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Dynamic access control model is the theoretical basis for constructing a dynamic access control system for big data.However, most existing access control models can only fulfill dynamic access control in a single scenario and are unable to adapt to access control in multiple types of dynamic scenarios.These scenarios include changes in the contextual environment of big data, changes in entity relationships, and changes in the state of objects.To address these issues, an analysis was conducted based on the research of existing access control models and the dynamic factors of big data.Subsequently, scenario-aware access control (SAAC) model was proposed, which was based on dynamic factor conversion and scenario unified modeling.All types of dynamic factors were converted into basic elements such as attributes and relationships.Then, scene information was incorporated to model the various types of constituent elements in a unified manner.A big data dynamic access control model was constructed based on scene information to support multi-type dynamic factors and extended dynamic factors.The working framework of the SAAC model was designed, and the SAAC rule learning algorithm and SAAC rule execution algorithm were proposed corresponding to the workflow of the framework.This enabled the automatic learning of access control rules and dynamic access control decision-making.The security of the proposed model was analyzed and verified by introducing the non-transitive non-interference theory.To validate the effectiveness of the access control policy mining method of the proposed model, experimental comparisons were conducted between the SAAC model and baseline models such as ABAC-L, PBAC-X, DTRM, and FB-CAAC using four datasets.The experimental results demonstrate that the SAAC model and its strategy mining method outperforms the baseline models in terms of metrics such as area under the curve AUC, monotonicity, and steepness of the ROC curve.This verification confirms that the proposed model can support multiple types of dynamic factors and dynamic factor extensions, and that the combined effect of the access control rules obtained from its mining algorithm is relatively high.

Fully understanding the environmental implications of engineered nanomaterials is crucial for their safe and sustainable use. Cyanobacteria, as the pioneers of the planet earth, play important roles in global carbon and nitrogen cycling. Here, we evaluated the biological effects of molybdenum disulfide (MoS2) nanosheets on a N2-fixation cyanobacteria (Nostoc sphaeroides) by monitoring growth and metabolome changes. MoS2 nanosheets did not exert overt toxicity to Nostoc at the tested doses (0.1 and 1 mg/L). On the contrary, the intrinsic enzyme-like activities and semiconducting properties of MoS2 nanosheets promoted the metabolic processes of Nostoc, including enhancing CO2-fixation-related Calvin cycle metabolic pathway. Meanwhile, MoS2 boosted the production of a range of biochemicals, including sugars, fatty acids, amino acids, and other valuable end products. The altered carbon metabolism subsequently drove proportional changes in nitrogen metabolism in Nostoc. These intracellular metabolic changes could potentially alter global C and N cycles. The findings of this study shed light on the nature and underlying mechanisms of bio-nanoparticle interactions, and offer the prospect of utilization bio-nanomaterials for efficient CO2 sequestration and sustainable biochemical production.

With the rapid development and wide application of big data technology, users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources.The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules, which enhances the logical expression of policies and realizes dynamic access control.However, It still faces the problems of missing entity relationship data and complex relationship paths of rules.To overcome these problems, a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs.A topology learning method based on directed enclosing subgraphs was designed in this modeled.And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments, including directed enclosing subgraph extraction, subgraph node labeling calculation and topological structure feature learning.A node embedding feature learning method based on directed neighbor subgraph was proposed, which incorporated elements such as attention coefficients and relationship types, and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning.A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs.The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR, MRR and Hits@N compared with the baseline models such as R-GCN, SEAL, GraIL and TACT.The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme.The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules.The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction.

Background: Microsatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA. Methods: We enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA. Results: Differential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A, ACVR2A, TGFBR2, KMT2D, RNF43, and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P < 0.001) and more positive programmed death ligand 1 (PD-L1) expression (37.5% vs. 11.9%, P < 0.001) than those with an MSS status. Among patients receiving PD-1 inhibitor-based therapy, those with MSI-H had a longer median overall survival (OS, hazard ratio (HR) = 0.17, P = 0.001) and progression-free survival (PFS, HR = 0.14, P < 0.001) than patients with MSS. Integrating MSI-H and PD-L1 expression status (combined positive score ≥ 5) could distinguish the efficacy of immunotherapy. Conclusions: MSI-H status was associated with a higher TMB value and more positive PD-L1 expression in CCA tumors. Moreover, in patients with advanced CCA who received PD-1 inhibitor-based immunotherapy, MSI-H and positive PD-L1 expression were associated with improved both OS and PFS. Trial registration: This study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577). [ABSTRACT FROM AUTHOR]

Fully understanding the environmental implications of engineered nanomaterials is crucial for their safe and sustainable use. Cyanobacteria, as the pioneers of the planet earth, play important roles in global carbon and nitrogen cycling. Here, we evaluated the biological effects of molybdenum disulfide (MoS 2 ) nanosheets on a N 2 -fixation cyanobacteria ( Nostoc sphaeroides ) by monitoring growth and metabolome changes. MoS 2 nanosheets did not exert overt toxicity to Nostoc at the tested doses (0.1 and 1 mg/L). On the contrary, the intrinsic enzyme-like activities and semiconducting properties of MoS 2 nanosheets promoted the metabolic processes of Nostoc , including enhancing CO 2 -fixation-related Calvin cycle metabolic pathway. Meanwhile, MoS 2 boosted the production of a range of biochemicals, including sugars, fatty acids, amino acids, and other valuable end products. The altered carbon metabolism subsequently drove proportional changes in nitrogen metabolism in Nostoc . These intracellular metabolic changes could potentially alter global C and N cycles. The findings of this study shed light on the nature and underlying mechanisms of bio-nanoparticle interactions, and offer the prospect of utilization bio-nanomaterials for efficient CO 2 sequestration and sustainable biochemical production.