a cornerstone in the design of optical manipulation techniques, apart from its theoretical significance. Based on the Cartesian multipole expansion theory, we establish a framework for optical force/torque exerted on an arbitrary sized bi-isotropic (chiral) spherical particle immersed in generic monochromatic optical fields. Rigorous expressions are thus derived which explicitly bridges such mechanical effects of light with particle-property-dependent coefficients and "force/torque source" quantities that characterize the incident light structures. Such quantities, totalled only 12, are quadratic in terms of electric and magnetic field vectors, among which are linear and angular momenta, gradient of energy density, spin density, and helicity. They are further organized into four categories based on their parity (P) and duality (D) symmetries and shown to couple with a particle with different P and D symmetries to induce optical force/torque. This classification specifies the symmetry-breaking criteria required to induce optical force/torque, offering a promising roadmap for engineering the optical manipulation.

Published
2024

6. BadCM: Invisible Backdoor Attack Against Cross-Modal Learning

Author

Zhang, Zheng, Yuan, Xu, Zhu, Lei, Song, Jingkuan, and Nie, Liqiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Multimedia
Abstract

Despite remarkable successes in unimodal learning tasks, backdoor attacks against cross-modal learning are still underexplored due to the limited generalization and inferior stealthiness when involving multiple modalities. Notably, since works in this area mainly inherit ideas from unimodal visual attacks, they struggle with dealing with diverse cross-modal attack circumstances and manipulating imperceptible trigger samples, which hinders their practicability in real-world applications. In this paper, we introduce a novel bilateral backdoor to fill in the missing pieces of the puzzle in the cross-modal backdoor and propose a generalized invisible backdoor framework against cross-modal learning (BadCM). Specifically, a cross-modal mining scheme is developed to capture the modality-invariant components as target poisoning areas, where well-designed trigger patterns injected into these regions can be efficiently recognized by the victim models. This strategy is adapted to different image-text cross-modal models, making our framework available to various attack scenarios. Furthermore, for generating poisoned samples of high stealthiness, we conceive modality-specific generators for visual and linguistic modalities that facilitate hiding explicit trigger patterns in modality-invariant regions. To the best of our knowledge, BadCM is the first invisible backdoor method deliberately designed for diverse cross-modal attacks within one unified framework. Comprehensive experimental evaluations on two typical applications, i.e., cross-modal retrieval and VQA, demonstrate the effectiveness and generalization of our method under multiple kinds of attack scenarios. Moreover, we show that BadCM can robustly evade existing backdoor defenses. Our code is available at https://github.com/xandery-geek/BadCM.

Published
2024
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7. Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model

Author

Zhou, Li, Yuan, Xu, Sun, Zenghui, Zhou, Zikun, and Lan, Jingsong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Large Multimodal Models (LMMs) have achieved significant progress by extending large language models. Building on this progress, the latest developments in LMMs demonstrate the ability to generate dense pixel-wise segmentation through the integration of segmentation models.Despite the innovations, the textual responses and segmentation masks of existing works remain at the instance level, showing limited ability to perform fine-grained understanding and segmentation even provided with detailed textual cues.To overcome this limitation, we introduce a Multi-Granularity Large Multimodal Model (MGLMM), which is capable of seamlessly adjusting the granularity of Segmentation and Captioning (SegCap) following user instructions, from panoptic SegCap to fine-grained SegCap. We name such a new task Multi-Granularity Segmentation and Captioning (MGSC). Observing the lack of a benchmark for model training and evaluation over the MGSC task, we establish a benchmark with aligned masks and captions in multi-granularity using our customized automated annotation pipeline. This benchmark comprises 10K images and more than 30K image-question pairs. We will release our dataset along with the implementation of our automated dataset annotation pipeline for further research.Besides, we propose a novel unified SegCap data format to unify heterogeneous segmentation datasets; it effectively facilitates learning to associate object concepts with visual features during multi-task training. Extensive experiments demonstrate that our MGLMM excels at tackling more than eight downstream tasks and achieves state-of-the-art performance in MGSC, GCG, image captioning, referring segmentation, multiple and empty segmentation, and reasoning segmentation tasks. The great performance and versatility of MGLMM underscore its potential impact on advancing multimodal research., Comment: Code and dataset will be released at https://github.com/lizhou-cs/mglmm. 7 pages, 4 figures with Supplementary Material

Published
2024

8. Towards Robust Vision Transformer via Masked Adaptive Ensemble

Author

Lin, Fudong, Lou, Jiadong, Yuan, Xu, and Tzeng, Nian-Feng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Adversarial training (AT) can help improve the robustness of Vision Transformers (ViT) against adversarial attacks by intentionally injecting adversarial examples into the training data. However, this way of adversarial injection inevitably incurs standard accuracy degradation to some extent, thereby calling for a trade-off between standard accuracy and robustness. Besides, the prominent AT solutions are still vulnerable to adaptive attacks. To tackle such shortcomings, this paper proposes a novel ViT architecture, including a detector and a classifier bridged by our newly developed adaptive ensemble. Specifically, we empirically discover that detecting adversarial examples can benefit from the Guided Backpropagation technique. Driven by this discovery, a novel Multi-head Self-Attention (MSA) mechanism is introduced to enhance our detector to sniff adversarial examples. Then, a classifier with two encoders is employed for extracting visual representations respectively from clean images and adversarial examples, with our adaptive ensemble to adaptively adjust the proportion of visual representations from the two encoders for accurate classification. This design enables our ViT architecture to achieve a better trade-off between standard accuracy and robustness. Besides, our adaptive ensemble technique allows us to mask off a random subset of image patches within input data, boosting our ViT's robustness against adaptive attacks, while maintaining high standard accuracy. Experimental results exhibit that our ViT architecture, on CIFAR-10, achieves the best standard accuracy and adversarial robustness of 90.3% and 49.8%, respectively., Comment: 9 pages

Published
2024
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9. FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering

Author

Islam, Md Sirajul, Javaherian, Simin, Xu, Fei, Yuan, Xu, Chen, Li, and Tzeng, Nian-Feng

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without exposing their local data. One of the major challenges in FL is the presence of uneven data distributions across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. To address the performance degradation issue incurred by such data heterogeneity, clustered federated learning (CFL) shows its promise by grouping clients into separate learning clusters based on the similarity of their local data distributions. However, state-of-the-art CFL approaches require a large number of communication rounds to learn the distribution similarities during training until the formation of clusters is stabilized. Moreover, some of these algorithms heavily rely on a predefined number of clusters, thus limiting their flexibility and adaptability. In this paper, we propose {\em FedClust}, a novel approach for CFL that leverages the correlation between local model weights and the data distribution of clients. {\em FedClust} groups clients into clusters in a one-shot manner by measuring the similarity degrees among clients based on the strategically selected partial weights of locally trained models. We conduct extensive experiments on four benchmark datasets with different non-IID data settings. Experimental results demonstrate that {\em FedClust} achieves higher model accuracy up to $\sim$45\% as well as faster convergence with a significantly reduced communication cost up to 2.7$\times$ compared to its state-of-the-art counterparts.

Published
2024

10. On the near soliton dynamics for the 2D cubic Zakharov-Kuznetsov equations

Author

Chen, Gong, Lan, Yang, and Yuan, Xu

Subjects
Mathematics - Analysis of PDEs
Abstract

In this article, we consider the Cauchy problem for the cubic (mass-critical) Zakharov-Kuznetsov equations in dimension two: $$\partial_t u+\partial_{x_1}(\Delta u+u^3)=0,\quad (t,x)\in [0,\infty)\times \mathbb{R}^{2}.$$ For initial data in $H^1$ close to the soliton with a suitable space-decay property, we fully describe the asymptotic behavior of the corresponding solution. More precisely, for such initial data, we show that only three possible behaviors can occur: 1) The solution leaves a tube near soliton in finite time; 2) the solution blows up in finite time; 3) the solution is global and locally converges to a soliton. In addition, we show that for initial data near a soliton with non-positive energy and above the threshold mass, the corresponding solution will blow up as described in Case 2. Our proof is inspired by the techniques developed for mass-critical generalized Korteweg-de Vries equation (gKdV) equation in a similar context by Martel-Merle-Rapha\"el. More precisely, our proof relies on refined modulation estimates and a modified energy-virial Lyapunov functional. The primary challenge in our problem is the lack of coercivity of the Schr\"odinger operator which appears in the virial-type estimate. To overcome the difficulty, we apply a transform, which was first introduced in Kenig-Martel [13], to perform the virial computations after converting the original problem to the adjoint one. Th coercivity of the Schr\"odinger operator in the adjoint problem has been numerically verified by Farah-Holmer-Roudenko-Yang [9]., Comment: 65 pages

Published
2024

11. An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions

Author

Lin, Fudong, Guillot, Kaleb, Crawford, Summer, Zhang, Yihe, Yuan, Xu, and Tzeng, Nian-Feng

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promising achievements in solving many scientific problems such as drug discovery, precipitation nowcasting, etc., the development of deep learning models for predicting crop yields is constantly hindered by the lack of an open and large-scale deep learning-ready dataset with multiple modalities to accommodate sufficient information. To remedy this, we introduce the CropNet dataset, the first terabyte-sized, publicly available, and multi-modal dataset specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. Our CropNet dataset is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, for over 2200 U.S. counties spanning 6 years (2017-2022), expected to facilitate researchers in developing versatile deep learning models for timely and precisely predicting crop yields at the county-level, by accounting for the effects of both short-term growing season weather variations and long-term climate change on crop yields. Besides, we develop the CropNet package, offering three types of APIs, for facilitating researchers in downloading the CropNet data on the fly over the time and region of interest, and flexibly building their deep learning models for accurate crop yield predictions. Extensive experiments have been conducted on our CropNet dataset via employing various types of deep learning solutions, with the results validating the general applicability and the efficacy of the CropNet dataset in climate change-aware crop yield predictions., Comment: 13 pages

Published
2024
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12. Higher-order Structure Based Anomaly Detection on Attributed Networks

Author

Yuan, Xu, Zhou, Na, Yu, Shuo, Huang, Huafei, Chen, Zhikui, and Xia, Feng

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks. However, due to the lack of an effective mechanism in most existing graph learning methods, these complex interaction patterns fail to be applied in detecting anomalies, hindering the progress of anomaly detection to some extent. In order to address the aforementioned issue, we present a higher-order structure based anomaly detection (GUIDE) method. We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. Moreover, we design a graph attention layer to evaluate the significance of neighbors to nodes through their higher-order structure differences. Finally, we leverage node attribute and higher-order structure reconstruction errors to find anomalies. Extensive experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly outperforms the state-of-art methods.

Published
2024

18. FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering

Author

Islam, Md Sirajul, Javaherian, Simin, Xu, Fei, Yuan, Xu, Chen, Li, and Tzeng, Nian-Feng

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions. However, existing CFL approaches require a large number of communication rounds for stable cluster formation and rely on a predefined number of clusters, thus limiting their flexibility and adaptability. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions. FedClust groups clients into clusters in a one-shot manner using strategically selected partial model weights and dynamically accommodates newcomers in real-time. Experimental results demonstrate FedClust outperforms baseline approaches in terms of accuracy and communication costs.

Published
2024

19. A charge reversal nano-assembly prevents hepatic steatosis by resolving inflammation and improving lipid metabolism

Author

Haoyu Wang, Sheng'e Su, Xin An, Yuan Xu, Jiacheng Sun, Mingming Zhen, Chunru Wang, and Chunli Bai

Subjects
Fullerene nano-assembly, Charge reversal, Inflammation, lipid metabolism, hepatic steatosis, Materials of engineering and construction. Mechanics of materials, TA401-492, Biology (General), QH301-705.5
Abstract

Lipid metabolism imbalance combined with over-activated inflammation are two key factors for hepatic stestosis. However, on-demand anchoring inflammation and lipid metabolism disorder for hepatic stestosis treatment has yet to be realized. Here we propose a charge reversal fullerene based nano-assembly to migrate hepatic steatosis via inhibiting macrophage-mediated inflammation and normalizing hepatocellular lipid metabolism in obesity mice. Our nano-assembly (abbreviated as FPPD) is comprised of electropositive polyetherimide (PEI), charge-shielded dimethylmaleic anhydride (DMA), and poly(lactic-co-glycolic acid) (PLGA), which provides hydrophobic chains for self-assembly with anti-oxidative dicarboxy fullerene poly(ethylene glycol) molecule (FP). The obtained FPPD nano-assembly owns a charge reversal ability that switches to a positive charge in an acidic environment that targets the electronegative mitochondria both in pro-inflammatory macrophages and steatosis hepatocytes. We demonstrate that the anti-oxidative and mitochondria-targeting FPPD notably reduces inflammation in macrophages and lipid accumulation in hepatocytes by quenching excessive reactive oxygen species (ROS) and improving mitochondrial function in vitro. Importantly, FPPD nano-assembly reveals a superior anti-hepatic steatosis effect via migrating inflammation and facilitating lipid transport in obesity mice. Overall, the charge reversal nano-assembly reduces over-activated inflammation and promotes lipid metabolism that provides an effectiveness of a multi-target strategy for hepatic steatosis treatment.

Published
2025
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20. Modeling and control of functional electrical stimulation cycling training system

Author

Mingxu Sun, Fangyuan Cheng, Tingting Wang, Chuan Hu, Sizhong Wang, Xiangyang Li, Yuan Xu, and Min Liu

Subjects
Functional electrical stimulation cycling, Modeling, Particle swarm optimization back propagation proportional integral differentiation, Inertial sensors, Medicine, Science
Abstract

Abstract Functional electrical stimulation (FES), a rehabilitation technique, typically relies on physiotherapists using trial-and-error tests to determine effective stimulation patterns. Therefore, this study proposed a kind of pedal hill modeling to establish an optimal stimulus mode with the maximum torque efficiency optimization objective. This study also proposed a new model based on the particle swarm optimization (PSO) algorithm, the back propagation (BP) neural network algorithm, and the proportional integral derivative (PID) control composite algorithm. Six participants were recruited for the experiment. Using the proposed modeling method, we found an appropriate stimulation mode for each of the six subjects, and then each subject performed three sets of experiments for cycling without electrical stimulation, cycling with fixed pulse width, and cycling with adaptive adjustment of pulse width by the fabricated controller. The results of the study showed that root mean square error (RMSE), average (AVE), training time and number of stops all performed well compared to the no control and fixed control conditions and the adaptive pulse width control system of the fabricated controller allows subjects to train at a longer continuous running time and a more stable cycling training speed.

Published
2025
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21. Therapeutic and prognostic impact of target volume delineation in postoperative radiotherapy for high-grade glioma patients with subventricular zone involvement

Author

Fei Sun, Yan Zhu, Guanghui Gan, Yuan Xu, and Xiaoting Xu

Subjects
High-grade glioma, Subventricular zone, Radiotherapy, Target volume delineation, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Objective This study aimed to analyze the effect of target volumes for radiotherapy and dose on the prognosis of high-grade glioma (HGG) patients when the tumor involves the subventricular zone (SVZ), and to provide a reference for postoperative target volume delineation in HGG patients with SVZ involvement. Methods The clinical and pathological data were collected from 50 HGG patients with SVZ involvement were collected in the Department of Neurosurgery of the First Affiliated Hospital of Soochow University during the period from January 1, 2017 to December 31, 2020. The average dose (Dmean) of the whole ipsilateral and contralateral SVZs as well as the V45Gy and V60Gy of the whole ipsilateral SVZs of the tumor were derived from the dose-volume histograms (DVH). The Kaplan-Meier analysis was applied to compare the survival differences between groups under different factors. The Cox proportional risk regression model was used to analyze the influencing factors of progression-free survival (PFS) and overall survival (OS). The correlation between the size of the ipsilateral SVZ target area range and the progression pattern was tested by chi-square test. Results Univariate analysis revealed that the potential predictors of PFS of HGG patients with tumor involvement in SVZ were as follows: multiple lesions, tumor size > 3.5 cm and total resection; the potential predictors of OS were multiple lesions, surgical approaches to the lateral ventricles and the dose of contralateral SVZ > 37.33 Gy. Multibariate analysis showed that tumor size > 3.5 cm and total resection were the independent prognostic factors of PFS; multiple lesions was the independent prognostic factors of OS. The Kaplan-Meier method showed that the median PFS and OS of HGG patients with V60Gy ≥ 50% was higher than that of patients with V60Gy

Published
2025
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22. An active representation learning method for reaction yield prediction with small-scale data

Author

Peng-Xiang Hua, Zhen Huang, Zhe-Yuan Xu, Qiang Zhao, Chen-Yang Ye, Yi-Feng Wang, Yun-He Xu, Yao Fu, and Hu Ding

Subjects
Chemistry, QD1-999
Abstract

Abstract Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called “RS-Coreset”, where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles.

Published
2025
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23. Dual targeting PD-L1 and 4-1BB to overcome dendritic cell-mediated lenalidomide resistance in follicular lymphoma

Author

Zhong Zheng, Jian-Biao Wang, Rui Sun, Nan Wang, Xiang-Qin Weng, Tian-Yuan Xu, Di Fu, Yan Feng, Peng-Peng Xu, Shu Cheng, Li Wang, Yan Zhao, Bin Qu, Chuan-Xin Huang, and Wei-Li Zhao

Subjects
Medicine, Biology (General), QH301-705.5
Abstract

Abstract Immunomodulatory agent lenalidomide is effective in treating follicular lymphoma (FL). We conducted the first trial of immunotherapy rituximab plus lenalidomide in newly diagnosed FL in China (NCT03715309). One-hundred and fifteen patients were enrolled and treated with rituximab 375 mg/m2 intravenously on day 0 and lenalidomide 25 mg orally on day 1–10 for 6 cycles of induction treatment, as well as lenalidomide for 6 cycles and rituximab for 8 cycles of maintenance treatment. We found that inferior progression-free survival of the patients was significantly associated with elevated serum β2m and lymph node >6 cm, linking to decreased lymphoma cell autophagy and dendritic cell infiltration within the tumor microenvironment. PU.1 transcriptionally downregulated PD-L1 (Programmed death ligand 1) expression and upregulated 4-1BBL (4-1BB ligand) expression, increased lymphoma cell autophagy and dendritic cell maturation via PD-1/PD-L1 and 4-1BB/4-1BBL interaction. In vitro in co-culture system and in vivo in murine xenograft model, knockdown of PU.1 induced lenalidomide resistance, but sensitized FL cells to bi-specific PD-L1/4-1BB antibody or combined treatment of PD-L1 inhibitor and 4-1BB agonist. Collectively, PU.1 is essential in immunomodulatory effect of FL through PD-1/PD-L1- and 4-1BB/4-1BBL-mediated microenvironmental modulation. Dual targeting PD-L1 and 4-1BB could be an alternative immunotherapeutic strategy in the chemo-free era of FL treatment.

Published
2025
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24. Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study

Author

Lele Ye, Chunhao Long, Binbing Xu, Xuyang Yao, Jiaye Yu, Yunhui Luo, Yuan Xu, Zhuofeng Jiang, Zekai Nian, Yawen Zheng, Yaoyao Cai, Xiangyang Xue, and Gangqiang Guo

Subjects
Serous ovarian carcinoma, Predictive model, Programmed cell death, Cell death index, Predictive preventive and personalized medicine (PPPM/3PM), Therapeutics. Pharmacology, RM1-950, Biochemistry, QD415-436
Abstract

Abstract Background Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)–based model using PCD-related genes. Methods We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. Results The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. Conclusions The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Published
2025
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25. Medication Decision-making for Patients with Multimorbidity——Study on Medical Records in Consultation of General Practice

Author

LUO Yuan, XU Zhijie, XIA Yu, SHI Jiana, JIANG Zhizhi, ZHOU Xinmei, ZHAO Yang, TONG Yuling

Subjects
multiple chronic conditions, consultation, general practitioners, medication decision-making, case reports, Medicine
Abstract

Patients with multimorbidity often require the concomitant use of multiple medications, presenting general practitioners (GPs) with the dilemma of assessing the benefits and risks due to complex and potential interactions between diseases and medications. This article takes one case of a multimorbid patient at a community health service center to illustrate and elucidate the process by which GP employs a medication decision-making framework for multimorbidity (MDMF). Furthermore, by examining the multi-stage goals set by Ariadne principle in the process of multimorbidity management, this article analyzes the key points and considerations for GPs in the assessment, communication, and making treatment plans, thereby offering reference to improve the quality of medication decision-making for patients with multimorbidity in the community.

Published
2025
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26. Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Author

Yuan, Xu, Zhang, Zheng, Wang, Xunguang, and Wu, Lin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia
Abstract

Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerability. Notably, it is challenging to efficiently distill reliable semantic representatives for deep hashing to guide adversarial learning, and thereby it hinders the enhancement of adversarial robustness of deep hashing-based retrieval models. Moreover, current researches on adversarial training for deep hashing are hard to be formalized into a unified minimax structure. In this paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the adversarial robustness of deep hashing models. Specifically, we conceive a discriminative mainstay features learning (DMFL) scheme to construct semantic representatives for guiding adversarial learning in deep hashing. Particularly, our DMFL with the strict theoretical guarantee is adaptively optimized in a discriminative learning manner, where both discriminative and semantic properties are jointly considered. Moreover, adversarial examples are fabricated by maximizing the Hamming distance between the hash codes of adversarial samples and mainstay features, the efficacy of which is validated in the adversarial attack trials. Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes. Extensive experiments on benchmark datasets show superb attack performance against the state-of-the-art algorithms, meanwhile, the proposed adversarial training can effectively eliminate adversarial perturbations for trustworthy deep hashing-based retrieval. Our code is available at https://github.com/xandery-geek/SAAT.

Published
2023
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27. Enhanced GaAs photocathode performance through segmented activation with IoT-integrated precision control

Author

Yuan Xu, Shaobo Cui, Xiangjiang li, Luzi Wang, Fan Feng, and Hao Wang

Subjects
GaAs photocathodes, Quantum efficiency, IoT integration, Segmented activation model, Photoemission efficiency, Engineering (General). Civil engineering (General), TA1-2040
Abstract

GaAs photocathodes are widely used in night vision devices, high-energy physics experiments, and other fields due to their high quantum efficiency and low thermal emission. However, traditional preparation methods struggle to precisely control the thickness and uniformity of the activation layer, leading to batch differences and performance instability. To address these issues, we propose a segmented activation model using solid oxygen sources with silver oxide and barium peroxide dispensers, integrated with IoT technology for dynamic adjustment, enabling precise control of the preparation process. This model leverages IoT for real-time monitoring and automatic adjustment of temperature, Cs, and O source release rates, enhancing consistency and automation. Experimental results show that the improved process increases quantum efficiency by about 20% and extends operational lifetime by nearly 60%, reducing batch variations and improving reliability. The optimized process supports large-scale production and improved performance of GaAs photocathodes, offering strong technical support for related fields.

Published
2025
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29. MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer

Author

Lin, Fudong, Crawford, Summer, Guillot, Kaleb, Zhang, Yihe, Chen, Yan, Yuan, Xu, Chen, Li, Williams, Shelby, Minvielle, Robert, Xiao, Xiangming, Gholson, Drew, Ashwell, Nicolas, Setiyono, Tri, Tubana, Brenda, Peng, Lu, Bayoumi, Magdy, and Tzeng, Nian-Feng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Precise crop yield prediction provides valuable information for agricultural planning and decision-making processes. However, timely predicting crop yields remains challenging as crop growth is sensitive to growing season weather variation and climate change. In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops. Specifically, our MMST-ViT consists of a Multi-Modal Transformer, a Spatial Transformer, and a Temporal Transformer. The Multi-Modal Transformer leverages both visual remote sensing data and short-term meteorological data for modeling the effect of growing season weather variations on crop growth. The Spatial Transformer learns the high-resolution spatial dependency among counties for accurate agricultural tracking. The Temporal Transformer captures the long-range temporal dependency for learning the impact of long-term climate change on crops. Meanwhile, we also devise a novel multi-modal contrastive learning technique to pre-train our model without extensive human supervision. Hence, our MMST-ViT captures the impacts of both short-term weather variations and long-term climate change on crops by leveraging both satellite images and meteorological data. We have conducted extensive experiments on over 200 counties in the United States, with the experimental results exhibiting that our MMST-ViT outperforms its counterparts under three performance metrics of interest.

Published
2023

30. Quantitative observability for one-dimensional Schr\'odinger equations with potentials

Author

Su, Pei, Sun, Chenmin, and Yuan, Xu

Subjects
Mathematics - Analysis of PDEs, Mathematics - Optimization and Control
Abstract

In this note, we prove the quantitative observability with an explicit control cost for the 1D Schr\"odinger equation over $\mathbb{R}$ with real-valued, bounded continuous potential on thick sets. Our proof relies on different techniques for low-frequency and high-frequency estimates. In particular, we extend the large time observability result for the 1D free Schrodinger equation in Theorem 1.1 of Huang-Wang-Wang [20] to any short time. As another byproduct, we extend the spectral inequality of Lebeau-Moyano [27] for real-analytic potentials to bounded continuous potentials in the one-dimensional case., Comment: 26 pages, comments are welcome

Published
2023

31. Backdoor Federated Learning by Poisoning Backdoor-Critical Layers

Author

Zhuang, Haomin, Yu, Mingxian, Wang, Hao, Hua, Yang, Li, Jian, and Yuan, Xu

Subjects
Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices. However, the decentralized learning paradigm and heterogeneity of FL further extend the attack surface for backdoor attacks. Existing FL attack and defense methodologies typically focus on the whole model. None of them recognizes the existence of backdoor-critical (BC) layers-a small subset of layers that dominate the model vulnerabilities. Attacking the BC layers achieves equivalent effects as attacking the whole model but at a far smaller chance of being detected by state-of-the-art (SOTA) defenses. This paper proposes a general in-situ approach that identifies and verifies BC layers from the perspective of attackers. Based on the identified BC layers, we carefully craft a new backdoor attack methodology that adaptively seeks a fundamental balance between attacking effects and stealthiness under various defense strategies. Extensive experiments show that our BC layer-aware backdoor attacks can successfully backdoor FL under seven SOTA defenses with only 10% malicious clients and outperform the latest backdoor attack methods., Comment: Accepted to ICLR'24

Published
2023

32. Factors associated with systemic inflammatory response syndrome negativity at the early stage of sepsis among nonsurviving sepsis patients in intensive care unit

Author

Chunyi Fu, Jingchao Luo, Xiaogang Wang, Yuan Xu, and Taotao Liu

Subjects
MIMIC database, sepsis, SIRS negative, SOFA, Surgery, RD1-811, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Objectives This study aims to investigate the factors associated with systemic inflammatory response syndrome (SIRS) negativity during the early stage of sepsis in deceased septic patients. Methods Adult septic patients were included from the Medical Information Mart for Intensive Care IV (MIMIC‐IV) database between 2008 and 2019. Patients who did not survive after 28 days were assigned to the SIRS‐negative or SIRS‐positive group according to whether the SIRS score was less than two points within 24 h of intensive care unit admission. Logistic regression and a restricted cubic spline model were used to analyze factors and dose–response relationships. Results A total of 53,150 patients were screened in the MIMIC‐IV database, and 2706 sepsis nonsurvivors were ultimately included, 101 of whom were negative for SIRS. There were significant differences in sequential organ failure assessment (SOFA) scores between groups (8.18 ± 3.58 vs. 9.75 ± 4.28, p

Published
2024
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33. Spatial network characteristics and drivers of carbon emission reduction capability in urban agglomerations: A case study of Shandong Province

Author

Shu Tian, Yujie Zhang, Yue Xu, Yuan Xu, Shuo Yang, Qingsong Wang, Xueliang Yuan, and Qiao Ma

Subjects
Carbon emission reduction capacity, Spatial correlation, Influencing factors, Prefectural cities in Shandong Province, Social networking analysis, Quadruple assignment procedure, Environmental sciences, GE1-350
Abstract

Synergistic carbon emission reduction at the urban scale is an inherent requirement for China to realize its carbon emission reduction commitments and promote sustainable and regional synergistic development. Using 16 prefectural cities in Shandong Province as an example, this study defines and quantifies the regional carbon emission reduction capacity (CERC) based on the synergistic development of carbon emission reduction, environmental protection, and economic growth objectives. The spatial network characteristics of the (CERC) and its drivers were analyzed using social network analysis and quadratic assignment procedure regression methods. The results revealed that the spatial correlation of the CERC among prefectural cities has been increasing over the years. Jinan-Zibo-Qingdao is the center of the network, while the spatial linkage strength in the southwest and northeast of Shandong Province is weak. Geospatial distance and scientific development differences have a significant negative effect on the intensity of spatial association, while differences in economic and informatization developments have a considerable positive impact. Environmental regulatory differences and transportation differences are not significant. This study offers a methodological reference for similar studies in other countries or regions. At the same time, the findings provide a scientific basis for the government to rationally allocate urban resources and promote regional synergistic carbon emission reduction.

Published
2024
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34. Genome-wide identification, gene cloning, subcellular location and expression analysis of the OPR gene family under salt stress in sweetpotato

Author

Wenxing Li, Yongping Li, Yuan Xu, Sunjeet Kumar, Yi Liu, and Guopeng Zhu

Subjects
Sweetpotato, OPR gene family, Gene cloning, Subcellular localization, Gene expression, Botany, QK1-989
Abstract

Abstract Background The 12-oxo-phytodienoic acid reductase (OPR) enzyme is crucial for the synthesis of jasmonates (JAs), and is involved in the plant stress response. However, the OPR gene family in sweetpotato, an important horticultural crop, remains unidentified. Results In this study, we employed bioinformatics techniques to identify nine IbOPR genes. Phylogenetic analysis revealed that these genes could be divided into Group I and Group II. Synteny analysis indicated that IbOPR evolution was driven by tandem duplication, whole-genome duplication (WGD), and segmental duplication events. The promoter sequences of IbOPRs were found to be associated with stress and hormonal responses. Additionally, we successfully cloned four IbOPRs from "Haida HD7791" and "Haida HD7798" using homologous cloning technology. These sequences were 1203 bp, 1200 bp, 1134 bp, and 1137 bp in length and encoded 400, 399, 377, and 378 amino acids, respectively. The protein sequence similarity between the salt-tolerant variety "Haida HD7791" and the salt-sensitive variety "Haida HD7798" was determined to be 96.75% for IbOPR2, 99.75% for IbOPR3, 92.06% for IbOPR6, and 98.68% for IbOPR7. Phylogenetic analysis categorized IbOPR2 and IbOPR3 proteins into Group II, while IbOPR6 and IbOPR7 proteins belonged to Group I. Subcellular localization experiments showed IbOPR2 protein present in the peroxisome, while IbOPR3, IbOPR6, and IbOPR7 proteins were found in the cytoplasm and nucleus. Salt stress induction experiments demonstrated that IbOPR2, IbOPR3, and IbOPR7 were significantly upregulated only in 'Haida HD7791' after 6 h. In contrast, IbOPR6 was induced in 'Haida HD7798' at 6 h but inhibited in 'Haida HD7791' at later time points (12, 24, 48, and 72 h), highlighting functional differences in salt stress responses. Conclusions Our findings suggest that IbOPR2 may play a crucial role in sweetpotato's response to salt stress by participating in JAs synthesis. These results provide a foundation for future functional analyses of OPR genes in sweetpotato.

Published
2024
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35. Modeling monocular form deprivation in rabbits using a simulated-cataract intraocular lens

Author

Si-Yi Gu, Li-Ming Xu, Wei-Jie Sun, Li-Li Liang, Lei Lin, Han Zou, Jing-Yuan Xu, Yu Zheng, Yuan-Yuan Li, Yin-Ying Zhao, Ping-Jun Chang, and Yun-E Zhao

Subjects
monocular deprivation, form deprivation, intraocular lens, congenital cataract, amblyopia, Ophthalmology, RE1-994
Abstract

AIM: To establish an animal model of form deprivation amblyopia based on a simulated cataract intraocular lens (IOLs). METHODS: Poly(dimethyl siloxane)-SiO2 thin films (PSF) with different degrees of opacity as IOL materials were prepared. The light transmission of the PSF-IOL was measured, and its in vitro biosafety was determined by cell counting kit (CCK)-8 assay using the HLEC-B3 cell line and ARPE-19 cell line. Subsequently, the in vivo safety was determined by implanting the PSF-IOL with 10% wt SiO2 into the right eyes of New Zealand white rabbits (PSF-IOL group), and compared with two control groups: contralateral comparison group and normal control (NC) group (Contralateral comparison group: the fellow eye; NC group: a group of binocular normal rabbits without intervention). The flash visual-evoked potentials (F-VEPs) were measured to verify amblyopia. RESULTS: PSFs containing 0, 2%, and 10% wt SiO2 were successfully constructed. The 0 SiO2 PSF was transparent, while the 10% wt SiO2 PSF was completely opaque. It was found that PSF did not induce unwanted cytotoxicity in HLECs and ARPE19 cells in vitro. In vitro, PSF-IOL with 10% wt SiO2 was also non-toxic, and no significant inflammation or structural changes occurred after four weeks of PSF-IOL implantation. Finally, our IOL-simulated congenital cataract rabbit detected by F-VEPs suggested tentative amblyopia. CONCLUSION: A PSF-IOL that mimics cataracts is created. A novel form deprivation model is created by the IOL-simulated congenital cataract rabbit. It can be developed fast and stable and holds great potential for future study.

Published
2024
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36. Lactate metabolism and histone lactylation in the central nervous system disorders: impacts and molecular mechanisms

Author

Yao Wang, Ping Li, Yuan Xu, Linyu Feng, Yongkang Fang, Guini Song, Li Xu, Zhou Zhu, Wei Wang, Qi Mei, and Minjie Xie

Subjects
Lactate, Histone lactylation, CNS diseases, Inflammation, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Brain takes up approximately 20% of the total body oxygen and glucose consumption due to its relatively high energy demand. Glucose is one of the major sources to generate ATP, the process of which can be realized via glycolysis, oxidative phosphorylation, pentose phosphate pathways and others. Lactate serves as a hub molecule amid these metabolic pathways, as it may function as product of glycolysis, substrate of a variety of enzymes and signal molecule. Thus, the roles of lactate in central nervous system (CNS) diseases need to be comprehensively elucidated. Histone lactylation is a novel lactate-dependent epigenetic modification that plays an important role in immune regulation and maintaining homeostasis. However, there’s still a lack of studies unveiling the functions of histone lactylation in the CNS. In this review, we first comprehensively reviewed the roles lactate plays in the CNS under both physiological and pathological conditions. Subsequently, we’ve further discussed the functions of histone lactylation in various neurological diseases. Furthermore, future perspectives regarding histone lactylation and its therapeutic potentials in stroke are also elucidated, which may possess potential clinical applications.

Published
2024
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37. Self-harm hospitalizations and neighbourhood level material and social deprivation in Canada: an ecological study

Author

Li Liu, Nathaniel J. Pollock, Gisèle Contreras, Yuan Xu, and Wendy Thompson

Subjects
Self-harm, Suicide, Health care, Social deprivation, Material deprivation, Socio-economic status, Psychiatry, RC435-571
Abstract

Abstract Background Socio-economic status is associated with self-harm at the individual and area level. In Canada, there is limited evidence on the relationship between area-level markers of socio-economic status and self-harm. The objective of this study was to assess the impact of small area-level material and social deprivation on rates of hospitalization due to self-harm. Methods Administrative data on hospitalizations from two databases in Canada (Discharge Abstract Database and Ontario Mental Health Reporting System) for the period April 1, 2015 to March 31, 2022, were analyzed. Rates of self-harm hospitalization and percentage of repeated admissions were estimated across quintiles of material and social deprivation. Rate ratios were computed to evaluate disparities. The rates were stratified by sex and age group; the percentage of repeated admissions were stratified by sex. Results In total, the study included 109,398 hospitalizations due to self-harm. Populations in more deprived areas had higher rates of self-harm hospitalizations than those in less deprived areas. The rate ratios for people who lived in the most deprived areas over the least deprived areas were 1.48 (95% CI: 1.38–1.58) and 1.71 (95% CI: 1.60–1.82) for material and social deprivation, separately. The largest disparity was among people aged 25–44 years for material and 45–64 years for social deprivation. Percentages of repeated self-harm hospitalizations were significantly higher in more deprived areas compared to less deprived areas for social deprivation, and among males but not females for material deprivation. Conclusions Both material and social deprivation were associated with self-harm hospitalization and repeated admissions; the disparity varied by subgroup and the deprivation components. This study demonstrated a need to consider interventions at the neighbourhood level and address both community and population-level conditions of social and material need.

Published
2024
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38. Transplanted deep-layer cortical neuroblasts integrate into host neural circuits and alleviate motor defects in hypoxic-ischemic encephalopathy injured mice

Author

Mengnan Wu, Yuan Xu, Xiaoli Ji, Yingying Zhou, Yuan Li, Ban Feng, Qian Cheng, Hui He, Xingsheng Peng, Wenhao Zhou, Yuejun Chen, and Man Xiong

Subjects
Hypoxic-ischemic encephalopathy, Human embryonic stem cells, Cortical neuroblasts, Cell transplantation, Graft overgrowth, Neural circuit repair, Medicine (General), R5-920, Biochemistry, QD415-436
Abstract

Abstract Background Hypoxic-ischemic encephalopathy (HIE) is a major cause of neonatal disability and mortality. Although intensive studies and therapeutic approaches, there are limited restorative treatments till now. Human embryonic stem cell (hESCs)-derived cortical neural progenitors have shown great potentials in ischemic stroke in adult brain. However, it is unclear whether they are feasible for cortical reconstruction in immature brain with hypoxic-ischemic encephalopathy. Methods By using embryonic body (EB) neural differentiation method combined with DAPT pre-treatment and quantitative cell transplantation, human cortical neuroblasts were obtained and transplanted into the cortex of hypoxic-ischemic injured brain with different dosages 2 weeks after surgery. Then, immunostaining, whole-cell patch clamp recordings and behavioral testing were applied to explore the graft survival and proliferation, fate commitment of cortical neuroblasts in vitro, neural circuit reconstruction and the therapeutic effects of cortical neuroblasts in HIE brain. Results Transplantation of human cortical neural progenitor cells (hCNPs) in HIE-injured cortex exhibited long-term graft overgrowth. DAPT pre-treatment successfully synchronized hCNPs from different developmental stages (day 17, day 21, day 28) to deep layer cortical neuroblasts which survived well in HIE injured brain and greatly prevented graft overgrowth after transplantation. Importantly, the cortical neuroblasts primarily differentiated into deep-layer cortical neurons and extended long axons to their projection targets, such as the cortex, striatum, thalamus, and internal capsule in both ipsilateral and contralateral HIE-injured brain. The transplanted cortical neurons established synapses with host cortical neurons and exhibited spontaneous excitatory or inhibitory post-synaptic currents (sEPSCs or sIPSCs) five months post-transplantation. Rotarod and open field tests showed greatly improved animal behavior by intra-cortex transplantation of deep layer cortical neuroblasts in HIE injured brain. Conclusions Transplanted hESCs derived cortical neuroblasts survive, project to endogenous targets, and integrate into host cortical neural circuits to rescue animal behavior in the HIE-injured brain without graft overgrowth, providing a novel and safe cell replacement strategy for the future treatment of HIE.

Published
2024
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39. Proteomic analysis identified proteins that are differentially expressed in the flavonoid and carotenoid biosynthetic pathways of Camellia Nitidissima flowers

Author

Xing-Wen Zhou, Xiao-Xia Ye, Bao-Jian Ye, Shi-Hong Yan, Hai-Bin Hu, Qiu-Yuan Xu, Xiong Yao, He-Xia Liu, Bo Li, Yi-Qing Xie, and Zhong-Jian Liu

Subjects
Flower color, Camellia Nitidissima, Metabolites, Proteome, Metabolic pathway, Tandem mass tag (TMT), Botany, QK1-989
Abstract

Abstract Background Camellia nitidissima Chi is a popular ornamental plant because of its golden flowers, which contain flavonoids and carotenoids. To understand the regulatory mechanism of golden color formation, the metabolites of C. nitidissima petals at five different developmental stages were detected, a proteome map of petals was first constructed via tandem mass tag (TMT) analysis, and the accuracy of the sequencing data was validated via parallel reaction monitoring (PRM). Results Nineteen color components were detected, and most of these components were carotenoids that gradually accumulated, while some metabolites were flavonoids that were gradually depleted. A total of 97,647 spectra were obtained, and 6,789 quantifiable proteins were identified. Then, 1,319 differentially expressed proteins (DEPs) were found, 55 of which belong to the flavonoid and carotenoid pathways, as revealed by pairwise comparisons of protein expression levels across the five developmental stages. Notably, most DEPs involved in the synthesis of flavonoids, such as phenylalanine ammonium lyase and 4-coumarate-CoA ligase, were downregulated during petal development, whereas DEPs involved in carotenoid synthesis, such as phytoene synthase, 1-deoxy-D-xylulose-5-phosphate synthase, and β-cyclase, tended to be upregulated. Furthermore, protein‒protein interaction (PPI) network analysis revealed that these 55 DEPs formed two distinct PPI networks closely tied to the flavonoid and carotenoid synthesis pathways. Phytoene synthase and chalcone synthase exhibited extensive interactions with numerous other proteins and displayed high connectivity within the PPI networks, suggesting their pivotal biological functions in flavonoid and carotenoid biosynthesis. Conclusion Proteomic data on the flavonoid and carotenoid biosynthesis pathways were obtained, and the regulatory roles of the DEPs were analyzed, which provided a theoretical basis for further understanding the golden color formation mechanism of C. nitidissima.

Published
2024
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40. Anomalous scaling of branching tidal networks in global coastal wetlands and mudflats

Author

Fan Xu, Zeng Zhou, Sergio Fagherazzi, Andrea D’Alpaos, Ian Townend, Kun Zhao, Weiming Xie, Leicheng Guo, Xianye Wang, Zhong Peng, Zhicheng Yang, Chunpeng Chen, Guangcheng Cheng, Yuan Xu, and Qing He

Subjects
Science
Abstract

Abstract Branching networks are key elements in natural landscapes and have attracted sustained research interest across the geosciences and numerous intersecting fields. The prevailing consensus has long held that branching networks are optimized and exhibit fractal properties adhering to power-law scaling relationships. However, tidal networks in coastal wetlands and mudflats exhibit scaling properties that defy conventional power-law descriptions, presenting a longstanding enigma. Here we show that the observed atypical scaling represents a universal deviation from an ideal fractal branching network capable of fully occupying the available space. Using satellite imagery of tidal networks from diverse global locations, we identified an inherent “laziness” in this deviation—where the increased ease of channel formation paradoxically decreases the space-filling efficiency of the network. We developed a theoretical model that reproduces the ideal fractal branching network and the laziness phenomenon. The model suggests that branching networks can emerge under a localized competition principle without adhering to conventionally assumed optimization-driven processes. These results reveal the dual nature of branching networks, where “laziness” complements the well-known optimization process. This property provides more flexible strategies for controlling tidal network morphogenesis, with implications for coastal management, wetland restoration, and studies in fluvial and planetary systems.

Published
2024
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41. Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports

Author

Ken Cheligeer, Guosong Wu, Alison Laws, May Lynn Quan, Andrea Li, Anne-Marie Brisson, Jason Xie, and Yuan Xu

Subjects
Large Language Models (LLMs), Breast cancer, Machine learning in healthcare, Natural language processing, Clinical pathology information extraction, Pathologic Complete Response (pCR), Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Aims The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies. Methods The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system’s computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports. Results In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0–100.0%), a positive predictive value of 90.9% (95%CI: 76.5–100.0%), and an F1 score of 93.0% (95%CI: 83.7–100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction. Conclusions The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.

Published
2024
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42. Longitudinal associations of dietary fiber and its source with 48-week weight loss maintenance, cardiometabolic risk factors and glycemic status under metformin or acarbose treatment: a secondary analysis of the March randomized trial

Author

Jia Liu, Yu An, Ning Yang, Yuan Xu, and Guang Wang

Subjects
Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Aims To examine longitudinal and dose-d ependent associations between dietary fiber intake and various clinical outcomes over 48 weeks of pharmacological treatment in T2DM patients. Methods In this secondary analysis, we used data from the MARCH trial, which was designed to compare the efficacy of acarbose or metformin monotherapy as the initial therapy in Chinese patients newly diagnosed with T2DM. Dietary data were obtained using a 24-h dietary recall method to evaluate the intakes of dietary fiber from different sources as well as the carbohydrate-to-fiber ratio. Results A total of 551 newly-diagnosed patients with T2DM complete dietary records (286 in the acarbose group and 265 in the metformin group) were included. Higher intake of total fiber and whole grain fiber was positively associated with better β‐cell function, insulin sensitivity and postprandial glycemic control under acarbose treatment. Higher intake of legume fiber was associated with better glycemic control under both acarbose and metformin treatment but with better weight loss only under metformin treatment. A high-carbohydrate–low-fiber diet was associated with worse glycemic control and lower HDL-C under acarbose treatment but with higher insulin sensitivity and better weight loss under metformin treatment. Conclusions The notable effects of various dietary fibers when combined with different oral glucose-lowering medications should be considered to maximize therapeutic benefit.

Published
2024
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43. Super multiple primary lung cancers harbor high-frequency BRAF and low-frequency EGFR mutations in the MAPK pathway

Author

Haochen Li, Zhicheng Huang, Chao Guo, Yadong Wang, Bowen Li, Sha Wang, Na Bai, Hanlin Chen, Jianchao Xue, Daoyun Wang, Zhibo Zheng, Zhongxing Bing, Yang Song, Yuan Xu, Guanghua Huang, Xiaoqing Yu, Ruirui Li, Ka Luk Fung, Ji Li, Lan Song, Ziwei Zhu, Songtao Liu, Naixin Liang, and Shanqing Li

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract The incidence of multiple primary lung cancer (MPLC) is increasing, with some of our surgical patients exhibiting numerous lesions. We defined lung cancer with five or more primary lesions as super MPLCs. Elucidating the genomic characteristics of this special MPLC subtype can help reduce disease burden and understand tumor evolution. In our cohort of synchronous super early-stage MPLCs (PUMCH-ssesMPLC), whole-exome sequencing on 130 resected malignant specimens from 18 patients provided comprehensive super-MPLC genomic landscapes. Mutations are enriched in PI3k-Akt and MAPK pathways. Their BRAF mutation frequency (31.5%) is significantly higher than MPLC with fewer lesions and early-stage single-lesion cancer, while EGFR mutations are significantly fewer (13.8%). As lesion counts increase, BRAF mutations gradually become dominant. Also, invasive lesions more tend to have classic super-MPLC mutation patterns. High-frequency BRAF mutations, especially Class II, and low-frequency EGFR mutations could be a reason for the limited effectiveness of targeted therapy in super-MPLC patients.

Published
2024
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44. A deep learning-based dose calculation method for volumetric modulated arc therapy

Author

Bin Liang, Wenlong Xia, Ran Wei, Yuan Xu, Zhiqiang Liu, and Jianrong Dai

Subjects
Deep learning, Dose calculation, VMAT, Planning optimization, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Volumetric modulated arc therapy (VMAT) planning optimization involves iterative adjustment of numerous parameters, and hence requires repeatedly dose recalculation. In this study, we used the deep learning method to develop a fast and accurate dose calculation method for VMAT. Methods The classical 3D UNet was adopted and trained to learn the physics principle of dose calculation. The inputs included the projected fluence map (FM), computed tomography (CT) images, the radiological depth and the source-to-voxel distance (SVD). The projected FM was generated by projecting the accumulated FM between two consecutive control points (CPs) onto the patient’s anatomy. The accumulated FM was calculated by simulating the movement of the multi-leaf collimator (MLC) from one CP to the next. The dose, calculated by the treatment planning system (TPS), was used as ground truth. 51 head and neck VMAT plans were used, with 43, 1 and 7 cases as training, validation, and testing datasets, respectively. Correspondingly, 7182, 180 and 1260 CP samples were included in the training, validation, and testing datasets. Results This presented method was evaluated by comparing the derived dose distribution to the TPS calculated dose distribution. The dose profiles coincided for both the single CP and the entire plan (summation of all CPs). But the network derived dose was smoother than the TPS calculated dose. Gamma analysis was performed between the network derived dose and the TPS calculated dose. The average gamma pass rate was 96.56%, 98.75%, 98.03% and 99.30% under the criteria of 2% (tolerance) -2 mm (distance to agreement, DTA). 2%-3 mm, 3%-2 mm and 3%-3 mm. No significant difference was observed on the critical indices including the max, mean dose, and the relative volume covered by the 2000 cGy, 4000 cGy and the prescription dose. For one CP, the average computational time of the network and TPS was 0.09s and 0.53s. And for one patient, the average time was 16.51s and 95.60s. Conclusion The dose distribution derived by the network showed good agreement with the TPS calculated dose distribution. The computational time was reduced to approximate one-sixth of its original duration. Therefore the presented deep learning-based dose calculation method has the potential to be used for planning optimization.

Published
2024
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45. Characterization of the complete chloroplast genome sequence of Artemisia sylvatica Maximowicz 1859 (Asteraceae)

Author

Qiaoyu Zhang, Zelong Yu, Chunsheng Wang, Yingli Zhang, Bailing Miao, Yuan Xu, and Qiong Chen

Subjects
Artemisia sylvatica, chloroplast genome, phylogenetic analyses, Genetics, QH426-470
Abstract

Artemisia sylvatica Maximowicz 1859 is one of the medicinal herbs in Artemisia. This study presents the complete chloroplast genome of A. sylvatica, sequenced using the Illumina NovaSeq platform. The genome is 151,161 bp in length, featuring a GC content of 38%. It consists of a large single-copy (LSC) region of 82,892 bp, a small single-copy (SSC) region of 18,353 bp, and two inverted repeat (IR) regions of 24,958 bp each. In total, the genome contains 132 genes, including 87 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Phylogenetic analysis positions A. sylvatica within the subgenus Artemisia, highlighting its evolutionary relationships within this diverse genus. The first chloroplast genome of A. sylvatica was reported in this work contributes to the enrichment of genomic data for the genus Artemisia.

Published
2024
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46. Maesa flabellifera (Primulaceae), a new species from southeast Yunnan, China

Author

Dan Wei, Yuan Xu, Gang Hao, and Timothy M. A. Utteridge

Subjects
Botany, QK1-989
Abstract

Maesa flabellifera (Primulaceae-Maesoideae) from southeast Yunnan, China, is described and illustrated here. This new species belongs to the informal long corolla-tube species group and is morphologically similar to M. permollis and M. kurzii, but can be distinguished by lacking hairs, membranaceous leaves and long panicles with 7−16 branches. According to the IUCN criteria, M. flabellifera is assessed as “Least Concern”.

Published
2024
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