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Genome-Wide Silencer Screening Reveals key Silencer Modulating Reprogramming Efficiency in Mouse Induced Pluripotent Stem Cells

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Advanced Science 2025年第0期 e2408839页

作者： Zhu, Xiusheng Huang, Lei Li, Guoli Deng, Biao Wang, Xiaoxiao Yang, Hu Zhang, Yuanyuan Wen, Qiuhan Wang, Chao Zhang, Jingshu Zhao, Yunxiang Li, Kui Liu, Yuwen Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Key Laboratory of Livestock and Poultry Multi-omics of MARA Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518124 China State Key Laboratory of Genetic Resources and Evolution Yunnan Laboratory of Molecular Biology of Domestic Animals Kunming Institute of Zoology Chinese Academy of Sciences Kunming650201 China Kunming College of Life Science University of Chinese Academy of Sciences Kunming650204 China Guangxi Key Laboratory of Animal Breeding Disease Control and Prevention College of Animal Science and Technology Guangxi University Nanning530004 China

The majority of the mouse genome is composed of non-coding regions, which harbor numerous regulatory sequences essential for gene regulation. While extensive research focuses on enhancers that activate gene expression, the role of silencers that repress gene expression remains less explored. In this study, the first genome-wide identification of silencers in the mouse genome is conducted. In mouse embryonic fibroblasts (MEFs) and embryonic stem cells (mESCs), 89 596 and 115 165 silencers are identified, respectively. These silencers are ubiquitously distributed across the genome and are predominantly associated with low-expression genes. Additionally, these silencers are mainly cell-specific and function by binding to repressive transcription factors (TFs). Further, these silencers are notably enriched with the histone modification H3K9me3. It is observed that the transformation between dual-function silencers and enhancers is correlated with intracellular transcription factor concentrations, accompanied by changes in epigenetic modifications. In terms of biological effects, we have identified silencers that can enhance the induction efficiency of MEFs and influence the pluripotency of mESCs. Collectively, this work offers the first comprehensive silencer landscape in the mouse genome and provides strong evidence for the role of silencers in the induction of induced pluripotent stem cells (iPSCs). © 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH.

关键词： Mammals

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Experimental Realization of Criticality-Enhanced Global Quantum Sensing via Non-Equilibrium Dynamics

arXiv

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arXiv 2025年

作者： Yu, Yefei Liu, Ran Xue, Guangming Yang, Chuhong Wang, Chenlu Zhang, Jingning Cui, Jiangyu Yang, Xiaodong Li, Jun Han, Jiaxiu Yu, Haifeng Beijing Academy of Quantum Information Sciences Beijing100193 China Institute of Quantum Precision Measurement State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Hefei National Laboratory Hefei230088 China Department of Modern Physics University of Science and Technology of China Hefei230026 China

Quantum critical systems offer promising advancements in quantum sensing and metrology, yet face limitations like critical slowing down and a restricted criticality-enhanced region. Here, we introduce a critical sensing scheme that mitigate critical slowing down by leveraging the non-equilibrium dynamics of a perturbed Ising spin model, coupled with an adaptive strategy to enlarge its sensing interval. We validate the proposed scheme on a superconducting quantum processor and demonstrate that our scheme achieves a Heisenberg scaling with respect to the encoding duration. Additionally, the adaptive strategy tunes the model to operate near its critical point with limited prior information about the parameter, enabling what is known as global sensing. Our work showcases the metrological applications empowered by non-equilibrium critical dynamics and hence opens up a pathway for devising critical quantum sensors. © 2025, CC BY.

关键词： Dynamics

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Optical attenuation coefficient based optical coherence tomography angiography

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Optics Communications 2025年 586卷

作者： Xiang, Ben Ding, Ning Jiang, Huiwen Liu, Jian Yu, Yao Luan, Jingmin Zhao, Yuqian Wang, Yi Yang, Yanqiu Wang, Fengwen Ma, Zhenhe School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China College of Information Science and Engineering Northeastern University Shenyang110819 China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

In this study, we reported an optical coherence tomography angiography (OCTA) method based on optical attenuation coefficients (OAC), called OAC-based decorrelation (OACD), for mapping high-quality microvascular images. OACD utilizes the OAC instead of amplitude or phase to calculate flow signals, so OACD can intrinsically suppress noise arising from phase errors and intensity variations in OCT signals caused by the blood flow in superficial vasculature. The performance of the newly proposed algorithm is tested against previously reported OCTA algorithms (i.e., split-spectrum amplitude and phase-gradient angiography (SSAPGA) and principal component analysis (PCA)). Compared to SSAGPA and PCA, OACD can provide enhanced motion contrast in phantoms and in vivo experiments, facilitating better differentiation between vessels and static tissue. Benefiting from these advantages, the vascular images processed by the OACD method exhibit increased vascular visibility and connectivity, enhancing overall image quality. Our method holds promise for disease diagnosis and monitoring. © 2025 Elsevier B.V.

关键词： Optical coherence tomography

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PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency

arXiv

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arXiv 2025年

作者： Dong, Xintong Yuan, Zhengyi Lin, Jun Dong, Shiqi Tong, Xunqian Li, Yue College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Northeast Electric Power University Jilin132012 China College of Communication Engineering Jilin University Changchun130012 China

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network’s output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Stratigraphy

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CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models

CoF: Coarse to Fine-Grained Image Understanding for Multi-mo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Yeyuan Gao, Dehong Li, Bin Long, Rujiao Yi, Lei Cai, Xiaoyan Yang, Libin Zhang, Jinxia Yu, Shanqing Xuan, Qi School of Automation Northwestern Polytechnical University Shaanxi Xi'an China School of Cybersecurity Northwestern Polytechnical University Shaanxi Xi'an China Alibaba Group Zhejiang Hangzhou China The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China Advanced Ocean Institute Southeast University Nantong226010 China Zhejiang University of Technology Zhejiang Hangzhou China Binjiang Institute of Artificial Intelligence Zhejiang Hangzhou China

ISBN: (纸本)9798350368741

The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks. However, current MLLM often struggles to effectively address fine-grained multi-modal challenges. We argue that this limitation is closely linked to the models' visual grounding capabilities. The restricted spatial awareness and perceptual acuity of visual encoders frequently lead to interference from irrelevant background information in images, causing the models to overlook subtle but crucial details. As a result, achieving fine-grained regional visual comprehension becomes difficult. In this paper, we break down multi-modal understanding into two stages, from Coarse to Fine (CoF). In the first stage, we prompt the MLLM to locate the approximate area of the answer. In the second stage, we further enhance the model's focus on relevant areas within the image through visual prompt engineering, adjusting attention weights of pertinent regions. This, in turn, improves both visual grounding and overall performance in downstream tasks. Our experiments show that this approach significantly boosts the performance of baseline models, demonstrating notable generalization and effectiveness. Our CoF approach is available online at https://***/Gavin001201/CoF. © 2025 IEEE.

关键词： fine-grained understanding prompt engineering vision and language

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MSEvoNet: Multi-scale spatiotemporal evolution networks for remaining useful life prediction in complex industrial systems

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IEEE Transactions on Instrumentation and measurement 2025年 74卷

作者： Huang, Kai Jia, Guozhu Jiao, Zeyu Zhang, Yingying Bai, Tian Cai, Yingjie Beihang University School of Economics and Management Beijing100191 China Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangzhou510070 China Civil Aviation Flight University of China Flight Technology College Deyang618301 China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong999077 Hong Kong

This paper introduces MSEvoNet, a multiscale spatiotemporal evolution network that analyzes complex industrial systems' dynamic changes during service, focusing on predicting the remaining useful life (RUL). By integrating a gated dilated convolution module, an adaptive relationship perception module, and a diffusion graph convolution module in a step-by-step stacking approach, MSEvoNet effectively enhances heterogeneous data management, spatiotemporal fusion and model interpretability in complex systems, providing deeper insights into industrial system degradation. The adopted spatiotemporal graph structure portrayed the evolutionary process of complex industrial systems, depicting the intricate interactions and influences among spatiotemporal variables during degradation and the intrinsic conduits governing RUL factors, thereby constructing an eXplainable Artificial Intelligence (XAI) model for complex industrial systems. Validated with the C-MAPSS and N-CMAPSS datasets, representing complex aerospace engines' systems, MSEvoNet demonstrated robustness and adaptability across diverse operational scenarios, significantly advancing the potential for RUL prediction. The findings underscore the importance of leveraging XAI and multi-scale spatiotemporal insights for RUL prediction, offering a pathway towards more reliable and efficient prognostics for complex industrial systems in the Industry 4.0 and 5.0 era. © 2025 IEEE.

关键词： Large scale systems

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Synchronous Estimation Scheme of PMSM Mechanical Parameters: A Decoupled Extended Sliding Mode Observer

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IEEE Transactions on Power Electronics 2025年

作者： Chang, He Wang, Zuo Yang, Jun Li, Shihua Lu, Shaowu Southeast University School of Automatin Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of CSE Nanjing210096 China Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

In the permanent magnet synchronous motor drives (PMSM), high-quality controller optimization and condition monitoring rely heavily on accurate knowledge of mechanical parameters. To address this need, this study presents a novel decoupled extended sliding mode observer (DESMO) capable of achieving high-quality synchronous estimation of the moment of inertia and the load disturbance. An extended state-space equation is, firstly, constructed by extending the mechanical equations. Subsequently, the novel DESMO is designed by introducing two new recursive equations. On this basis, the excitation weight on the moment of inertia is analyzed, resulting in the derivation of Lyapunov-mechanism-based decoupling updating conditions (DOCs) to further exploit the DESMO's estimation potential. Furthermore, when the DOCs are not all satisfied, the second observation equation is derived with the reaching law of the moment of inertia set to zero to achieve state decoupling during estimation process. With the switching excitation of DOCs, notably, the DESMO is capable of serving as a more competitive candidate for observing multi-mechanical parameters, as demonstrated through simulation and experimental results. © 1986-2012 IEEE.

关键词： Permanent magnets

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

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仪器仪表学报 2014年第S2期35卷 210-214页

作者： Wang Hongjun Xu Xiaoli Rosen B G Key Laboratory of Modern Measurement & Control Technology Beijing Information Science & Technology University School of Mechanic and Electric Engineering Beijing Information Science&Technology University Halmstad University

Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

关键词： fault diagnosis multi-manifold learning particle swarm optimization support vector machine

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An improved ICEEMDAN-MPA-GRU model for GNSS height time series prediction with weighted quality evaluation index

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GPS Solutions 2025年第3期29卷 1-19页

作者： Zhou, Yu He, Xiaoxing Montillet, Jean-Philippe Wang, Shengdao Hu, Shunqiang Sun, Xiwen Huang, Jiahui Ma, Xiaping Jiangxi Province Key Laboratory of Water Ecological Conservation in Headwater Regions (2023SSY02031) Jiangxi University of Science and Technology Ganzhou China Institute Dom Luiz University of Beira Interior Covilhã Portugal Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC) Davos Switzerland Division of Geodetic Science School of Earth Sciences The Ohio State University Columbus Columbus USA Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang China School of Geophysics and Measurement-control Technology East China University of Technology Nanchang China College of Geomatics Xi’an University of Science and Technology Xi’an China

High-precision GNSS height time series prediction can provide a critical reference for applications such as regional or global reference frame maintenance, crustal movements, structural safety monitoring, and geological hazards. An improved ICEEMDAN-MPA-GRU model has been developed to achieve high-precision prediction results. The Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is used to decompose the original time series, and subsequently the Gated Recurrent Unit (GRU) is employed for the forecast of the time series. Marine Predators Algorithm (MPA) is also included to optimize the parameters of the algorithms (e.g., number of neurons, learning rate, and iteration count). The ICEEMDAN-MPA-GRU model is applied to the daily height coordinate (Up component) of 14 GNSS stations in North American with time span of 20 years (2000.0–2020.0). A comprehensive evaluation was performed using the proposed Weighted Quality Evaluation index (WQE). The ICEEMDAN-MPA-GRU model achieved an average WQE of 0.463. Compared to the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), MPA-GRU, and ICEEMDAN-MPA-LSTM models, the ICEEMDAN-MPA-GRU model reduced the prediction WQE by an average of 63.77%, 58.47%, 54.20%, and 30.09%, respectively. Furthermore, the ICEEMDAN-MPA-GRU model demonstrated strong robustness in the presence of data gaps.

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