Cancer development involves the co-evolution of cancer cells and their surrounding microenvironment, yet the dynamics of this interaction within the physical architecture remains poorly understood. Here, we present a spatial transcriptomic map at single-cell resolution, encompassing 127 multi-stage fields of view from 43 patients, to chart the evolutionary trajectories of human esophageal squamous cell carcinoma (ESCC). By analyzing 6.4 million cells, we reveal that ESCC progression is driven by a proliferative epithelial cell subpopulation that acquires dedifferentiated and invasive characteristics. At the late precancerous stage, these cells disrupt the epithelial-stromal interface and recruit normal fibroblasts via JAG1-NOTCH1 signaling, transforming them into cancer-associated fibroblasts (CAFs). This interaction leads to the formation of a "CAF-Epi" (CAF and epithelial cell) niche at the tumor edge that shields the tumor from immune surveillance. The CAF-Epi niche formation is a key indicator of progression in ESCC and other squamous cell carcinomas and patient outcomes.

Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

Wireless traffic prediction can help operators accurately predict the usage of wireless networks, and it plays an important role in the load balancing and energy saving of base stations. Currently, most traffic prediction methods are centralized learning strategies, which need to transmit a large amount of traffic data and have timeliness and data privacy issues. Federated learning, as a distributed learning framework with no client data sharing and multi-client collaborative training, can solve such problems. We propose a federated learning wireless traffic prediction framework based on mutual information clustering (FedMIC). First, a sliding window scheme is used to construct the raw data into adjacent and periodic dual-traffic sequences and capture their traffic characteristics separately to enhance the client model learning capability. Second, clients with similar traffic data distributions are clustered together using a mutual information-based spectral clustering algorithm to facilitate the capture of the personalized features of each clustered model. Then, models are aggregated using a hierarchical aggregation architecture of intra-cluster model aggregation and inter-cluster model aggregation to address the statistical heterogeneity challenge of federated learning and to improve the prediction accuracy of models. Finally, an attention mechanism-based model aggregation algorithm is used to improve the generalization ability of the global model. Experimental results show that our proposed method minimizes the prediction error and has superior traffic prediction performance compared to traditional distributed machine learning methods and other federated learning methods.

Network-security situation prediction is a crucial aspect in the field of network security. It is primarily achieved through monitoring network behavior and identifying potential threats to prevent and respond to network attacks. In order to enhance the accuracy of situation prediction, this paper proposes a method that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU), while also incorporating an attention mechanism. The model can simultaneously handle the spatial and temporal features of network behavior and optimize the weight allocation of features through the attention mechanism. Firstly, the CNN’s powerful feature extraction ability is utilized to extract the spatial features of the network behavior. Secondly, time-series features of network behavior are processed through the GRU layer. Finally, to enhance the model’s performance further, we introduce attention mechanisms, which can dynamically adjust the importance of different features based on the current context information; this enables the model to focus more on critical information for accurate predictions. The experimental results show that the network-security situation prediction method, which combines a CNN and a GRU and introduces an attention mechanism, performs well in terms of the fitting effect and can effectively enhance the accuracy of situation prediction.

Nowadays, the industrial Internet is developing rapidly, but at the same time it faces serious information security risks. At present, industrial Internet data generally have the problems of complex attack sample types, large numbers, and high feature dimensions. When training a model, the complexity and quantity of attack samples will result in a long detection time for the intrusion detection algorithm, which will fall short of the system’s real-time performance. Due to the high feature dimension of the data, shallow feature extraction will be unable to extract the data’s more significant features, which will render the model’s overall detection capacity insufficient. Aiming at the above problems, an industrial Internet intrusion detection method based on Res-CNN-SRU is proposed. This method not only considers the temporality of network traffic data but can also effectively capture the local features in the data. The dataset used in the experiment is the gas pipeline industry dataset proposed by Mississippi State University in 2014. Experiments show that the algorithm can effectively improve the recognition rate of the system and reduce the false-alarm rate. At the same time, the training time required for this method is also greatly shortened, and it can perform efficient intrusion detection on the industrial Internet.