Dual RNA-seq reveals the complement protein C3-mediated host-pathogen interaction in the brain abscess caused by Staphylococcus aureus by Qiyuan Jin, Yaxuan Zhai, Rui Qiang, Xin Ma, Chenhao Zhao, Jinqi Zhong, Jijie Li, Qi Chen, Mingxiao Han, Hong Du, Qifei Cong, Haifang Zhang

“…ABSTRACT This study aimed to elucidate the complement protein C3-mediated host-pathogen interaction in the brain abscess caused by Staphylococcus aureus infection. …”
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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing by Zekun Xu, Yipeng Zhou, Shiting Wen, Weipeng Jing

“…First, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network is proposed to promote information interaction between features, improving the model’s learning capability. …”
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Depression detection based on dual path DCGAN data generation and classification-regression network by LU Jingxue, LI Hongyan, ZHENG Ruichao, QIN Ruizhen

“…For residual networks in classification networks, multi-scale convolution is introduced to enhance the information interaction between features, so that residual networks can fully perceive the multi-level information contained in feature maps.Results and Discussions　Feature validity test was carried out for the six emotional features selected, that is, MFCC, MFCC-TEO, LPCC and Jitter features were added in turn on the basis of short-term energy, zero cross rate and sound intensity, and accuracy (Acc), root mean square error (RMSE) and mean absolute error (MAE) under different input characteristics were calculated. …”
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Multi-tasking role of the mechanosensing protein Ankrd2 in the signaling network of striated muscle. by Anna Belgrano, Ljiljana Rakicevic, Lorenza Mittempergher, Stefano Campanaro, Valentina C Martinelli, Vincent Mouly, Giorgio Valle, Snezana Kojic, Georgine Faulkner

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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…First, a cross-modal encoding module (CME) is designed by fusing convolutional neural networks, recurrent neural networks, and feature enhancement mechanisms, which is capable of extracting multi-scale deep features from peptide and protein sequences, and thus better capturing their interactions at different levels. …”
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DGCFNet: Dual Global Context Fusion Network for remote sensing image semantic segmentation by Yuan Liao, Tongchi Zhou, Lu Li, Jinming Li, Jiuhao Shen, Askar Hamdulla

“…Although convolutional neural networks (CNNs) have strong capabilities in extracting local information, they are limited in establishing long-range dependencies due to the inherent limitations of convolution. …”
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Eye Gaze Estimation Based on Stacked Hourglass Neural Network for Aircraft Helmet Aiming by Quanlong Qiu, Jie Zhu, Chao Gou, Mengtang Li

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CSA-Net: Complex Scenarios Adaptive Network for Building Extraction for Remote Sensing Images by Dongjie Yang, Xianjun Gao, Yuanwei Yang, Minghan Jiang, Kangliang Guo, Bo Liu, Shaohua Li, Shengyan Yu

“…Therefore, we propose a complex scenarios adaptive network (CSA-Net) for building extraction. CSA-Net is comprised of the hierarchical-context feature extraction (HFE) module, the global-local feature interaction (GFI) module, and the multiscale-adaptive feature fusion (MFF) structure. …”
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Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data by Sayantika Roy, Armelle Varillas, Armelle Varillas, Emily A. Pereira, Patrick Myers, Patrick Myers, Golnoosh Kamali, Kristin M. Gunnarsdottir, Kristin M. Gunnarsdottir, Nathan E. Crone, Adam G. Rouse, Jennifer J. Cheng, Michael J. Kinsman, Patrick Landazuri, Patrick Landazuri, Utku Uysal, Carol M. Ulloa, Nathaniel Cameron, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge González-Martínez, Joon Y. Kang, Sridevi V. Sarma, Sridevi V. Sarma

“…However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs).Methods:In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. …”
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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

“…To address these issues, this paper proposes Crack-ConvT Net, a U-Shape architecture that integrates Convolutional Neural Networks (CNNs) and Transformers for underwater dam crack segmentation. …”
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Using Generative Adversarial Networks for the synthesis of emotional facial expressions in virtual educational environments by William Villegas-Ch, Alexandra Maldonado Navarro, Araceli Mera-Navarrete

“…The generation of emotional facial expressions using Generative Adversarial Networks (GANs) has been widely researched, achieving significant advances in creating high-quality images. …”
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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images by Yu Wang, Hao Chen, Ye Zhang, Guozheng Li

“…Next, an adaptive local feature enhancement block is constructed using cross-fusion and adjacent feature propagation between dynamic convolutions, aimed at enhancing the ability of model to recover details. Then, we present a dynamic enhancement-based bidirectional information flow module to model the dynamic interaction between multitask features, guiding detail recovery and feedback for optimized cloud removal features. …”
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Emergency scheduling strategy of integrated electricity-gas energy system considering wind-power fluctuation in typhoon disaster by JIN Haixiang, BIAN Xiaoyan, HUANG Ruanming, ZHOU Qibin, XU Ling

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Optimization of deep learning architecture based on multi-path convolutional neural network algorithm by Chuan Zhou, Yan Liu, Xinghan An, Xiyao Xu, Hao Wang

“…Abstract Current multi-stream convolutional neural network (MSCNN) exhibits notable limitations in path cooperation, feature fusion, and resource utilization when handling complex tasks. …”
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Adaptive distributed honeypot detection network for enhanced cybersecurity against DoS and DDoS attacks by V. Selva Kumar, K.R. Mohan Raj, S. Gopalakrishnan, G. Vennila, D. Dhinakaran, P. Kavitha

“…The increasing prevalence and sophistication of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks present significant challenges in ensuring the security and stability of modern networked systems. These attacks, characterized by their ability to disrupt services and compromise resources, require innovative and robust detection mechanisms to safeguard highly interactive environments such as honeypot systems. …”
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CFM-UNet: coupling local and global feature extraction networks for medical image segmentation by Ke Niu, Jiacheng Han, Jiuyun Cai

“…Conversely, Mamba, a novel network framework, effectively captures long-range feature dependencies and excels in processing linearly arranged image inputs, albeit at the cost of overlooking fine spatial relationships and local pixel interactions. …”
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Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives by Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING

“…Although deep learning methods have achieved better results than traditional trajectory prediction algorithms, there are still problems such as information loss, interaction and uncertainty difficulties in modelling, and lack of interpretability of predictions when implementing multimodal high-precision prediction for autonomous vehicles in heterogeneous, highly dynamic and complex changing environments.The newly developed Transformer's long-range modelling capability and parallel computing ability make it a great success not only in the field of natural language processing, but also in solving the above problems when extended to the task of multimodal trajectory prediction for autonomous driving.Based on this, the aim of this paper is to provide a comprehensive summary and review of past deep neural network-based approaches, in particular the Transformer-based approach.The advantages of Transformer over traditional sequential network, graphical neural network and generative model were also analyzed and classified in relation to existing challenges, simultaneously.Transformer models can be better applied to multimodal trajectory prediction tasks, and that such models have better generalisation and interpretability.Finally, the future directions of multimodal trajectory prediction were presented.…”
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Coordinated Actions at Free Play Activities on Children Interethnic Encounters by Paula Rasia Lira, Luana Santos, Vinicius Rocha, Danilo Silva Guimarães, Briseida Dogo Resende

“…Abstract: This study aimed to investigate coordinated actions built between Mbya-Guarani and non-indigenous children during free play activities. 21 Mbya-Guarani and 61 non-indigenous children participated in two “Encounters for Play,” a project constructed by the Indigenous Network and Mbya-Guarani communities. We recorded children’s social interactions during the free playtimes, selected the first and last 10 minutes of each encounter: (1) Performed scans every 30s registering children who were playing together; (2) Applied Social Network Analysis to explore children’s association pattern on each encounter; (3) Performed a focal continuous transcription of each child present in the interethnic clusters using an ethogram. …”
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HGNN-GAMS: Heterogeneous Graph Neural Networks for Graph Attribute Mining and Semantic Fusion by Yufei Zhao, Hua Liu, Hua Duan

“…Heterogeneous Graph Neural Networks (HGNNs) have attracted significant research attention in recent years due to their ability to capture complex interactions among various node types in heterogeneous graphs (HGs). …”
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Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition by Chengyuan Ke, Sheng Liu, Zhenghao Ke, Yuan Feng, Shengyong Chen

“…To address this issue, we propose a Cross-scale Spatial Refinement Graph Convolutional Network (CSR-GCN), which aims to improve action recognition accuracy by effectively capturing fine-grained features of skeleton sequences. …”
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