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Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches

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computers in Biology and Medicine 2025年 190卷 110044-110044页

作者： Saif, Ahmed Islam, Md. Tarikul Raihan, Md. Obayed Yousefi, Niloofar Rahman, Md. Ajijur Faridi, Hafeez Hasan, Al Riyad Hossain, Mirza Mahfuj Saleem, Rasha Mohammed Albadrani, Ghadeer M. Al-Ghadi, Muath Q. Ahasan Setu, Md. Ali Kamel, Mohamed Abdel-Daim, Mohamed M. Aktaruzzaman, Md. Department of Pharmacy Faculty of Science University of Rajshahi Rajshahi6205 Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Jashore7408 Bangladesh Department of Pharmaceutical Sciences College of Health Sciences and Pharmacy Chicago State University ChicagoIL United States Department of Industrial Engineering and Management Systems University of Central Florida USA OrlandoFL United States Department of Pharmacy Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Laboratory Medicine Faculty of Applied Medical Sciences Al-Baha University Al-Baha65431 Saudi Arabia Department of Biology College of Science Princess Nourah bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Microbiology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Medicine and Infectious Diseases Faculty of Veterinary Medicine Cairo University Giza12211 Egypt Department of Pharmaceutical Sciences Pharmacy Program Batterjee Medical College P.O. Box 6231 Jeddah21442 Saudi Arabia Pharmacology Department Faculty of Veterinary Medicine Suez Canal University Ismailia41522 Egypt

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysis of CDC7 across multiple cancers is lacking, and existing therapeutic options have come with limited clinical success. The aim of this is to integrate a comprehensive pan-cancer analysis of CDC7 with the identification of novel marine-derived inhibitors, bridging the understanding of CDC7's role as a prognostic biomarker and therapeutic target across diverse cancer types. In this study, we conducted a pan-cancer analysis of CDC7 across 33 tumor types using publicly available datasets to evaluate its expression, genetic alterations, immune interactions, survival, and prognostic significance. Additionally, a marine-derived compound library of 31,492 molecules was screened to identify potential CDC7 inhibitors using chemoinformatics and machine learning. The top candidates underwent rigorous evaluations, including molecular docking, pharmacokinetics, toxicity, Density Functional Theory (DFT) calculations, and Molecular Dynamics (MD) simulations. The findings revealed that CDC7 is overexpressed in several cancers and is associated with poor survival outcomes and unfavorable prognosis. Enrichment analysis linked CDC7 to critical DNA replication pathways, while its role in modulating tumor-immune interactions highlighted its potential as a target for immunotherapy. Among all tested compounds, Tetrahydroaltersolanol D (CMNPD21999) exhibited the strongest binding affinity and stability, along with better drug-likeness and zero toxicity. These attributes highlight its potential as a promising drug candidate for CDC7 inhibition and future cancer treatment development. Furthermore, additional in vitro and in vivo studies are required to confirm the effectiveness of this drug candidate against the CDC7 protein. © 2025

关键词： Gene expression

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Competitor Activation and Semantic Interference: Evidence from Combined Phonological and Semantic Similarity 36

Competitor Activation and Semantic Interference: Evidence fr...

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36th Annual Meeting of the cognitive science Society, CogSci 2014

作者： Frazer, Alexandra K. O'Séaghdha, Padraig G. Munoz-Avila, Hector Roessler, Nicholas Department of Psychology and Cognitive Science Program Lehigh University United States Computer Science and Engineering and Cognitive Science Program Lehigh University United States

ISBN: (纸本)9780991196708

Incremental learning explanations state that semantic interference is driven by activation levels of competitors. To explore nonsemantic contributions to interference, we examined the combined and separate effects of facilitatory phonological form preparation and semantic relatedness in a blocked cyclic picture naming procedure. Phonological similarity was facilitatory when tested separately, but had little effect when tested with the other conditions. We found about twice as much interference in word sets that shared both meaning and form (e.g., cyclically name puffin, pigeon, and peacock) as in semantic-only sets. Thus, phonological similarity impacted interference when it was combined with a semantic attribute. A computational model that isolated the learning mechanism and eliminated carryover effects simulated this result and additionally showed cumulative interference over naming cycles. Together with other findings from our research group and in the literature, these results suggest that co-activation from a variety of sources can drive interference. © 2014 Proceedings of the 36th Annual Meeting of the cognitive science Society, CogSci 2014. All rights reserved.

关键词： Semantics

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Visual Control and Mapping for UAV-Based Platform Inspection

Visual Control and Mapping for UAV-Based Platform Inspection

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes Carlos M. Soria Raquel F. Vassallo Alexandre S. Brandão Department of Electrical Engineering Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil Facultad de Ingeniería Instituto de Automática Universidad Nacional de San Juan Argentine Department of Electric Engineering Federal University of Espirito Santo Vitória ES Brazil

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Autonomous take-off and landing capabilities are crucial in UAV vision-based missions, ensuring adaptive navigation, especially in challenging environments where realtime identification and interaction with a variety of landing platforms are required. In this context, this paper presents a servo-visual controller that uses pattern detection and color segmentation techniques to identify take-off/landing platforms and estimate their current orientation. The proposed system was subjected to experimental validation with four platforms positioned in different orientations, heights, and positions, demonstrating its versatility in various conditions. Our study addresses the Flying Robots Trial League challenge, which emulates mapping and inspection tasks in offshore platforms.

关键词： Visualization Offshore installations Navigation Color Inspection Autonomous aerial vehicles Aircraft Drones Aerospace control

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model

arXiv

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arXiv 2025年

作者： Ha, Sumin Kim, Jun Hyeong Piao, Yinhua Kim, Sun Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Bio-MAX/N-Bio Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Korea Republic of

Human expertise in chemistry and biomedicine relies on contextual molecular understanding, a capability that large language models (LLMs) can extend through fine-grained alignment between molecular structures and text. Recent multimodal learning advances focus on cross-modal alignment, but existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecular understanding. Moreover, naïve multi-view alignment strategies face two challenges: (1) separate aligned spaces with inconsistent mappings between molecule and text embeddings, and that (2) existing loss objectives fail to preserve complementary information for fine-grained alignment. This can limit the LLM’s ability to fully understand the molecular properties. To address these issues, we propose MV-CLAM, a novel framework that aligns multi-view molecular representations into a unified textual space using a multi-query transformer (MQ-Former). Our approach ensures cross-view consistency while a token-level contrastive loss preserves diverse molecular features across textual queries. MV-CLAM enhances molecular reasoning, improving retrieval and captioning accuracy. The source code of MV-CLAM is available in https://***/sumin124/***. © 2025, CC BY-NC-SA.

关键词： Molecular docking

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Microbial Communities in Coastal Salt Marshes Along the Southeast Coast of China and their Effects on Carbon Storage

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Current Analytical Chemistry 2025年

作者： Lv, Chunguang Li, Yan Zhang, Huining Wang, Chenyi Ren, Min Zhang, Chiqian Zhejiang Wanli University College of Biological & Environmental Sciences 315000 China NingboTech University School of Civil Engineering 315000 China Marine Environmental Monitoring Centre of Ningbo 315100 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University AR72467 United States

Background: Wetlands are vital carbon sinks, with coastal salt marshes being particularly effective in carbon sequestration. Understanding how different vegetation types influence soil carbon storage and microbial composition can enhance our knowledge of these ecosystems’ roles in global carbon cycling. Methods: This study investigates soil physicochemical properties, soil carbon storage, and microbial community composition and diversity at three depths (10, 30, and 60 cm) in five salt marsh plots from five coastal salt marshes: Bare flat (a non-vegetated marsh) and plots dominated by Imperata cylindrica, Solidago canadensis, Suaeda spp., and Phragmites australis. Carbon storage was evaluated by measuring soil organic carbon (SOC) content. At the same time, high-throughput sequencing was employed to analyze microbial communities, aiming to elucidate their relationships with soil carbon storage and wetland vegetation. Results: The average SOC contents in the five plots were in the order of S. canadensis (13.33 g·kg–1) > Bare flat (11.45 g·kg–1) > Suaeda spp. (8.10 g·kg–1) > I. cylindrica (6.15 g·kg–1) > P. australis (5.80 g·kg–1). S. canadensis and Bare flat marshes had the highest carbon content, which is mainly attributed to the presence of the most abundant carbon-fixation microbes, Fulvivirgaceae (family) and Acidobacteria RB41 (genus), in the soil of these two plots. Both organic and dissolved organic carbon contents at 10 cm were higher than those at the other two depths for all five marshes. Conclusion: The Bare flat exhibited strong carbon sequestration capability, second only to the invasive S. canadensis plot among the five plots surveyed. It also showed the highest microbial abundance and the greatest number of carbon-sequestration-related functional genes. The S. canadensis plot exhibited the lowest microbial community diversity and abundance despite having the highest carbon storage capacity. As an invasive species, S. canadensis should be removed to mit

关键词： Vegetation

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Effect of Music Training in Neural Responses to Emotional Speech Prosody: Insights from EEG and Brain Network Analysis 4th

Effect of Music Training in Neural Responses to Emotional...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Gao, Ci-Jun Liu, Xucheng Jia, Gufeng Yang, Hongzhe Tao, Wei Wu, Haiyan Huang, Ruey-Song Wan, Feng Department of Psychology Faculty of Social Sciences University of Macau Taipa China Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Taipa China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9789819640003

Speech prosody conveys crucial information during social communication. However, the neural mechanisms underlying speech pro-sody in the human brain remain unclear. The primary aim of this study was to explore the impact of long-term musical training on the recognition of emotional speech prosody. Employing a task that isolated prosody recognition without semantic content interference, we recorded behavioral and electrophysiological data. Participants were categorized into two groups: individuals with systematic musical training and those without any formal musical training. Our findings indicate that the musician group exhibited enhanced neural activity in the frontal region, characterized by significant differences in the P50, P200, P3a, P3b, and P600 components. Additionally, a larger Power Spectral Density (PSD) was observed in the frontal, occipital, and parietal regions of musicians, particularly in the theta and alpha frequency bands. Graph theoretical analysis also indicated that musically trained individuals demonstrated an increased local coefficient and more concentrated degree. This study provides a comprehensive analysis of the neural alteration during speech prosody recognition attributable to long-term musical training, incorporating both static and dynamic analyses, with a focus on temporal and spatial dimensions. Our study provides new insights into the neural mechanisms induced by speech prosody. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power spectral density

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Local and global sensitivity analyses on the overflow probability of Abadia de Goiás radioactive near surface repository

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Nuclear engineering and Design 2025年 439卷

作者： Teixeira, D.G. Frutuoso e Melo, P.F. Alvim, L.G.M. Alvim, A.C.M. Alves, A.S.M. Graduate Program of Nuclear Engineering COPPE Federal University of Rio de Janeiro RJ Rio de Janeiro Brazil Department of Computer Science Multidisciplinary Institute Federal Rural University of Rio de Janeiro UFRRJ RJ Nova Iguaçu Brazil Eletronuclear S. A. RJ Rio de Janeiro Brazil

This work presents a sensitivity analysis of the overflow probability of the near- surface radioactive repository built after the Abadia de Goiás accident, where radioactive 137Cs was released. The purpose of the analysis is to help the repository owner and the regulatory body make decisions on design parameters that affect the overflow probability. The following parameters have been considered: degradation function of the repository ceiling, internal porosity of the repository, internal area of the repository base, repository base and wall thicknesses, repository height, height of the liquid column inside the repository, average rainfall rate, evapotranspiration rate, surface runoff and hydraulic conductivity of the repository concrete. The choice for the range of each parameter is discussed. An investigation of the role of the mentioned parameters is detailed based on results obtained in a previous paper, and the dependencies among the rate parameters on the average rainfall rate is emphasized and modeled. To give a deeper perspective, local and global sensitivity analyses have been performed. In the case of global analysis, two approaches have been followed: in the first, the concept of Normalized Mutual Information and the other one leverages Shapley values derived from Light Gradient Boosting Machine and Shapley values have been used for investigating non-linearities. For some parameters the lowest values are to be considered, e.g., the repository base thickness and the average rainfall rate, while for others the highest value is desirable, like the evapotranspiration rate and the surface runoff in order to obtain lower overflow probabilities. These results should be looked with care because nonlinearities have been identified by the Shapley value analysis. Overall, the degradation function and the evapotranspiration rate emerge as the most influential parameters. The legacy of this work lies in the robust methodological framework it provides, paving the way fo

关键词： Nuclear reactor accidents

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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