Cancer is a highly heterogeneous disease underpinned by complex molecular alterations. Accurate subtyping is critical for guiding personalized treatment and improving clinical outcomes. However, multi-omics data are high-dimensional, noisy, and heterogeneous across platforms, posing major challenges for reliable subtyping. To address this, dimensionality reduction is necessary to capture underlying molecular patterns in a low-dimensional space, facilitating both computational efficiency and biological interpretation. We present Consensus subtyping method with Ensemble Dimensionality Reduction for multi-omics data integration (CEDR), a consensus subtyping framework that integrates complementary linear and nonlinear dimensionality reduction methods with robust clustering and probabilistic ensemble modeling. Different from existing dimensionality reduction techniques, our framework adopts an ensemble learning framework that integrates multiple dimensionality reduction techniques with robust clustering to achieve reliable consensus cancer subtyping. We apply Optimally Tuned Robust Improper Maximum Likelihood Estimator to the concatenated low-dimensional matrix for robust subtyping, and ensemble the result with the Mixture Model for Clustering Ensembles to identify stable subtypes. Across extensive simulations, CEDR consistently outperformed conventional dimensionality reduction-based clustering, the Cluster Of Clusters Analysis (COCA) ensemble strategy, and state-of-the-art multi-omics integration algorithms (SNF and CIMLR) in both accuracy and robustness. Application to clear cell renal cell carcinoma and lower-grade glioma revealed biologically interpretable subtypes characterized by distinctive survival outcomes, pathway activities, and immune infiltration patterns. These findings demonstrate that CEDR provides a powerful and reliable strategy for multi-omics data integration and cancer subtyping, with strong potential for broader applications in high-dimensional multimodal data analysis.

Ophthalmic diseases such significantly impair the vision of numerous individuals globally. Accurate and real-time 3D reconstruction of macular edema and retinal tears is crucial for improving surgical efficiency and success rates. However, lesion areas often exhibit considerable noise and high heterogeneity, and the imaging devices employed may introduce electronic noise and artifacts. Current 2D medical image segmentation techniques fail to achieve optimal outcomes. To overcome these challenges, we propose the Tri-Path Fourier-Temporal Modulation Network (TriFTM-Net). TriFTM-Net synergistically integrates spatial, frequency, and spatiotemporal features. This design effectively augments both feature representation and extraction. TriFTM-Net comprises three critical modules: the Tri-Path Spectral Hierarchical Encoder (TPSHE), which amplifies feature representation by integrating tri-path features; the Feature Re-Modulation (FRM), which reduces noise interference and enhances feature extraction; and the Hierarchical Feature Reconstruction Module (HFRM), which improves detail preservation in upsampled images. Comparative analysis with thirteen baseline methods demonstrates that our approach achieves the highest Dice scores, IoU, and Kappa coefficient on the OIMHS dataset.Our code is publicly available at https://github.com/IMOP-lab/TriFTM-Net.

Cancer is a highly heterogeneous disease characterized by complex molecular changes. Subtypes identified through multi-omics data hold significant promise for improving prognosis and facilitating personalized precision treatment. Recent multi-omics integration methods have mostly focused on capturing complementary information from different data types, often overlooking potential interactions between omics data. Here we develop a novel method named interactive multi-kernel learning (iMKL), which incorporates omics-omics interactions alongside heterogeneous data types under the unsupervised multi-kernel learning framework, to improve subtype identification. Using the sample-similarity kernel for each dataset, we propose a joint Hadamard product strategy to capture higher-order interactive effects from different omics data types. We applied iMKL to two renal cell carcinoma (RCC) datasets-clear renal cell carcinoma (ccRCC) and type II papillary renal cell carcinoma (type II pRCC)-both including miRNA expression, mRNA expression, and DNA methylation data. Stability analysis through random sampling of patients or features demonstrated that iMKL exhibits strong robustness and accuracy in identifying patient subtypes. The identified subtypes revealed dramatic differences in patient survival, with both ccRCC and type II pRCC classified into three distinct subtypes. The findings in the real application highlight potential biomarkers associated with adverse patient outcomes and demonstrate substantial advancement in cancer subtype identification. The iMKL method effectively identifies tumor molecular subtypes that are strongly associated with clinical features and survival rates, providing valuable insights for accurate cancer subtyping, clinical decision-making, and the realization of personalized treatment strategies.

In an era marked by the rapid advancement of information technology, individuals now have the ability to enhance their organic bodies with mechanical and computational devices, revolutionizing their capabilities in everyday activities. However, the seamless integration of biosignals with mechanical counterparts continues to pose significant challenges. To address this, fuzzy logic (FL) emerges as a potential key to these challenges, offering a promising solution for handling the uncertainties and ambiguity inherent in biosignals. It thereby facilitates improved cyborg intelligence in an array of applications, including but not limited to wireless body area networks, brain–computer interfaces, prosthetics, and exoskeletons. Although previous works have highlighted the enhancement of cyborg intelligence using fuzzy sets, all of them only dived single aspects of the cyborg intelligence enhancement applications, leading to a lack of comprehensive understanding. Therefore, we provide a holistic overview of the state-of-the-art applications of FL in cyborg enhancement technology, encompassing its benefits, challenges, and potential directions for future research.

Auditory signals are crucial in Level 3 autonomous vehicle takeovers, complementing visual information and reducing the demand on visual attention. This study investigated the effects of different auditory warning types (auditory icon, earcon, speech, spearcon) and emergency obstacle avoidance takeover scenarios (caused by road conditions, vehicle movement, vulnerable road users) and their interactions on driver takeover behavior. Generalized Linear Mixed Models (GLMMs) were used to analyze the data of simulated driving experiments from 40 participants. The results indicate that, for emergency obstacle avoidance takeover scenarios caused by road conditions, speech warnings exhibit high perceived acceptability but low perceived urgency; whereas in situations caused by vehicle motion or vulnerable road users, the result is the opposite. Speech and earcon warnings significantly outperform auditory icon and spearcon warnings in terms of perceived satisfaction. By integrating scale and simulated driving data, we discuss the roles of different auditory warning types in various takeover scenarios.

DNS tunneling has led to significant privacy breaches and financial losses. Different tool for DNS tunneling serves varied purposes: penetration testing, firewall bypassing, and communication with C2 servers. Therefore, achieving multi-classification for different DNS tunneling software is crucial. However, previous research faced three issues: ineffective use of PDNS data, overlooking the topological relationships of entities, and not leveraging DNS tunnels’ structural features, causing inefficiencies in multi-classification tasks. Our method utilizes the graph’s powerful representation ability for relationship to exploit DNS tunnel domain names’ structural features from PDNS data and to address sample imbalance meanwhile. We constructed a PDNS dataset containing 691,769 domain names and a graph dataset named CSR-PTDNG, comprising 41,943 graphs. The latter represents the first graph dataset related to DNS tunneling research. Besides, we adopt three encoders for Client, Subdomain, and Record data (Rdata) nodes. Using GNNs for node embeddings updating and graph classification, we evaluate the models and the framework in binary and multi-class tasks. Ultimately, all GNN models achieved nearly 1 AUC and F1-score, demonstrating the effectiveness of our graph construction approach.

Renal cell carcinoma (RCC) ranks among the most prevalent cancers worldwide, with both incidence and mortality rates increasing annually. The heterogeneity among RCC patients presents considerable challenges for developing universally effective treatment strategies, emphasizing the necessity of in-depth research into RCC's molecular mechanisms, understanding the variations among RCC patients and further identifying distinct molecular subtypes for precise treatment. We proposed a metagene-based similarity network fusion (Meta-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes. These latent metagenes were then integrated to construct a fused network under the Similarity Network Fusion (SNF) framework for more precise subtyping. We conducted simulation studies and analyzed real-world data from two RCC datasets, namely kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) to demonstrate the utility of Meta-SNF. The simulation studies indicated that Meta-SNF achieved higher accuracy in subtype identification compared with the original SNF and other state-of-the-art methods. In analyses of real data, Meta-SNF produced more distinct and well-separated clusters, classifying both KIRC and KIRP into four subtypes with significant differences in survival outcomes. Subsequently, we performed comprehensive bioinformatics analyses focused on subtypes with poor prognoses in KIRC and KIRP and identified several potential biomarkers. Meta-SNF offers a novel strategy for subtype identification using multi-omics data, and its application to RCC datasets has yielded diverse biological insights which are highly valuable for informing clinical decision-making processes in the treatment of RCC.

Abstract Frequency diversity array (FDA) radar can provide full spatial coverage with stable gains within a pulse duration. Based on the FDA, the integrated radar‐communication system can perform multi‐directional communication and whole‐space detection. However, the embedded communication bits disrupt the correlation of the transmitting waveform of each element. Correspondingly, the range sidelobe level (SLL) of the multi‐dimensional ambiguity function increases significantly. To address this issue, a low‐sidelobe waveform for integrated radar‐communication systems based on the FDA was designed. Two techniques based on the subarray time delay are employed to reduce the SLL in range dimension. Both methods, however, lower the angular resolution. Thus, a tangent FM signal as the baseband waveform to improve the angular resolution was selected. Simultaneously, the received signal processing methods of radar and communication was designed. The performances of the designed waveform are verified by analysing the multi‐dimensional ambiguity function and the bit error rate. The simulation results reveal that the proposed method can maintain a good radar target detection capability and satisfy the communication function.

Ductal carcinoma in situ (DCIS) is considered a pre-invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists' review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46±0.40)% and identifies the DCIS tissue patches with a F1-score of (97.40±1.18)% (mean±variance). Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post-processing steps.