FK506, a secondary metabolite produced by Streptomyces tsukubaensis, is well known for its immunosuppressant properties to prevent rejection of transplanted organs and treat autoimmune diseases. However, the low titer of FK506 in the original producer strain limits the further industrialization efforts and restricts its clinical applications. To address this issue, a highly efficient method combined genome shuffling and dynamic fed-batch strategies was systematically performed in this work. Firstly, after five rounds of genome shuffling based on precursors and product resistances, a higher yielding strain TJ-P325 was successfully acquired, whose production reached 365.6 mg/L, 11-fold increase compared with the original strain. Then, the possible mechanism of different production capabilities between TJ-P325 and the wild type was explored through comparative gene expression analysis of key genes. Results showed that the transcription level of key genes was altered significantly in the mutant. Moreover, precursors addition enhanced the FK506 production by 28 %, as well as reduced the by-products biosynthesis. Finally, the disodium malonate and disodium methylmalonate dynamic fed-batch strategies dramatically led to the production of 514.5 mg/L in a 7.5-L bioreactor. These results demonstrated that genome shuffling and dynamic fed-batch strategies could be successfully applied to generate high-yield strains with value-added natural products during industrial microbial fermentation.

With the in-depth study of the internal process mechanism of the global ocean by oceanographers, traditional ocean observation methods have been unable to meet the new observation requirements. In order to achieve a low-cost ocean observation mechanism with high spatio-temporal resolution, this paper introduces mobile crowd sensing technology into the field of ocean observation. Firstly, a Transformer-based vessel trajectory prediction algorithm is proposed, which can monitor the location and movement trajectory of vessel in real time. Secondly, the participant selection algorithm in mobile crowd sensing is studied, and based on the trajectory prediction algorithm, a dynamic participant selection algorithm for ocean mobile crowd sensing is proposed by combining it with the discrete particle swarm optimization (DPSO) algorithm. Thirdly, a coverage estimation algorithm is designed to estimate the coverage of the selection scheme. Finally, the spatio-temporal resolution of the vessel's driving trajectory is analyzed through experiments, which verifies the effectiveness of the algorithm and comprehensively confirms the feasibility of mobile crowd sensing in the field of ocean observation.

Sea surface temperature (SST) serves as a critical indicator of oceanic thermodynamic processes and climate variability, exerting essential influence on ocean fronts, typhoon tracks, and monsoon evolution. Nevertheless, owing to the highly nonlinear and complex multi-scale characteristics of SST, achieving accurate spatiotemporal forecasting remains a formidable challenge. To address this issue, we proposed an enhanced Transformer architecture that incorporates a Coordinate Attention (CA) module and an Adaptive Fusion (AD) module, enabling the joint extraction and integration of temporal and spatial features. The proposed model is evaluated through SST prediction experiments over a localized region of the South China Sea with lead times of 1, 7, 15, and 30 days. Results indicate that our approach consistently outperforms baseline models across multiple evaluation metrics. Moreover, generalization experiments conducted on datasets from regions with diverse latitudes and climate regimes further demonstrate the model’s robustness and adaptability in terms of both accuracy and stability.

Due to the complexity of ocean sensing tasks, buoy detection in traditional ocean observation methods has the disadvantages of high cost and insufficient real-time performance. Ocean mobile crowd sensing technology collects high-resolution data in real-time through shipborne sensors and has significant potential for future development in the ocean Internet of Things. In this article, we consider a cooperative vessel activity that performs sensing tasks, incentivizing multiple vessels through dynamic external task profits. To spontaneously incentivize the team to take part in tasks and maximize the total profit of the vessels, we propose a multiagent task allocation model (MATM-SN) based on deep reinforcement learning (DRL). This model uses a generative adversarial network to predict the trajectory of the vessels, and STGCN is used to extract synergistic features between vessels effectively. DRL training provides an optimal task allocation strategy for vessels. Finally, we compared four baselines and demonstrated that the MATM-SN model outperformed other baselines regarding task completion rate and total efficiency.

Social relationship recognition (SRR) holds significant value in fields such as behavior analysis and intelligent social systems. However, existing methods primarily focus on modeling individual visual traits, interaction patterns, and scene-level contextual cues, often failing to capture the complex dependencies among these features and the hierarchical structure of social groups, which are crucial for effective reasoning. In order to overcome these restrictions, this essay suggests a SRR model that integrates Gated Graph Neural Network (GGNN) and Transformer. The task for SRR in this model is image-based. Specifically, the purpose of a novel and robust hybrid feature extraction module is to capture individual characteristics, relative positional information, and group-level cues, which are used to construct relation nodes and group nodes. A modified GGNN is then employed to model the logical dependencies between features. Nevertheless, GGNN alone lacks the capacity to dynamically adjust feature importance, which may result in ambiguous relationship representations. The Transformer’s multi-head self-attention (MSA) mechanism is integrated to improve feature interaction modeling, allowing the model to capture global context and higher-order dependencies effectively. By fusing pairwise features, graph-structured features, and group-level information. Experimental results on public datasets such as PISC demonstrate that the proposed approach outperforms comparison models including Dual-Glance, GRM, GRRN, Graph-BERT, and SRT in terms of accuracy and mean average precision (mAP), validating its effectiveness in multi-feature representation learning and global reasoning.

In recent years, multimodal social relation recognition has become a critical task in the fields of computer vision and natural language processing. However, existing research still faces key gaps, particularly in effectively aligning image features with linguistic features to improve recognition accuracy. This paper proposes a social relation recognition method based on multimodal feature fusion and validates the crucial role of cross-modal alignment mechanisms in enhancing recognition accuracy. Our approach leverages large language models to meticulously extract event structures from textual descriptions, capturing key elements such as emotional states, scenarios, and relationships, while employing convolutional neural networks to extract deep features from images. Subsequently, we introduce a cross-modal alignment mechanism to semantically align textual event structures with visual features, ensuring high semantic consistency between the two modalities. Extensive experiments on multiple public datasets demonstrate that our method significantly outperforms existing unimodal and basic multimodal approaches, confirming its effectiveness and innovative contributions to social relation recognition.