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Despite recent advances in immunotherapy, its efficacy remains constrained by the absence of immune coordination. Especially, the interplay between tumor‐draining lymph nodes (TDLNs) and tumors is frequently disregarded. Here, a self‐adjuvanting hydrogel capable of eliciting a powerful and sustained immune response is developed. Briefly, the engineered arabinose response bacteria (ARB) expressing IL‐15 and mannose‐modified hollow mesoporous Prussian blue nanoparticles (NPs) loaded with vitamin E (Man/HMPB(VE), MHV) are mixed with arabinose hydrogel (AraGel), forming the system designated as AraGel@ARB/MHV (AAM). Employing mild photothermal therapy mediated by MHV, immunogenic cell death (ICD) triggers the release of tumor‐associated antigens. Subsequently, Man‐modified NPs target TDLNs and release VE, which suppresses the checkpoint Src homology region 2 domain‐containing phosphatase‐1 (SHP1) in dendritic cells, thereby enhancing antigen presentation and T cell activation. Meanwhile, IL‐15 expression of ARB(IL‐15) induced by AraGel degradation enables ARB to serve as an enhanced adjuvant in a self‐adjuvanting manner, working synergistically with ICD and TDLN reprogramming to promote cytotoxic T lymphocytes activation. The hydrogel system efficiently suppresses tumor growth by eliciting prolonged and powerful immunotherapy in an orchestrated manner. Overall, the self‐adjuvanting hydrogel holds great potential for cancer immunotherapy. [ABSTRACT FROM AUTHOR]

The long short-term memory (LSTM) network is especially suitable for dealing with time series-related problems, which has led to a wide range of applications in analyzing stock market quotations and predicting future price trends. However, the selection of hyperparameters in LSTM networks was often based on subjective experience and existing research. The inability to determine the optimal values of the parameters results in a reduced generalization capability of the model. Therefore, we proposed a sparrow search algorithm-optimized LSTM (SSA-LSTM) model for stock trend prediction. The SSA was used to find the optimal hyperparameters of the LSTM model to adapt the features of the data to the structure of the model, so as to construct a highly accurate stock trend prediction model. With the Shanghai Composite Index stock data in the last decade, the mean absolute percentage error, root mean square error, mean absolute error, and coefficient of determination between stock prices predicted by the SSA-LSTM method and actual prices are 0.0093, 41.9505, 30.5300, and 0.9754. The result indicates that the proposed model possesses higher forecasting precision than other traditional stock forecasting methods and enhances the interpretability of the network model structure and parameters. [ABSTRACT FROM AUTHOR]

The malicious flow originating from massive access devices in 6G network will increase sharply. In order to effectively reduce malicious flow, we hope to establish a new framework for coordination of security monitoring and malicious behaviour control in 6G network. Federated learning provides data and privacy protection for the distributed network security behaviour knowledge base. However, since the equipment of its participants needs to upload the original data to the central server for model training, this may lead to data leakage in the knowledge base. Therefore, in this article, we first use the knowledge graph to describe network security behaviours, then build a universal network security malicious behaviour knowledge base, and discuss its application scenarios. Then, we propose a blockchain empowered federated learning (BeFL) for distributed network security malicious behaviour knowledge base architecture to ensure the security of knowledge transmission. Finally, we deployed the designed distributed knowledge base in the prototype system and compared it with the other two baseline methods to verify the performance. Relevant results show that our method outperforms other methods in terms of user identification, flow detection, and attack source tracing. [ABSTRACT FROM AUTHOR]

Metastatic cancer is difficult to cure because of its uncontrollable nature and side effects during treatment. We constructed a reactive oxygen species (ROS)‐activated smart theranostic prodrug system based on an ROS active site linked with both a targeting group and an anticancer drug for efficient regional chemotherapy of metastatic cancers. The optimized prodrug (Bio‐(8)‐MB‐CPT) with biotin as the targeting group displayed high sensitivity towards ROS and selectively targeting ability towards cervical cancer cells, showing highly efficient drug release (up to 92 %) in vitro. Bio‐(8)‐MB‐CPT thus exerted strong toxicity towards cervical cancer cells, but unlike the parent drug (camptothecin), showed no toxicity towards normal cells. Moreover, the prodrug displayed significantly enhanced antitumor efficacy in vivo and eradicated the tumor with no obvious side effects (inhibition of the tumor reached up to 99.9 %). [ABSTRACT FROM AUTHOR]

Tumor microenvironment‐responsive nanodrugs offer promising opportunities for imaging‐guided precision therapy with reduced side effects. Considering that the antitumor effect is closely related to the size of the nanodrugs, it is particularly important to develop a therapeutic system with size adjustability in the tumor microenvironment, which is still a great challenge in the field of nanotheranostics. Herein, a reactive oxygen species (ROS)‐activated aggregation strategy is reported for imaging‐guided precision therapy of tumors. The ROS‐activated nanoplatform is constructed based on gold nanoparticles (AuNPs) coated with an HOCl probe on its surface (namely, Au–MB–PEG NPs). The Au–MB–PEG NPs show high sensitivity toward HOCl, resulting in the modulation of surface charge and rapid aggregation of AuNPs, and simultaneous release of methylene blue as a photosensitizer for photodynamic therapy (PDT). In the tumor environment, the aggregated AuNPs ensure higher tumor accumulation and retention. Furthermore, the redshift of the absorption of aggregated AuNPs leads to activated photoacoustic imaging signals and photothermal therapy (PTT) under near‐infrared irradiation. Au–MB–PEG NPs thus efficiently inhibit the tumor growth through combined PTT–PDT therapy. This work contributes to the design of stimuli‐induced size‐aggregation nanodrugs, thereby attaining advanced performance in cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Supramolecular gel systems provide an ideal platform for the study of conjugated polydiacetylene (PDA) in soft materials. Compared with the well studied amphiphiles, the bolaamphiphilic molecules received much less attention in the fabrication of supramolecular gels. It's a big challenge to develop bolaamphiphilic type cogelators with reasonable molecular structures for the preparation of PDA supramolecular gels. Herein, a novel bolaamphiphilic cogelator containing an imidazole group at both ends was designed and coassembled with commercially available diacetylene acid monomer at 1:2 molar ratio by multiple non‐covalent interactions. A white supramolecular gel was successfully prepared through the conventional heating‐cooling method and systematically characterized by 1H‐NMR, FT‐IR, XRD, SEM and rheology test. After UV irradiation, blue PDA was polymerized in the supramolecular gel system, as verified by UV‐Vis absorption and Raman spectroscopy. When heated to 55°C and 75°C, a successive blue‐to‐purple‐to‐red colorimetric transition was observed for the PDA supramolecular gel. Interestingly, after cooled to room temperature, the purple PDA could return to the original blue phase reversibly, while the red PDA remained in the red phase with an obvious fluorescence enhancement effect. This work would promote the investigation of bolaamphiphilic molecules as well as conjugated PDA in soft materials. [ABSTRACT FROM AUTHOR]

Helicopter‐borne rotating synthetic aperture radar (ROSAR) enjoys fast imaging property as the synthetic aperture obtained by rotor rotation. However, ROSAR data processing is usually a challenging task due to severe range cell migration (RCM) and motion error. To solve this problem, an enhanced phase gradient autofocus (PGA) algorithm based on helicopter‐borne ROSAR wavenumber‐domain imaging approach is proposed in this study, which alleviates the imaging performance degradation due to the RCM and motion error. With even small motion error induced in the wavenumber domain, the influence of motion error will become evident after performing Stolt interpolation, which induces serious image defocusing and degradation. Hence, the authors further propose the PGA‐based ROSAR motion compensation scheme, which combines quadratic term correction with phase gradient estimation, and well‐focused ROSAR images can be obtained via iterative processing. Several results of simulated experiments are presented to validate the proposed method for helicopter‐borne ROSAR imaging. [ABSTRACT FROM AUTHOR]