Human leukocyte antigen (HLA) molecules play a pivotal role in antigen presentation. Tumor cells present neoantigens on the cell surface via HLA molecules, thereby activating cytotoxic T cells and eliciting immune responses. This process offers critical opportunities for cancer immunotherapy and tumor vaccine development. However, the identification of tumor neoantigens remains challenging due to limitations in data scale, prediction accuracy, and cross-species compatibility of existing methods. To address these challenges, we developed TransBindpMHCI, a transformer-based pan-specific major histocompatibility complex (MHC) peptide binding prediction model. By employing 1,404,492 mass spectrometry-screened MHC-presented peptides for modeling, the model directly captures the authentic processes of peptide generation and presentation. Its dual-tier transformer encoder architecture significantly enhances feature extraction capabilities for peptide-MHC binding patterns while reducing computational complexity. Furthermore, TransBindpMHCI extends prediction coverage to peptides spanning 8–15 amino acids and achieves cross-species compatibility for both human and murine MHC-I molecules. Comprehensive evaluations demonstrate that TransBindpMHCI outperforms existing methods in accuracy, computational efficiency, and generalizability, enabling the identification of more immunogenic neoantigens. This model holds substantial promise for advancing tumor neoantigen validation and personalized vaccine design.

Rapid technological advancements have enabled widespread tourism experience sharing across digital platforms, influencing not only destination awareness but also travelers’ psychological decision-making. This study investigates how visual, textual, and interactive content-sharing behaviors affect consumer tourism loyalty, using data from 507 survey responses analyzed through an Ordered Probit model. Results show that all three content-sharing behaviors significantly enhance consumer tourism loyalty, suggesting that sharing travel experiences via images, videos, text, and interactions fosters stronger loyalty to tourism brands and destinations. The study also examines the role of psychological satisfaction, finding it moderates and partially mediates the tourism experience sharing-loyalty relationship. Practical implications highlight how destination marketers and tourism service providers can leverage tourism experience sharing to enhance visitor engagement, build emotional connections, and cultivate sustained loyalty. This study advances the understanding of tourism experience sharing as a strategic tool in digital tourism marketing and loyalty building.

The management of large-scale events poses significant resilience challenges to urban traffic systems, where classical computational methods often prove inadequate for the resulting complex, dynamic optimization problems. This paper introduces a quantum-enhanced hybrid high-performance computing framework designed to improve traffic resilience during such events. The framework leverages a Quantum Annealing (QA) algorithm to solve a Quadratic Unconstrained Binary Optimization (QUBO) model of key traffic optimization tasks, integrated with classical computing for data processing and workflow control. Using the traffic management for a major concert at Hefei Luogang Park as a case study, we focus on modeling and applying this framework to two core resilience problems: Dynamic Evacuation Route Optimization (DERO) and Emergency Resource Allocation (ERA). Our simulation results provide empirical validation of the framework's effectiveness, demonstrating that the proposed quantumenhanced approach reduces total evacuation time by 20.0 % and average emergency response time by 32.2 % compared to welldefined greedy heuristics. These findings highlight the potential of quantum computing as an emerging high-performance computing paradigm for addressing the intricate challenges of traffic resilience.

As more and more multilingual knowledge becomes available on the Web, knowledge sharing across languages has become an important task to benefit many applications. One of the most crucial kinds of knowledge on the Web is taxonomy, which is used to organize and classify the Web data. To facilitate knowledge sharing across languages, we need to deal with the problem of cross-lingual taxonomy alignment, which discovers the most relevant category in the target taxonomy of one language for each category in the source taxonomy of another language. Current approaches for aligning cross-lingual taxonomies strongly rely on domain-specific information and the features based on string similarities. In this paper, we present a new approach to deal with the problem of cross-lingual taxonomy alignment without using any domain-specific information. We first identify the candidate matched categories in the target taxonomy for each category in the source taxonomy using the cross-lingual string similarity. We then propose a novel bilingual topic model, called Bilingual Biterm Topic Model (BiBTM), to perform exact matching. BiBTM is trained by the textual contexts extracted from the Web. We conduct experiments on two kinds of real world datasets. The experimental results show that our approach significantly outperforms the designed state-of-the-art comparison methods.