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SnoRNAs: The promising targets for anti-tumor therapy

Journal of Pharmaceutical Analysis 2024年第11期14卷 1588-1602页

作者： Xiaoyun Hu Wanlin Cui Min Liu Fangxiao Zhang Yingqi Zhao Mingrong Zhang Yuhang Yin Yalun Li Ying Che Xianglong Zhu Yuxuan Fan Xiaolan Deng Minjie Wei Huizhe Wu Department of Pharmacology School of PharmacyChina Medical UniversityShenyang110122China Liaoning Key Laboratory of Molecular Targeted Anti-tumor Drug Development and Evaluation Liaoning Cancer Immune Peptide Drug Engineering Technology Research CenterKey Laboratory of Precision Diagnosis and Treatment of Gastrointestinal TumorsMinistry of EducationChina Medical UniversityShenyang110122China Scientific Experimental Center School of PharmacyChina Medical UniversityShenyang110122China The Second Department of Infectious Diseases The First Affiliated Hospital of China Medical UniversityShenyang110001China Department of Anorectal Surgery The First Affiliated Hospital of China Medical UniversityShenyang110001China Department of Systems Biology Beckman Research Institute of City of HopeMonroviaCA91016USA Shenyang Kangwei Medical Laboratory Analysis Co. Ltd.Shenyang110000China

Recently,small nucleolar RNAs(snoRNAs)have transcended the genomic“noise”to emerge as pivotal molecular markers due to their essential roles in tumor *** evidence indicates a strong association between snoRNAs and critical clinical features such as tumor pathology and drug ***,snoRNA research has concentrated on two classical mechanisms:2'-O-ribose methylation and *** review specifically summarizes the novel regulatory mechanisms and functional patterns of snoRNAs in tumors,encompassing transcriptional,post-transcriptional,and post-translational *** further discuss the synergistic effect between snoRNA host genes(SNHGs)and snoRNAs in tumor *** importantly,snoRNAs extensively contribute to the development of tumor cell resistance as oncogenes or tumor suppressor ***,we provide a comprehensive review of the clinical diagnosis and treatment associated with snoRNAs and explore their significant potential as novel drug targets.

关键词： snoRNA Cancer snoRNA host gene Multidrug resistance Targeted therapy Tumor immune microenvironment

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Long-Lived Triplet Charge Separated State and Thermally Activated Delayed Fluorescence in a Compact Orthogonal Anthraquinone-Phenothiazine Electron Donor-Acceptor Dyad

引用

Journal of Physical Chemistry Letters 2022年第11期13卷 2533-2539页

作者： Zhao, Xiaoyu Sukhanov, Andrey A. Jiang, Xiao Zhao, Jianzhang Voronkova, Violeta K. State Key Laboratory of Fine Chemicals School of Chemical Engineering Dalian University of Technology Dalian 116024 China State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources College of Chemistry Xinjiang University Urumqi 830017 China Zavoisky Physical-Technical Institute FRC Kazan Scientific Center of Russian Academy of Sciences Kazan 420029 Russian Federation Key Laboratory of Industrial Ecology and Environmental Engineering Ministry of Education School of Environmental Science and Technology Dalian University of Technology Dalian 116024 China

A long-lived triplet charge separated state (3CS state lifetime: 0.56 μs) was observed in a compact electron donor-acceptor dyad with electron donor phenothiazine (PTZ) and acceptor anthraquinone (AQ) directly connec... 详细信息

A long-lived triplet charge separated state (³CS state lifetime: 0.56 μs) was observed in a compact electron donor-acceptor dyad with electron donor phenothiazine (PTZ) and acceptor anthraquinone (AQ) directly connected by a single C-N bond (AQ-PTZ). The ¹CS state energy (2.0 eV in cyclohexane) is lower than those of the ³AQ (2.7 eV) or the ³PTZ state (2.6 eV). By oxidation of the PTZ unit, thus increasing of the ¹CS state energy (2.7 eV in cyclohexane), thermally activated delayed fluorescence (TADF) was observed [τ = 17.7 ns (99.9%)/1.5 μs (0.1%)]. Time-resolved electron paramagnetic resonance (TREPR) spectra confirm the electron spin multiplicity of the ³CS state, and the zero-field-splitting (ZFS) parameters |D| and |E| are 48.2 mT and 11.2 mT, respectively. These results are useful for design of compact electron donor-acceptor dyads to access the long-lived ³CS state and study the TADF mechanism. © 2022 American Chemical Society. All rights reserved.

关键词： Electrical energy Energy Quantum mechanics Absorption Fluorescence

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Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling

arXiv

引用

arXiv 2024年

作者： Huang, Sili Hu, Jifeng Yang, Zhejian Yang, Liwei Luo, Tao Chen, Hechang Sun, Lichao Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China School of Artificial Intelligence Jilin University China Institute of High Performance Computing A*STAR Singapore Lehigh University BethlehemPA United States

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to the quadratic computation complexity of attention in transformers, current in-context RL methods suffer from huge computational costs as the task horizon increases. In contrast, the Mamba model is renowned for its efficient ability to process long-term dependencies, which provides an opportunity for in-context RL to solve tasks that require long-term memory. To this end, we first implement Decision Mamba (DM) by replacing the backbone of Decision Transformer (DT). Then, we propose a Decision Mamba-Hybrid (DM-H) with the merits of transformers and Mamba in high-quality prediction and long-term memory. Specifically, DM-H first generates high-value sub-goals from long-term memory through the Mamba model. Then, we use sub-goals to prompt the transformer, establishing high-quality predictions. Experimental results demonstrate that DM-H achieves state-of-the-art in long and short-term tasks, such as D4RL, Grid World, and Tmaze benchmarks. Regarding efficiency, the online testing of DM-H in the long-term task is 28× times faster than the transformer-based baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

arXiv

引用

arXiv 2024年

作者： Zhang, Quan Wang, Lei Patel, Vishal M. Xie, Xiaohua Lai, Jianhuang School of Computer Science and Engineering Sun Yat-Sen University China Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Electrical and Computer Engineering Johns Hopkins University United States

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID. © 2024, CC BY.

关键词： Antennas

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From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning

arXiv

引用

arXiv 2025年

作者： Li, Ziang Zhang, Hongguang Wang, Juan Chen, Meihui Hu, Hongxin Yi, Wenzhe Xu, Xiaoyang Yang, Mengda Ma, Chenjun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Shanghai Innovation Institute China Ant Group China Department of Computer Science and Engineering University at Buffalo SUNY United States

Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs necessitate an impractical number of queries, incurring significant overhead. Therefore, we analyze the limitations of existing MIAs and introduce Surrogate Model-based Inversion with Long-tailed Enhancement (SMILE), a high-resolution oriented and query-efficient MIA for the black-box setting. We begin by analyzing the initialization of MIAs from a data distribution perspective and propose a long-tailed surrogate training method to obtain high-quality initial points. We then enhance the attack’s effectiveness by employing the gradient-free black-box optimization algorithm selected by NGOpt. Our experiments show that SMILE outperforms existing state-of-the-art black-box MIAs while requiring only about 5% of the query overhead. Our code is available at https://***/L1ziang/SMILE. Copyright © 2025, The Authors. All rights reserved.

关键词： Face recognition

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TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition

arXiv

引用

arXiv 2023年

作者： Liu, Jiang Fei, Hao Li, Fei Li, Jingye Li, Bobo Zhao, Liang Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China School of Computing National University of Singapore Singapore University of São Paulo Brazil

Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions. Effectively transferring the internal or external resources thus becomes the key to few-shot NER. While the existing prompt tuning methods have shown remarkable few-shot performances, they still fail to make full use of knowledge. In this work, we investigate the integration of rich knowledge to prompt tuning for stronger few-shot NER. We propose incorporating the deep prompt tuning framework with threefold knowledge (namely TKDP), including the internal 1) context knowledge and the external 2) label knowledge & 3) sememe knowledge. TKDP encodes the three feature sources and incorporates them into the soft prompt embeddings, which are further injected into an existing pre-trained language model to facilitate predictions. On five benchmark datasets, our knowledge-enriched model boosts by at most 11.53% F1 over the raw deep prompt method, and significantly outperforms 8 strong-performing baseline systems in 5-/10-/20-shot settings, showing great potential in few-shot NER. Our TKDP can be broadly adapted to other few-shot tasks without effort. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Full description of dipole orientation in organic light-emitting diodes

引用

Chinese Optics Letters 2023年第2期21卷 92-98页

作者：范灵杰赵茂雄楚娇沈唐尧郑敏嘉关放殷海玮石磊资剑 Department of Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and State Key Laboratory of Surface PhysicsFudan UniversityShanghai 200433China Shanghai Engineering Research Center of Optical Metrology for Nano-fabrication(SERCOM) Shanghai 200433China Institute for Nanoelectronic Devices and Quantum Computing Fudan UniversityShanghai 200438China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China

Considerable progress has been made in organic light-emitting diodes(OLEDs)to achieve high external quantum efficiency,among which dipole orientation has a remarkable *** most cases,the radiation of the dipoles in OLEDs is theoretically predicted with only one orientation parameter to match with corresponding ***,we develop a new theory with three orientation parameters to fully describe the relationship between dipole orientation and power ***,we design an optimal test structure for measuring all three orientation *** three orientation parameters could be retrieved from non-polarized *** theory provides a universal plot of dipole orientations in OLEDs,paving the way for designing more complicated OLED devices.

关键词： organic light-emitting diodes dipole orientation Fourier series expansion

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Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

引用

Computers, Materials & Continua 2021年第8期68卷 2579-2595页

作者： Jinxin Zuo Yueming Lu Hui Gao Tong Peng Ziyv Guo Tong An Enjie Liu School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China University of Bedfordshire Institute for Research in Applicable Computing(IRAC)LutonUK

With the skyrocketing development of technologies,there are many issues in information security quantitative evaluation(ISQE)of complex heterogeneous information systems(CHISs).The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation *** this paper,we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE *** evaluation model establishes a framework for ISQE of CHISs that are updated ***,with the support of asset identification and topology data,we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components(set)of the ***,we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the ***,we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed *** results show the advantages of the proposed algorithm in the evaluation of CHISs.

关键词： Complex heterogeneous information system security-critical component threat attack tree information security quantitative evaluation

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Federated Graph Learning for Cross-Domain Recommendation

arXiv

引用

arXiv 2024年

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. © 2024, CC BY.

关键词： Federated learning

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CHECKWHY: Causal Fact Verification via Argument Structure

arXiv

引用

arXiv 2024年

作者： Si, Jiasheng Zhao, Yibo Zhu, Yingjie Zhu, Haiyang Lu, Wenpeng Zhou, Deyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K "why" claim-evidence-argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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