Biological age is a more direct reflection of physiological status than chronological age, serving as a vital measure to evaluate health risks and aging interventions. While steroid metabolomics offers rich information for exploring aging mechanisms, the complex and nonlinear interactions within metabolic networks remain challenging in modeling. Here, we propose and describe METRON as a deep learning framework to predict biological ages from steroid metabolomics. Specifically, a Metabolite Interaction Perception Module (MIPM) is proposed to capture the interactions. Subsequently, a Group-Rational Kolmogorov-Arnold Network is also integrated to capture intricate dependencies and enhance the representation capability. We demonstrate that METRON achieves promising performance as compared to other machine learning and deep learning methods. Beyond performance, METRON offers interpretability by recovering the established markers such as Dehydroepiandrosterone (DHEA) and identifying 17-hydroxyprogesterone (17-OH-P4) as the key signature linked to hypothalamic-pituitary-adrenal axis dynamics. These results support the capacity of METRON not only to estimate biological age but also to uncover underappreciated metabolic drivers behind aging.

Large Language Models (LLMs) demonstrate promising capabilities in natural language understanding and reasoning with enormous parameter spaces and vast amounts of training data. These attributes have facilitated their deployment into diverse application domains. However, the underlying parameters implicitly assume decision-making boundaries, resulting in a significant number of decision spaces not covered by training data. This makes them susceptible to adversarial manipulations through carefully crafted inputs. To illuminate the vulnerabilities of LLMs, we propose a unified token-level jailbreaking attack that makes victim models generate responses for potentially harmful queries. Specifically, we propose an evolutionary algorithm to evolve perturbation sets, utilizing gradient-based and crossover-based operators to enhance performance under multiple scenarios. Furthermore, we develop a repository for reusing past perturbations and conduct an analysis of token sensitivity within LLMs, facilitating zero-shot attacks with convergence. Extensive benchmark experiments validate the effectiveness of our method on three different models, achieving increases of 62.36%, 57.89%, and 64.81% in attack success rates compared to baseline methods under white-box scenarios. In addition, the evaluation experiments demonstrate our method is effective for multiple scenarios and different size of models. This research reveals vulnerabilities of LLMs, provides theoretical foundations for developing more robust defense strategies, and contributes to building more reliable AI systems.

Drug combination therapy plays a vital role in disease treatment, including cancer, as it contributes to treatment efficacy and can alleviate the effect of drug resistance. Although clinical trials and screening may provide valuable information about synergistic drug combinations, they suffer from challenging combinatorial space. Multiple methods are proposed to address those issues. However, they still fail in making full use of global and local triplet context relationships of known synergistic combinations. To this end, a deep learning model which leverages dual view hypergraph representation fusion for synergistic drug combinations identification is proposed, namely DVHSyn. It first extracts the transcriptome features of cancer cell lines and molecular structures of drugs. Subsequently, by modeling the synergistic effect on a hypergraph, DVHSyn simultaneously learns the local and global context of the sample triplets via a hypergraph view and its expanded heterogeneous graph view. Finally, the learned representations of the above two branches are fused selectively to predict synergistic drug combinations. Experiment results demonstrate that DVHSyn surpasses six other competing methods. One case study also reflects that DVHSyn has the potential to predict novel synergistic drug combinations. Overall, our method is effective in identifying synergistic drug combinations and provides new insights for novel drug development.

LiDAR-based 3D perception plays a pivotal role in autonomous driving systems, which is a crucial component facilitating obstacle avoidance for vehicles, posing potential security risks in daily usage. Previous LiDAR-based adversarial attacks against autonomous driving models generally focus on the elimination or modification of specific objects in the point cloud. However, such attacks severely rely on the prior knowledge of the location of the target object and can only have limited adversarial impacts within certain local detection results. Instead, in this paper, we develop a novel LiDAR-based 3D adversarial attack from a more comprehensive perspective, which adaptively learns to impose global hazards without choosing particular local point sets of genuine obstacles, while enhancing the effectiveness, imperceptibility, and practicality of the perturbed LiDAR point clouds. We conduct extensive experiments on the large self-driving dataset Waymo to demonstrate the effectiveness of our attack against four object detection models (CenterPoint, PV-RCNN, DSVT, VoxelNext).

Temporal Activity Localization (TAL) is crucial and fundamental for multimedia understanding. Although many works have made great efforts and achieved significant progress on this task, most of them directly utilize the mixed visual features extracted by the 3D backbone network to match with the complicated query semantic, thus failing to capture the subtly distinct visual features associated with the interested entities or events for better activity modeling. To overcome this challenge, in this paper, we present a novel Disentangled Appearance-Motion Learning (DAML) framework that is able to learn the disentangled representations and capture finer levels of granularity across different modalities, such as nouns-related visual appearance or verbs-related visual motion for more interpretable cross-modal alignment. Specifically, without introducing any large feature extraction model, we disentangle the mixed video feature extracted by 3D backbone into separate appearance and motion contexts with the help of vector quantization. In this way, we can achieve more fine-grained correspondence between the visual appearance and textual nouns, visual motion and textual verbs for better modeling the object entities, events of the target activity. Extensive experiments on three challenging datasets (Charades-STA, TACoS and ActivityNet) show the effectiveness of DAML.

High-throughput sequencing such as Hi-C captures spatiotemporal chromatin interactions, revealing the intricate interplays within transcriptional regulation and chromatin dynamics during cellular reprogramming and developmental processes. However, the forecast on chromatin dynamics in successive developmental stages remains challenging due to the inherent complexity of spatial and temporal patterns in Hi-C data across developmental stages. Towards such a direction, we present StarMie, a deep learning framework that integrates the spatiotemporal-aware module and the multi-stage differential variation module to predict high-throughput chromatin interactions in next developmental stages. Our comprehensive evaluation demonstrates that StarMie outperforms existing methods and sufficiently captures discriminative spatial and temporal dependencies as well as inter-stage-level variations of chromatin interactions. Moreover, the dual importance of both spatial and temporal information in Hi-C data is observed in parameter analysis. Ablation studies also confirm the essential role of each component in StarMie. Furthermore, five cross-species case studies support StarMie's cross-species generalizability and its capability to extract universal chromatin interaction patterns in different developmental stages. In-depth analysis demonstrates that StarMie uncovers conserved genomic logic in cardiac development and disease. Overall, this work paves a new approach for exploring genome reprogramming and development through predictive modeling of Hi-C dynamics.

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.

MOTIVATION: Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data. RESULTS: To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights. AVAILABILITY AND IMPLEMENTATION: The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.