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Targeting the HER2-ELF3-KRAS axis: a novel therapeutic strategy for KRAS^G13D colorectal cancer

Molecular Cancer 2025年第1期24卷 1-22页

作者： Hwang, Soo-Yeon Seo, Yoojeong Park, Seojeong Kim, Seul-Ah Moon, Inhye Liu, Yi Kim, Seojeong Pak, Eun Seon Jung, Sehyun Kim, Hyeyoon Jeon, Kyung-Hwa Seo, Seung Hee Sung, Inyoung Lee, Heetak Park, So-Yeon Na, Younghwa Kim, Tae Il Kwon, Youngjoo College of Pharmacy Graduate School of Pharmaceutical Sciences Ewha Womans University Seoul 03760 South Korea Graduate Program in Innovative Biomaterials Convergence Ewha Womans University Seoul 03760 South Korea Division of Gastroenterology Department of Internal Medicine Yonsei University College of Medicine 50 Yonseiro Seodaemun-Gu Seoul 03722 South Korea BK21 FOUR Intelligence Computing Seoul National University Seoul South Korea Center for Genome Engineering Institute for Basic Science 55 Expo-Ro Yuseong-Gu Daejeon 34126 South Korea College of Pharmacy CHA University Pocheon 11160 South Korea

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, with KRAS mutations playing a significant role in its tumorigenesis. Among the KRAS variants, the G13D mutation is associated with poor prognosis and distinctive biological behaviors. This study focuses on the role of HER2, a critical prognostic and predictive biomarker, in modulating the unique characteristics of KRAS^G13D-mutated CRCs. We identified a novel transcriptional regulatory network involving HER2, ELF3, and KRAS, with ELF3 acting as a key transcription factor (TF) that regulates KRAS expression under conditions of HER2 overexpression. Our findings reveal that this HER2-ELF3-KRAS axis is exclusively activated in KRAS^G13D, driving aggressive oncogenic features and conferring resistance to cetuximab (CTX) therapy. Through comprehensive analysis of gene expression profiles, we demonstrated that HER2 is a crucial therapeutic target specifically for KRAS^G13D CRCs. To explore this further, we introduced YK1, a small molecule inhibitor designed to disrupt the ELF3-MED23 interaction, leading to the transcriptional downregulation of HER2 and KRAS. This intervention significantly attenuated the HER2-ELF3-KRAS axis, sensitizing KRAS^G13D CRCs to CTX and reducing their tumorigenic potential by inhibiting the epithelial-to-mesenchymal transition process. Our study underscores the importance of HER2 as a key determinant in the unique biological characteristics of KRAS^G13D CRCs and highlights the therapeutic potential of targeting the HER2-ELF3-KRAS axis. By presenting YK1 as a novel pharmacological approach, we provide a promising strategy for developing tailored interventions for KRAS^G13D CRCs, contributing to the ongoing efforts in precision medicine for CRCs. © The Author(s) 2025.

关键词： Colorectal cancer ELF3 HER2 HER2-ELF3-KRAS axis KRAS mutation KRASG13D Protein–protein interaction inhibitor Transcriptional regulation

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Haptic Empathy: Investigating Individual Differences in Affective Haptic Communications 25

Haptic Empathy: Investigating Individual Differences in Affe...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Yulan Ju Xiaru Meng Harunobu Taguchi Tamil Selvan Gunasekaran Matthias Hoppe Hironori Ishikawa Yoshihiro Tanaka Yun Suen Pai Kouta Minamizawa Keio University Graduate School of Media Design Yokohama Japan Empathic Computing Lab Auckland Bioengineering Institute University of Auckland Auckland New Zealand Keio University Graduate School of Media Design Yokohama Japan and JSPS International Research Fellow Tokyo Japan NTT DOCOMO Tokyo Japan Nagoya Institute of Technology Nagoya Japan and Inamori Research Institute for Science Kyoto Japan School of Computer Science University of Auckland Auckland New Zealand and Empathic Computing Laboratory Auckland Bioengineering Institute University of Auckland Auckland New Zealand

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Haptic Empathy: Investigating Individual Di!erences in A!ective Haptic Communications

arXiv

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arXiv 2025年

作者： Ju, Yulan Meng, Xiaru Taguchi, Harunobu Gunasekaran, Tamil Selvan Hoppe, Matthias Ishikawa, Hironori Tanaka, Yoshihiro Pai, Yun Suen Minamizawa, Kouta Keio University Graduate School of Media Design Yokohama Japan Empathic Computing Lab Auckland Bioengineering Institute University of Auckland Auckland New Zealand JSPS International Research Tokyo Japan NTT DOCOMO Tokyo Japan Nagoya Institute of Technology Nagoya Japan Inamori Research Institute for Science Kyoto Japan School of Computer Science University of Auckland Auckland New Zealand Empathic Computing Laboratory Auckland Bioengineering Institute University of Auckland Auckland New Zealand

Nowadays, touch remains essential for emotional conveyance and interpersonal communication as more interactions are mediated remotely. While many studies have discussed the e!ectiveness of using haptics to communicate emotions, incorporating a!ect into haptic design still faces challenges due to individual user tactile acuity and preferences. We assessed the conveying of emotions using a two-channel haptic display, emphasizing individual di!erences. First, 24 participants generated 187 haptic messages re"ecting their immediate sentiments after watching 8 emotionally charged #lm clips. Afterwards, 19 participants were asked to identify emotions from haptic messages designed by themselves and others, yielding 593 samples. Our #ndings suggest potential links between haptic message decoding ability and emotional traits, particularly Emotional Competence (EC) and A!ect Intensity Measure (AIM). Additionally, qualitative analysis revealed three strategies participants used to create touch messages: perceptive, empathetic, and metaphorical expression. © 2025, CC BY.

关键词： Haptic interfaces

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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HAAQI-Net: A Non-Intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1877-1892页

作者： Dyah A. M. G. Wisnu Stefano Rini Ryandhimas E. Zezario Hsin-Min Wang Yu Tsao Taiwan International Graduate Program—Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taiwan

This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.

关键词： Hearing aids Quality assessment Training Feature extraction Speech processing Auditory system Computational modeling Adaptation models Transformers Multiple signal classification

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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SHIFTING ATTENTION TO YOU: PERSONALIZED BRAIN-INSPIRED AI MODELS

arXiv

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arXiv 2025年

作者： Zhao, Stephen Chong Hu, Yang Lee, Jason Bender, Andrew Wallace, Mark Tovar, David A. Mazumdar, Trisha Data Science Institute Vanderbilt University Nashville United States Department of Computer Science Vanderbilt University Nashville United States Neurosciences Graduate Program University of California San Diego San Diego United States Department of Psychology Vanderbilt University Nashville United States

The integration of human and artificial intelligence represents a scientific opportunity to advance our understanding of information processing, as each system offers unique computational insights that can enhance and inform the other. The synthesis of human cognitive principles with artificial intelligence has the potential to produce more interpretable and functionally aligned computational models, while simultaneously providing a formal framework for investigating the neural mechanisms underlying perception, learning, and decision-making through systematic model comparisons and representational analyses. In this study, we introduce personalized brain-inspired modeling that integrates human behavioral embeddings and neural data to align with cognitive processes. We took a stepwise approach, fine-tuning the Contrastive Language–Image Pre-training (CLIP) model with large-scale behavioral decisions, group-level neural data, and finally, participant-level neural data within a broader framework that we have named CLIP-Human-Based Analysis (CLIP-HBA). We found that fine-tuning on behavioral data enhances its ability to predict human similarity judgments while indirectly aligning it with dynamic representations captured via magnetoencephalography (MEG). To further gain mechanistic insights into the temporal evolution of cognitive processes, we introduced a model specifically fine-tuned on millisecond-level MEG neural dynamics (CLIP-HBA-MEG). This model resulted in enhanced temporal alignment with human neural processing while still showing improvement on behavioral alignment. Finally, we trained individualized models on participant-specific neural data, effectively capturing individualized neural dynamics and highlighting the potential for personalized AI systems. These personalized systems have far-reaching implications for the fields of medicine, cognitive research, human-computer interfaces, and AI development. © 2025, CC BY-NC-ND.

关键词： Magnetoencephalography

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Remote-controlled mechanical and directional motions of photoswitchable DNA condensates

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Nature Communications 2025年第1期16卷 1-16页

作者： Udono, Hirotake Nomura, Shin-ichiro M. Takinoue, Masahiro Department of Computer Science School of Computing Institute of Science Tokyo Kanagawa Yokohama 226-8501 Japan Department of Robotics Graduate School of Engineering Tohoku University Miyagi Sendai 980-8579 Japan Research Center for Autonomous Systems Materialogy (ASMat) Institute of Innovative Research (IIR) Institute of Science Tokyo Kanagawa Yokohama 226-8501 Japan

Membrane-free synthetic DNA-based condensates enable programmable control of dynamic behaviors as shown by phase-separated condensates in biological cells. We demonstrate remote-controlled microflow using photocontrollable state transitions of DNA condensates, assembled from multi-branched DNA nanostructures via sticky-end (SE) hybridization. Introducing azobenzene into SEs enables their photoswitchable binding affinity, which underlies photoreversible fluidity of the resulting condensates that transition between gel/liquid/dissociated states in a wavelength-dependent manner. Leveraging base-sequence programmability, spatially coupled orthogonal DNA condensates with divergent photoresponsive capabilities perform multi-modal mechanical actions that depend on azobenzene insertion sites in the SE, including switching flows radially expanding and converging under photoswitching. Localizing photoswitching within a DNA liquid condensate generates two distinct directional motions, whose contrasting morphology, direction, and lifetime are determined by switching frequency. Numerical simulations reveal its regulatory role in weight-adjusting energy-exchanging and energy-dissipative interactions between the photoirradiated and unirradiated domains. © The Author(s) 2025.

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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