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Background Sustainable management of tree diseases requires harnessing the plant’s own immune system. Leaf blight caused by Neopestalotiopsis clavispora ( N. clavispora ) affects the ornamental value of Ginkgo biloba ( G. biloba ) and the accumulation of its medicinal components. Elicitors, as a novel biological control method, hold potential application value in the prevention and management of G. biloba leaf blight. Results We demonstrate that a cell wall elicitor extracted from the fungal pathogen N. clavispora potently induces systemic resistance against leaf blight in the ancient gymnosperm G. biloba . The elicitor exhibited no direct antifungal activity, confirming that its protective function is mediated exclusively through plant innate immunity. Pre-treatment with the elicitor resulted in over 80% disease control, outperforming commercial resistance inducers and matching the efficacy of carbendazim fungicide. This resistance was associated with a primed state, characterized by a rapid and sustained burst in key defense enzyme activities (POD, PAL, PPO), accelerated accumulation of lignin and phenolics, and mitigated oxidative damage. Metabolomic analyses revealed that the elicitor triggers a change of the defense landscape. We identified coordinated upregulation of the phenylpropanoid pathway, which was directly mirrored by the accumulation of defensive flavonoids and flavonols. Furthermore, tryptophan metabolism and glycerophospholipid pathways were significantly altered, indicating a comprehensive reconfiguration of primary and secondary metabolism. Conclusions Our findings uncover a multifaceted defense strategy in G. biloba , wherein the fungal elicitor acts as a priming agent to establish a state of alert, enabling a robust, integrated metabolic response that effectively restricts pathogen invasion. This work provides a molecular framework for elicitor-induced resistance in trees and validates a sustainable, vaccine-like strategy for forest protection.

The multi-party Privacy-Preserving Record Linkage (PPRL) aims to identify and match the same entity across different parties’ data sources while ensuring that all private data remains protected and undisclosed, except for the final matching results shared among the parties. The previously proposed a multi-party PPRL method based on secondary encoding suffers from issues related to the number of data splits and load balancing, which impact computational efficiency and linkage quality. We propose an extended approach—a multi-party PPRL method based on improved secondary encoding(ISE_PPRL). By adopting a rational data split strategy and participant work strategy, and incorporating the geometric mean and consistent hashing algorithm, this method overcomes the traditional limitation where each party could only process a single data split. It effectively mitigates potential collusion risks among data splits, enhances security, optimizes computational efficiency, and maintains linkage quality. Experimental results demonstrate that this method exhibits significant advantages in improving security, enhancing computational efficiency, and maintaining linkage quality.

Aqueous zinc‐iodine (Zn‐I2) batteries are promising energy storage devices; however, the conventional single‐electron reaction potential and energy density of iodine cathode are inadequate for practical applications. Activation of high‐valence iodine cathode reactions has evoked a compelling direction to developing high‐voltage zinc‐iodine batteries. Herein, ethylene glycol (EG) is proposed as a co‐solvent in a water‐in‐deep eutectic solvent (WiDES) electrolyte, enabling significant utilization of two‐electron‐transfer I+/I0/I− reactions and facilitating an additional reversibility of Cl0/Cl− redox reaction. Spectroscopic characterizations and calculations analyses reveal that EG integrates into the Zn2+ solvation structure as a hydrogen‐bond donor, competitively binding O atoms in H2O, which triggers a transition from water‐rich to water‐poor clusters of Zn2+, effectively disrupting the H2O hydrogen‐bond network. Consequently, the aqueous Zn‐I2 cell achieves an exceptional capacity of 987 mAh gI2−1 with an energy density of 1278 Wh kgI2−1, marking an enhancement of ≈300 mAh g−1 compared to electrolyte devoid of EG, and enhancing the Coulombic efficiency (CE) from 68.2% to 98.7%. Moreover, the pouch cell exhibits 3.72 mAh cm−2 capacity with an energy density of 4.52 mWh cm−2, exhibiting robust cycling stability. Overall, this work contributes to the further development of high‐valence and high‐capacity aqueous Zn‐I2 batteries. A novel water‐in‐deep eutectic solvent electrolyte uses ethylene glycol as a co‐solvent to transform water‐rich clusters into water‐poor clusters, enabling the cathode to achieve the two‐electron conversion of I+/I0/I– and the conversion of Cl0/Cl–. The aqueous Zn‐I2 cell achieves an exceptional capacity and energy density with high Coulombic efficiency.

Privacy-preserving record linkage (PPRL) is the process of linking records from various data sources, ensuring that matching records for the same entity are shared among parties while not disclosing other sensitive data. However, most existing PPRL approaches currently rely on third parties for linking, posing risks of malicious tampering and privacy breaches, making it difficult to ensure the security of the linkage. Therefore, we propose a parallel multi-party PPRL method based on consortium blockchain technology which can effectively address the issue of semi-trusted third-party validation, auditing all parties involved in the PPRL process for potential malicious tampering or attacks. To improve the efficiency and security of consensus within a consortium blockchain, we propose a practical Byzantine fault tolerance consensus algorithm based on matching efficiency. Additionally, we have incorporated homomorphic encryption into Bloom filter encoding to enhance its security. To optimize computational efficiency, we have adopted the MapReduce model for parallel encryption and utilized a binary storage tree as the data structure for similarity computation. The experimental results show that our method can effectively ensure data security while also exhibiting relatively high linkage quality and scalability.

The era of big data has brought rapid growth and widespread application of data, but the imperfections in the existing data integration system have become obstacles to its high-quality development. The conflict between data security and shared utilization is significant, with traditional data integration methods risking data leakage and privacy breaches. The proposed Privacy-Preserving Record Linkage (PPRL) technology, has effectively resolved this contradiction, enabling efficient and secure data sharing. Currently, many solutions have been developed for PPRL issues, but existing assessments of PPRL methods mainly focus on single indicators. There is a scarcity of comprehensive evaluation and comparison frameworks that consider multiple indicators of PPRL(such as linkage quality, computational efficiency, and security), making it challenging to achieve a comprehensive and objective assessment. Therefore, it has become an urgent issue for us to conduct a multi-indicator comprehensive evaluation of different PPRL methods to explore the optimal approach. This article proposes the use of an modified CRITIC method to comprehensively evaluate PPRL methods, aiming to select the optimal PPRL method in terms of linkage quality, computational efficiency, and security. The research results indicate that the improved CRITIC method based on mathematical statistics can achieve weight allocation more objectively and quantify the allocation process effectively. This approach exhibits exceptional objectivity and broad applicability in assessing various PPRL methods, thereby providing robust scientific support for the optimization of PPRL techniques.

With the world’s data volume growing exponentially, it becomes critical to link it and make decisions. Privacy-preserving record linkage (PPRL) aims to identify all the record information corresponding to the same entity from multiple data sources, without disclosing sensitive information. Previous works on multi-party PPRL methods typically adopt homomorphic encryption technology due to its ability to perform computations on encrypted data without needing to decrypt it first, thus maintaining data confidentiality. However, these methods have notable shortcomings, such as the risk of collusion among participants leading to the potential disclosure of private keys, high computational costs, and decreased efficiency. The advent of trusted execution environments (TEEs) offers a solution by protecting computations involving private data through hardware isolation, thereby eliminating reliance on trusted third parties, preventing malicious collusion, and improving efficiency. Nevertheless, TEEs are vulnerable to side-channel attacks. In this work, we propose an enhanced PPRL method based on TEE technology. Our methodology involves processing plaintext data within a TEE using the inner product mask technique, which effectively obfuscates the data, making it impervious to side-channel attacks. The experimental results demonstrate that our approach not only significantly improves resistance to side-channel attacks but also enhances efficiency, showing better performance and privacy preservation compared to existing methods. This work provides a robust solution to the challenges faced by current PPRL methods and sets the stage for future research aimed at further enhancing scalability and security.

With the advent of the big data era, data security and sharing have become the core elements of new-era data processing. Privacy-preserving record linkage (PPRL), as a method capable of accurately and securely matching and sharing the same entity across multiple data sources, is receiving increasing attention. Among the existing research methods, although PPRL methods based on Bloom Filter encoding excel in computational efficiency, they are susceptible to privacy attacks, and the security risks they face cannot be ignored. To balance the contradiction between security and computational efficiency, we propose a multi-party PPRL method based on secondary encoding. This method, based on Bloom Filter encoding, generates secondary encoding according to well-designed encoding rules and utilizes the proposed linking rules for secure matching. Owing to its excellent encoding and linking rules, this method successfully addresses the balance between security and computational efficiency. The experimental results clearly show that, in comparison to the original Bloom Filter encoding, this method has nearly equivalent computational efficiency and linkage quality. The proposed rules can effectively prevent the re-identification problem in Bloom Filter encoding (proven). Compared to existing privacy-preserving record linkage methods, this method shows higher security, making it more suitable for various practical application scenarios. The introduction of this method is of great significance for promoting the widespread application of privacy-preserving record linkage technology.

Privacy-preserving record linkage (PPRL) aims to link records from different data sources while ensuring sensitive information is not disclosed. Utilizing blockchain as a trusted third party is an effective strategy for enhancing transparency and auditability in PPRL. However, to ensure data privacy during computation, such approaches often require computationally intensive cryptographic techniques. This can introduce significant computational overhead, limiting the method’s efficiency and scalability. To address this performance bottleneck, we combine blockchain with the distributed computation of secret sharing to propose a PPRL method based on blockchain-coordinated distributed computation. At its core, the approach utilizes Bloom filters to encode data and employs Boolean and arithmetic secret sharing to decompose the data into secret shares, which are uploaded to the InterPlanetary File System (IPFS). Combined with masking and random permutation mechanisms, it enhances privacy protection. Computing nodes perform similarity calculations locally, interacting with IPFS only a limited number of times, effectively reducing communication overhead. Furthermore, blockchain manages the entire computation process through smart contracts, ensuring transparency and correctness of the computation, achieving efficient and secure record linkage. Experimental results demonstrate that this method effectively safeguards data privacy while exhibiting high linkage quality and scalability.

Rechargeable aqueous zinc batteries are promising for large‐scale energy storage due to their low cost and high safety; however, their energy density has reached the ceiling based on conventional cathodes with a single cationic redox reaction mechanism. Herein, a highly reversible cathode of typical layered vanadium oxide is reported, which operates on both the cationic redox couple of V5+/V3+ accompanied by the Zn2+ storage and the anionic O–/O2– redox couple by anion hosting in an aqueous deep eutectic solvent electrolyte. The reversible oxygen redox delivers an additional capacity of ∼100 mAh g–1 at an operating voltage of ∼1.80 V, which increases the energy density of the cathode by ∼36%, endowing the cathode system a record high energy density of ∼506 Wh kg–1. The findings highlight new opportunities for the design of high‐energy zinc batteries with both Zn2+ and anions as charge carriers. A zinc battery operating on joint cationic and anionic redox chemistry of a vanadium oxide cathode in an aqueous deep eutectic solvent electrolyte is presented, where the reversible anionic O–/O2– by anion hosting delivers an excess capacity of ∼100 mAh g–1 with an voltage of ∼1.80 V, endowing the cathode a record high energy density of ∼506 Wh kg–1.

Small convolutional neural network (CNN)‐based models usually require transferring knowledge from a large model before they are deployed in computationally resource‐limited edge devices. Masked image modelling (MIM) methods achieve great success in various visual tasks but remain largely unexplored in knowledge distillation for heterogeneous deep models. The reason is mainly due to the significant discrepancy between the transformer‐based large model and the CNN‐based small network. In this paper, the authors develop the first heterogeneous self‐supervised knowledge distillation (HSKD) based on MIM, which can efficiently transfer knowledge from large transformer models to small CNN‐based models in a self‐supervised fashion. Our method builds a bridge between transformer‐based models and CNNs by training a UNet‐style student with sparse convolution, which can effectively mimic the visual representation inferred by a teacher over masked modelling. Our method is a simple yet effective learning paradigm to learn the visual representation and distribution of data from heterogeneous teacher models, which can be pre‐trained using advanced self‐supervised methods. Extensive experiments show that it adapts well to various models and sizes, consistently achieving state‐of‐the‐art performance in image classification, object detection, and semantic segmentation tasks. For example, in the Imagenet 1K dataset, HSKD improves the accuracy of Resnet‐50 (sparse) from 76.98% to 80.01%.