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It has become a cardinal task to identify fake information (misinformation) on social media, because it has significantly harmed the government and the public. There are many spam bots maliciously retweeting misinformation. This study proposes an efficient model for detecting misinformation with self-supervised contrastive learning. A Bayesian graph Local extrema Convolution (BLC) is first proposed to aggregate node features in the graph structure. The BLC approach considers unreliable relationships and uncertainties in the propagation structure, and the differences between nodes and neighboring nodes are emphasized in the attributes. Then, a new long-tail strategy for matching long-tail users with the global social network is advocated to avoid over-concentration on high-degree nodes in graph neural networks. Finally, the proposed model is experimentally evaluated with two public Twitter datasets and demonstrates that the proposed long-tail strategy significantly improves the effectiveness of existing graph-based methods in terms of detecting misinformation. The robustness of BLC has also been examined on three graph datasets and demonstrates that it consistently outperforms traditional algorithms when perturbed by 15% of a dataset. [ABSTRACT FROM AUTHOR]

Tin-based materials, characterized by their high specific capacities, show significant promise for use in the next generation of lithium-ion batteries (LIBs). However, the large volume change of Sn-based materials upon operation significantly compromises the battery performance. To solve this problem, a nanoporous Ni current collector with cone arrays is developed using a template-free method to support a Sn–Co alloy as an anode for LIBs. Interestingly, the Sn–Co alloy inherits the nanoporous cone array structure of the Ni current collector. Thus, the nanoporous and cone array-structured electrode provides abundant interspace to alleviate the mechanical stress of the Sn-based anode caused by volume changes and facilitate rapid mass transfer. Besides, the in situ generated inert Co dispersed in the electrode can further buffer the volume expansion of the Sn-based anode. Due to the synergistic effect, the electrode shows a high initial discharge/charge capacity of 1.34/0.64 mA h cm−2 and a reversible capacity of 0.89 mA h cm−2 with high coulombic efficiency (99.44%) even after 200 cycles at 1 mA cm−2. Moreover, the electrode also demonstrates excellent rate performance (0.55 mA h cm−2 at 8 mA cm−2). This work provides a novel design idea for the preparation of high-performance tin-based electrodes for next-generation lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Radiotherapy is a commonly used method in the treatment of bladder cancers (BC). Radiation-induced immunogenic cell death (ICD) is related to the immune response against cancers and their prognoses. Even though dendritic cells (DC) act as powerful antigen-presenting cells in the body, their precise role in this ICD process remains unclear. Accordingly, an in vitro study was undertaken to ascertain whether high-dose radiation-induced ICD of BC cells could regulate the immune response of DC. The results indicated that high-dose radiation treatments of BC cells significantly increased their levels of apoptosis, blocked their cell cycle in the G2/M phase, increased their expression of ICD-related proteins, and upregulated their secretion of CCL5 and CCL21 which control the directed migration of DC. It was also noted that expression of CD80, CD86, CCR5, and CCR7 on DC was upregulated in the medium containing the irradiated cells. In conclusion, the present findings illustrate that high-dose radiation can induce the occurrence of ICD within BC cells, concomitantly resulting in the activation of DC. Such findings could be of great significance in increasing the understanding how radiotherapy of BC may work to bring about reductions in cell activity and how these processes in turn lead to immunoregulation of the function of DC. [ABSTRACT FROM AUTHOR]

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of root mean squared error (RMSE), mean absolute error (MAE) and normalized discounted cumulative gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets. [ABSTRACT FROM AUTHOR]