Search

Your search keyword '"Li, Bingchen"' showing total 24 results
Start Over Author "Li, Bingchen" Publication Year Range This year
24 results on '"Li, Bingchen"'

1. MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs

Author

Ren, Yulin, Li, Xin, Guo, Mengxi, Li, Bingchen, Zhao, Shijie, and Chen, Zhibo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present MambaCSR, a simple but effective framework based on Mamba for the challenging compressed image super-resolution (CSR) task. Particularly, the scanning strategies of Mamba are crucial for effective contextual knowledge modeling in the restoration process despite it relying on selective state space modeling for all tokens. In this work, we propose an efficient dual-interleaved scanning paradigm (DIS) for CSR, which is composed of two scanning strategies: (i) hierarchical interleaved scanning is designed to comprehensively capture and utilize the most potential contextual information within an image by simultaneously taking advantage of the local window-based and sequential scanning methods; (ii) horizontal-to-vertical interleaved scanning is proposed to reduce the computational cost by leaving the redundancy between the scanning of different directions. To overcome the non-uniform compression artifacts, we also propose position-aligned cross-scale scanning to model multi-scale contextual information. Experimental results on multiple benchmarks have shown the great performance of our MambaCSR in the compressed image super-resolution task. The code will be soon available in~\textcolor{magenta}{\url{https://github.com/renyulin-f/MambaCSR}}.

Published
2024

2. UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt

Author

Li, Xin, Li, Bingchen, Jin, Yeying, Lan, Cuiling, Zhu, Hanxin, Ren, Yulin, and Chen, Zhibo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Compressed Image Super-resolution (CSR) aims to simultaneously super-resolve the compressed images and tackle the challenging hybrid distortions caused by compression. However, existing works on CSR usually focuses on a single compression codec, i.e., JPEG, ignoring the diverse traditional or learning-based codecs in the practical application, e.g., HEVC, VVC, HIFIC, etc. In this work, we propose the first universal CSR framework, dubbed UCIP, with dynamic prompt learning, intending to jointly support the CSR distortions of any compression codecs/modes. Particularly, an efficient dynamic prompt strategy is proposed to mine the content/spatial-aware task-adaptive contextual information for the universal CSR task, using only a small amount of prompts with spatial size 1x1. To simplify contextual information mining, we introduce the novel MLP-like framework backbone for our UCIP by adapting the Active Token Mixer (ATM) to CSR tasks for the first time, where the global information modeling is only taken in horizontal and vertical directions with offset prediction. We also build an all-in-one benchmark dataset for the CSR task by collecting the datasets with the popular 6 diverse traditional and learning-based codecs, including JPEG, HEVC, VVC, HIFIC, etc., resulting in 23 common degradations. Extensive experiments have shown the consistent and excellent performance of our UCIP on universal CSR tasks. The project can be found in https://lixinustc.github.io/UCIP.github.io, Comment: Accepted by ECCV 2024

Published
2024

3. MoE-DiffIR: Task-customized Diffusion Priors for Universal Compressed Image Restoration

Author

Ren, Yulin, Li, Xin, Li, Bingchen, Wang, Xingrui, Guo, Mengxi, Zhao, Shijie, Zhang, Li, and Chen, Zhibo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present MoE-DiffIR, an innovative universal compressed image restoration (CIR) method with task-customized diffusion priors. This intends to handle two pivotal challenges in the existing CIR methods: (i) lacking adaptability and universality for different image codecs, e.g., JPEG and WebP; (ii) poor texture generation capability, particularly at low bitrates. Specifically, our MoE-DiffIR develops the powerful mixture-of-experts (MoE) prompt module, where some basic prompts cooperate to excavate the task-customized diffusion priors from Stable Diffusion (SD) for each compression task. Moreover, the degradation-aware routing mechanism is proposed to enable the flexible assignment of basic prompts. To activate and reuse the cross-modality generation prior of SD, we design the visual-to-text adapter for MoE-DiffIR, which aims to adapt the embedding of low-quality images from the visual domain to the textual domain as the textual guidance for SD, enabling more consistent and reasonable texture generation. We also construct one comprehensive benchmark dataset for universal CIR, covering 21 types of degradations from 7 popular traditional and learned codecs. Extensive experiments on universal CIR have demonstrated the excellent robustness and texture restoration capability of our proposed MoE-DiffIR. The project can be found at https://renyulin-f.github.io/MoE-DiffIR.github.io/., Comment: Accepted by ECCV 2024

Published
2024

4. PromptCIR: Blind Compressed Image Restoration with Prompt Learning

Author

Li, Bingchen, Li, Xin, Lu, Yiting, Feng, Ruoyu, Guo, Mengxi, Zhao, Shijie, Zhang, Li, and Chen, Zhibo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Blind Compressed Image Restoration (CIR) has garnered significant attention due to its practical applications. It aims to mitigate compression artifacts caused by unknown quality factors, particularly with JPEG codecs. Existing works on blind CIR often seek assistance from a quality factor prediction network to facilitate their network to restore compressed images. However, the predicted numerical quality factor lacks spatial information, preventing network adaptability toward image contents. Recent studies in prompt-learning-based image restoration have showcased the potential of prompts to generalize across varied degradation types and degrees. This motivated us to design a prompt-learning-based compressed image restoration network, dubbed PromptCIR, which can effectively restore images from various compress levels. Specifically, PromptCIR exploits prompts to encode compression information implicitly, where prompts directly interact with soft weights generated from image features, thus providing dynamic content-aware and distortion-aware guidance for the restoration process. The light-weight prompts enable our method to adapt to different compression levels, while introducing minimal parameter overhead. Overall, PromptCIR leverages the powerful transformer-based backbone with the dynamic prompt module to proficiently handle blind CIR tasks, winning first place in the NTIRE 2024 challenge of blind compressed image enhancement track. Extensive experiments have validated the effectiveness of our proposed PromptCIR. The code is available at https://github.com/lbc12345/PromptCIR-NTIRE24., Comment: Winner of NTIRE 2024 Blind Compressed Image Enhancement Challenge

Published
2024

5. Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising

Author

Hu, Jintong, Xia, Bin, Li, Bingchen, and Yang, Wenming

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep learning-based denoiser has been the focus of recent development on image denoising. In the past few years, there has been increasing interest in developing self-supervised denoising networks that only require noisy images, without the need for clean ground truth for training. However, a performance gap remains between current self-supervised methods and their supervised counterparts. Additionally, these methods commonly depend on assumptions about noise characteristics, thereby constraining their applicability in real-world scenarios. Inspired by the properties of the Frobenius norm expansion, we discover that incorporating a trace term reduces the optimization goal disparity between self-supervised and supervised methods, thereby enhancing the performance of self-supervised learning. To exploit this insight, we propose a trace-constraint loss function and design the low-trace adaptation Noise2Noise (LoTA-N2N) model that bridges the gap between self-supervised and supervised learning. Furthermore, we have discovered that several existing self-supervised denoising frameworks naturally fall within the proposed trace-constraint loss as subcases. Extensive experiments conducted on natural and confocal image datasets indicate that our method achieves state-of-the-art performance within the realm of zero-shot self-supervised image denoising approaches, without relying on any assumptions regarding the noise., Comment: 11pages, 6 figures

Published
2024

6. Is Vanilla MLP in Neural Radiance Field Enough for Few-shot View Synthesis?

Author

Zhu, Hanxin, He, Tianyu, Li, Xin, Li, Bingchen, and Chen, Zhibo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Neural Radiance Field (NeRF) has achieved superior performance for novel view synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a volume rendering procedure, however, when fewer known views are given (i.e., few-shot view synthesis), the model is prone to overfit the given views. To handle this issue, previous efforts have been made towards leveraging learned priors or introducing additional regularizations. In contrast, in this paper, we for the first time provide an orthogonal method from the perspective of network structure. Given the observation that trivially reducing the number of model parameters alleviates the overfitting issue, but at the cost of missing details, we propose the multi-input MLP (mi-MLP) that incorporates the inputs (i.e., location and viewing direction) of the vanilla MLP into each layer to prevent the overfitting issue without harming detailed synthesis. To further reduce the artifacts, we propose to model colors and volume density separately and present two regularization terms. Extensive experiments on multiple datasets demonstrate that: 1) although the proposed mi-MLP is easy to implement, it is surprisingly effective as it boosts the PSNR of the baseline from $14.73$ to $24.23$. 2) the overall framework achieves state-of-the-art results on a wide range of benchmarks. We will release the code upon publication., Comment: Accepted by CVPR 2024

Published
2024

7. SeD: Semantic-Aware Discriminator for Image Super-Resolution

Author

Li, Bingchen, Li, Xin, Zhu, Hanxin, Jin, Yeying, Feng, Ruoyu, Zhang, Zhizheng, and Chen, Zhibo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular, one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner. However, the distribution learning is overly coarse-grained, which is susceptible to virtual textures and causes counter-intuitive generation results. To mitigate this, we propose the simple and effective Semantic-aware Discriminator (denoted as SeD), which encourages the SR network to learn the fine-grained distributions by introducing the semantics of images as a condition. Concretely, we aim to excavate the semantics of images from a well-trained semantic extractor. Under different semantics, the discriminator is able to distinguish the real-fake images individually and adaptively, which guides the SR network to learn the more fine-grained semantic-aware textures. To obtain accurate and abundant semantics, we take full advantage of recently popular pretrained vision models (PVMs) with extensive datasets, and then incorporate its semantic features into the discriminator through a well-designed spatial cross-attention module. In this way, our proposed semantic-aware discriminator empowered the SR network to produce more photo-realistic and pleasing images. Extensive experiments on two typical tasks, i.e., SR and Real SR have demonstrated the effectiveness of our proposed methods., Comment: CVPR2024

Published
2024

12. Author Correction: A double-stranded RNA binding protein enhances drought resistance via protein phase separation in rice

Author

Wang, Huaijun, primary, Ye, Tiantian, additional, Guo, Zilong, additional, Yao, Yilong, additional, Tu, Haifu, additional, Wang, Pengfei, additional, Zhang, Yu, additional, Wang, Yao, additional, Li, Xiaokai, additional, Li, Bingchen, additional, Xiong, Haiyan, additional, Lai, Xuelei, additional, and Xiong, Lizhong, additional

Published
2024
Full Text
View/download PDF

17. Fast predicting the trajectory of chip-like particles by integrating a discrete element method model with a computational fluid dynamics-based aerodynamic database.

Author

Li, Bingchen, Lin, Junjie, Wang, Shuai, Luo, Kun, and Fan, Jianren

Abstract

The accumulation of ice within aircraft engines poses a significant safety concern, necessitating effective and accessible methods to predict ice particle shedding trajectories. This study develops a novel method by integrating the discrete element method with a computational fluid dynamics (CFD)-based aerodynamic database, aiming to accurately predict the trajectories of chip-like ice particles under various conditions. The accuracy of the CFD-based aerodynamic database is validated through a quantitative comparison with experimental data, and the predicted trajectories align well with the experimental trajectories under varied conditions following a database-independence analysis. The results indicate that aerodynamic coefficients are independent of both the relative velocity and the scaling factor (k) for chip-like particles. Moreover, the initial angle of attack significantly influences the translational and rotational dynamics of chip-like particles. Furthermore, the chip-like ice particles released closer to the engine inlet exhibit a more uniform distribution of landing points, whereas those released at longer distances from the engine inlet tend to converge toward the central area of the engine. The methodology developed in this paper is expected to be a promising tool for fast predicting the trajectories of chip-like particles, thereby enhancing engine protection against ice impacts and improving overall operational safety. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

18. Fluid‐Driven Particle Migration and Its Impact on Hydraulic Transmissivity of Stressed Filled Fractures.

Author

Tan, Jie, Li, Bingchen, Li, Changdong, Zhan, Hongbin, Zhou, Jia‐Qing, Tang, Yating, and Tang, Huiming

Subjects
*POROSITY, *STRESS fractures (Orthopedics), *ROCK properties, *FLUID flow, *PARTICLE dynamics, *DRAG force
Abstract

The accurate assessment of hydraulic transmissivity in rock fractures filled with particles is not only a scientific challenge but also a critical need for various industrial applications. However, the intricate dynamics of particle erosion and pore clogging that govern transmissivity evolution remain largely unexplored. In this study, we experimentally examine the fluid‐driven particle migration behavior in filled fractures and its consequent impact on fracture transmissivity under various hydraulic gradients, normal stresses, and fracture apertures. We find that escalating hydraulic gradients not only intensify particle erosion through amplified fluid drag forces and hydro‐mechanical coupling effects but also lead to an increase in the size of migrating particles, thereby augmenting pore clogging. The dynamics of erosion and clogging define four distinct migration phases within the filled fractures. Variations in normal stress and initial fracture aperture significantly alter the particle arrangement and the soil structure stability within the fractures, thereby modulating the progress of particle migration in response to hydraulic gradients. The pattern of particle migration in filled fractures dictates the development of the internal pore structure and normal deformation, ultimately affecting fracture transmissivity. We propose an empirical expression to encapsulate the comprehensive evolution of fracture transmissivity across different particle migration patterns. Our research advances the understanding of fluid‐driven particle migration within filled fractures and provides a practical tool for the precise determination of hydraulic properties of fractured rocks amidst complex geological settings. Plain Language Summary: Filled fractures in rock masses, serving as dominant flow channels and potential failure planes, are typically perceived to exhibit constant hydraulic transmissivity. However, this perspective oversimplifies the dynamic nature of filled fractures, where fluid flow can greatly change the hydraulic characteristics by either eroding or accumulating the internal particles. Understanding how these filling particles respond to water flow and affect fracture transmissivity is vital for many geological processes and human activities. Here, we experimentally uncover four distinct phases of particle migration within a filled fracture, instigated by the dynamics of erosion and clogging under elevated hydraulic gradients. Variations in normal stress and fracture aperture significantly influence the particle arrangement and the soil structure stability within fractures, thereby affecting particle migration characteristics across phases. On this basis, we delve into how the patterns of particle migration impact the geometric structure of the filled fracture and, thus, the fracture transmissivity. Finally, we quantitatively portray the evolution in fracture transmissivity across various particle migration scenarios using an empirical model. Our findings enhance the understanding of fluid‐driven particle migration in filled fractures and provide a path forward for accurately assessing the hydraulic properties of fractured rocks in complex geological environments. Key Points: Erosion and clogging dynamics, modulated by hydraulic gradients, define four particle migration phases in stressed‐filled fracturesParticle migration patterns dictate pore structure and normal deformation, impacting fracture transmissivityAn empirical model encapsulates fracture transmissivity evolution across different particle migration patterns [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

19. Effects of Land Use/Cover Change on Terrestrial Carbon Stocks in the Yellow River Basin of China from 2000 to 2030.

Author

Zhang, Jiejun, Yang, Jie, Liu, Pengfei, Liu, Yi, Zheng, Yiwen, Shen, Xiaoyu, Li, Bingchen, Song, Hongquan, and Liang, Zongzheng

Subjects
WATERSHEDS, LAND use, CARBON nanofibers, ECOSYSTEMS, CARBON, LAND management, REMOTE sensing
Abstract

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of "double carbon". In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

20. Optimal configuration of energy storage considering flexibility requirements and operational risks in a power system.

Author

Hui, Zijia, Yan, Huan, Li, Bingchen, He, Wenwen, Wu, Xiong, Fan, Hong, and Pan, Guangsheng

Subjects
OPERATIONAL risk, ENERGY storage, BILEVEL programming, ELECTRICAL load shedding, LINEAR programming, POWER resources
Abstract

The integration of renewable energy units into power systems brings a huge challenge to the flexible regulation ability. As an efficient and convenient flexible resource, energy storage systems (ESSs) have the advantages of fast-response characteristics and bi-directional power conversion, which can provide flexible support for the power system. This paper establishes an optimization model for the ESS based on a bi-level programming model. The upper-level model optimizes the decision strategy of ESS configuration planning. The lower-level model is based on scenario analysis theory to simulate the operation of typical daily scenarios. Flexibility requirement constraints are added to characterize the required flexibility resources of the power system. In addition, the conditional value-at-risk (CVaR) is applied to characterize the risk of wind curtailment and load shedding during operation. To simplify the model, a set of association constraints is introduced to convert the original bi-level programming model into a direct-solvable single-level mixed-integer linear programming (MILP) model. Finally, the effectiveness of the proposed model is verified through case studies. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

21. ZmWAK02 encoding an RD‐WAK protein confers maize resistance against gray leaf spot.

Author

Dai, Zhikang, Pi, Qianyu, Liu, Yutong, Hu, Long, Li, Bingchen, Zhang, Bao, Wang, Yanbo, Jiang, Min, Qi, Xin, Li, Wenqiang, Gui, Songtao, Llaca, Victor, Fengler, Kevin, Thatcher, Shawn, Li, Ziwei, Liu, Xiangguo, Fan, Xingming, and Lai, Zhibing

Subjects
LEAF spots, CORN, CORN breeding, MOLECULAR cloning, PHENOTYPIC plasticity, GRAIN yields, CORN diseases
Abstract

Summary: Gray leaf spot (GLS) caused by Cercospora zeina or C. zeae‐maydis is a major maize disease throughout the world. Although more than 100 QTLs resistant against GLS have been identified, very few of them have been cloned.Here, we identified a major resistance QTL against GLS, qRglsSB, explaining 58.42% phenotypic variation in SB12×SA101 BC1F1 population. By fine‐mapping, it was narrowed down into a 928 kb region. By using transgenic lines, mutants and complementation lines, it was confirmed that the ZmWAK02 gene, encoding an RD wall‐associated kinase, is the responsible gene in qRglsSB resistant against GLS.The introgression of the ZmWAK02 gene into hybrid lines significantly improves their grain yield in the presence of GLS pressure and does not reduce their grain yield in the absence of GLS.In summary, we cloned a gene, ZmWAK02, conferring large effect of GLS resistance and confirmed its great value in maize breeding. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

22. A double-stranded RNA binding protein enhances drought resistance via protein phase separation in rice.

Author

Wang, Huaijun, Ye, Tiantian, Guo, Zilong, Yao, Yilong, Tu, Haifu, Wang, Pengfei, Zhang, Yu, Wang, Yao, Li, Xiaokai, Li, Bingchen, Xiong, Haiyan, Lai, Xuelei, and Xiong, Lizhong

Abstract

Drought stress significantly impacts global rice production, highlighting the critical need to understand the genetic basis of drought resistance in rice. Here, through a genome-wide association study, we reveal that natural variations in DROUGHT RESISTANCE GENE 9 (DRG9), encoding a double-stranded RNA (dsRNA) binding protein, contribute to drought resistance. Under drought stress, DRG9 condenses into stress granules (SGs) through liquid-liquid phase separation via a crucial α-helix. DRG9 recruits the mRNAs of OsNCED4, a key gene for the biosynthesis of abscisic acid, into SGs and protects them from degradation. In drought-resistant DRG9 allele, natural variations in the coding region, causing an amino acid substitution (G267F) within the zinc finger domain, increase DRG9’s binding ability to OsNCED4 mRNA and enhance drought resistance. Introgression of the drought-resistant DRG9 allele into the elite rice Huanghuazhan significantly improves its drought resistance. Thus, our study underscores the role of a dsRNA-binding protein in drought resistance and its promising value in breeding drought-resistant rice.Drought is one of the major abiotic stresses affecting rice growth and development. Here, the authors identify a dsRNA-binding protein positively regulates rice drought resistance through promoting stability of OsNCED4 mRNAs, transcript of a key gene for the biosynthesis of abscisic acid, via protein phase separation. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

23. High Precision Trajectory Learning Method Based Improved Dynamic Movement Primitives for Robot Skill Learning

Author

Zhai, Bin, Zhang, Enzheng, Li, Bingchen, and Fang, Xiujun

Abstract

In robot skill learning, the higher accuracy of trajectory reproduction, the smaller difference between the trajectory reproduced by the robot and the expected trajectory, and the higher accuracy and reliability of the robot’s task execution. In robot skill learning methods based on dynamic movement primitives, the problems are found that the tail exponential decay of Gaussian kernel function and the number of shape parameters have a great influence on the accuracy of trajectory reproduction, a high precision trajectory learning method is proposed. A novel truncation processing method is proposed to eliminate the impact of tail exponential decay, and the optimization on the number of shape parameters is used to improve the approximation of the local gradient of the target forcing term, which are combined to improve trajectory reproduction accuracy. The principle of the proposed method is described in detail. The simulation and comparison experiments are performed to verify the effectiveness of the proposed method in improving trajectory reproduction accuracy. This paper makes contributions to the field of robot skill trajectory learning and provides a promising method for improving trajectory reproduction accuracy.

Published
2024
Full Text
View/download PDF

24. TFE3 fusions direct an oncogenic transcriptional program that drives OXPHOS and unveils vulnerabilities in translocation renal cell carcinoma.

Author

Li J, Huang K, McBride F, Sadagopan A, Gallant DS, Thakur M, Khanna P, Li B, Ge M, Weiss CN, Achom M, Xu Q, Huang K, Ryback BA, Gui M, Bar-Peled L, and Viswanathan SR

Abstract

Translocation renal cell carcinoma (tRCC) is an aggressive subtype of kidney cancer driven by TFE3 gene fusions, which act via poorly characterized downstream mechanisms. Here we report that TFE3 fusions transcriptionally rewire tRCCs toward oxidative phosphorylation (OXPHOS), contrasting with the highly glycolytic metabolism of most other renal cancers. This TFE3 fusion-driven OXPHOS program, together with heightened glutathione levels found in renal cancers, renders tRCCs sensitive to reductive stress - a metabolic stress state induced by an imbalance of reducing equivalents. Genome-scale CRISPR screening identifies tRCC-selective vulnerabilities linked to this metabolic state, including EGLN1 , which hydroxylates HIF-1α and targets it for proteolysis. Inhibition of EGLN1 compromises tRCC cell growth by stabilizing HIF-1a and promoting metabolic reprogramming away from OXPHOS, thus representing a vulnerability to OXPHOS-dependent tRCC cells. Our study defines a distinctive tRCC-essential metabolic program driven by TFE3 fusions and nominates EGLN1 inhibition as a therapeutic strategy to counteract fusion-induced metabolic rewiring., Competing Interests: Declaration of Interests S.R.V. has consulted for Jnana Therapeutics within the past 3 years and receives research support from Bayer. L.B-P. is a co-founder, holds equity in and is a consultant for Scorpion Therapeutics.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results