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Large pre-trained models (PLMs) have provided tremendous opportunities and potentialities for multimodal fake news detection. However, existing multimodal fake news detection methods never manipulate the token-wise hierarchical semantics of news yielded from PLMs and extremely rely on contrastive learning but ignore the symmetry between text and image in terms of the abstract level. This paper proposes a novel multimodal fake news detection method that helps to balance the understanding between text and image via (1) designing a global-token across-attention mechanism to capture the correlations between global text and tokenwise image representations (or tokenwise text and global image representations) obtained from BERT and ViT; (2) proposing a QK-sharing strategy within cross-attention to enforce model symmetry that reduces information redundancy and accelerates fusion without sacrificing representational power; (3) deploying a semantic augmentation module that systematically extracts token-wise multilayered text semantics from stacked BERT blocks via CNN and Bi-LSTM layers, thereby rebalancing abstract-level disparities by symmetrically enriching shallow and deep textual signals. We also prove the effectiveness of our approach by comparing it with four state-of-the-art baselines. All the comparisons were conducted using three widely adopted multimodal fake news datasets. The results show that our approach outperforms the benchmarks by 0.8% in accuracy and 2.2% in F1-score on average across the three datasets, which demonstrates a symmetric, token-centric fusion of fine-grained semantic fusion, thereby driving more robust fake news detection.

High‐entropy materials, composed of five or more elements in near‐equiatomic percentage, have been attracting tremendous interests due to their advantageous properties in a variety of applications. Recently, electrocatalysis on high‐entropy alloys (HEAs) and high‐entropy compounds (HECs) has emerged as a new and promising material owing to the tailored composition and the disordered configuration of HEAs and HECs. Though extensive efforts have been devoted to investigating the catalytic nature of HEAs and HECs, the details related to the active sites and intrinsic activity of such catalysts still remain uncertain due to the complexity of the multicomponent systems. In this review, the recent progress of HEAs and HECs is systematically reviewed in terms of their synthetic strategies and electrocatalytic applications. Importantly, the computationally assisted methods (e.g., density functional theory [DFT]) are also presented to discover and design the optimum HEA‐ and HEC‐based catalysts. Subsequently, the applications of HEAs and HECs in electrocatalytic energy conversion reactions will be discussed, including hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, carbon dioxide reduction reaction, nitrogen reduction reaction, methanol oxidation reaction, and ethanol oxidation reaction (EOR). Moreover, the prospects and future opportunities for this research field are cautiously discussed. A series of upcoming challenges and questions are thoroughly proposed from the experimental and theoretical aspects as well as other future applications in electrocatalysis. In this review, the recent progress of high‐entropy alloys (HEAs) and high‐entropy compounds (HECs) is systematically reviewed in terms of their synthetic strategies and electrocatalytic applications. Subsequently, the applications of HEAs and HECs in electrocatalytic energy conversion reactions are discussed, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2 RR), nitrogen reduction reaction (NRR), methanol oxidation reaction (MOR), and ethanol oxidation reaction (EOR). Moreover, the prospects and future opportunities for this research field are cautiously discussed.

Spintronics-based microwave devices, such as oscillators and detectors, have been the subject of intensive investigation in recent years owing to the potential reductions in size and power consumption. However, only a few concepts for spintronic amplifiers have been proposed, typically requiring complex device configurations or material stacks. Here, we demonstrate a spintronic amplifier based on two-terminal magnetic tunnel junctions (MTJs) produced with CMOS-compatible material stacks that have already been used for spin-transfer torque memories. We achieve a record gain (| S 11  | > 2) for input power on the order of nW (<−40 dBm) at an appropriate choice of the bias field direction and amplitude. Based on micromagnetic simulations and experiments, we describe the fundamental aspects driving the amplification and show the key role of the co-existence in microwave emissions of a dynamic state of the MTJ excited by a dc current and the injection locking mode driven by the microwave input signal. Our work provides a way to develop a class of compact amplifiers that can impact the design of the next generation of spintronics-CMOS hybrid systems. Microwave devices are instrumental in wireless communications. Recently, spintronic-based microwave devices have seen significant interest, with the potential for smaller size, and lower power consumption. Here, Zhu et al demonstrate a spintronic amplifier with record gain, which uses material stacks already employed in industrially fabricated magnetic memories.

Background Tuberculosis (TB) caused by Mycobacterium tuberculosis ( M. tb ) remains a global health issue. The characterized virulent M. tb H37Rv, avirulent M. tb H37Ra and BCG strains are widely used as reference strains to investigate the mechanism of TB pathogenicity. Here, we attempted to determine metabolomic signatures associated with the Mycobacterial virulence in human macrophages through comparison of metabolite profile in THP-1-derived macrophages following exposure to the M. tb H37Rv, M. tb H37Ra and BCG strains. Results Our findings revealed remarkably changed metabolites in infected macrophages compared to uninfected macrophages. H37Rv infection specifically induced 247 differentially changed metabolites compared to H37Ra or BCG infection. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed H37Rv specifically induces tryptophan metabolism. Moreover, quantitative PCR (qPCR) results showed that indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) which converts the tryptophan to a series of biologically second metabolites were up-regulated in H37Rv-infected macrophages compared to H37Ra- or BCG-infected macrophages, confirming the result of enhanced tryptophan metabolism induced by H37Rv infection. These findings indicated that targeting tryptophan (Trp) metabolism may be a potential therapeutic strategy for pulmonary TB. Conclusions We identified a number of differentially changed metabolites that specifically induced in H37Rv infected macrophages. These signatures may be associated with the Mycobacterial virulence in human macrophages. The present findings provide a better understanding of the host response associated with the virulence of the Mtb strain.

In the past few decades, traditional methods for developing amorphous alloys, such as empirical trial-and-error approaches and density functional theory (DFT)-based calculations, have enabled researchers to explore numerous amorphous alloy systems and investigate their properties. However, these methods are increasingly unable to meet the demands of modern research due to their long development cycles and low efficiency. In contrast, machine learning (ML) has gained widespread adoption in the design, analysis, and property prediction of amorphous alloys due to its advantages of low experimental cost, powerful performance, and short development cycles. This review focuses on four key applications of ML in amorphous alloys: (1) prediction of amorphous alloy phases, (2) prediction of amorphous composite phases, (3) prediction of glass-forming ability (GFA), and (4) prediction of material properties. Finally, we outline future directions for ML in materials science, including the development of more sophisticated models, integration with high-throughput experimentation, and the creation of standardized data-sharing platforms. These insights provide potential research directions and frameworks for subsequent studies in this field.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Heterogeneous nuclear ribonucleoprotein H1 (hnRNPH1) is a multifunctional RNA-binding protein (RBP) that plays a central role in post-transcriptional regulation. Through its quasi-RNA recognition motifs and low-complexity domains, hnRNPH1 specifically binds guanine-rich RNA sequences, including G-quadruplex structures, to precisely modulate multiple aspects of RNA metabolism, such as alternative splicing, mRNA stability, translation, and subcellular localization. Accumulating evidence has implicated hnRNPH1 dysfunction in the pathogenesis of several human diseases. In cancer, hnRNPH1 often acts as a pro-tumorigenic factor, albeit in a context-dependent manner, influencing the alternative splicing of crucial oncogenes, mRNA stability, and tumor cell sensitivity to therapeutic agents. In the nervous system, hnRNPH1 is involved in neurodevelopment, neurodegenerative diseases, and drug addiction and plays an essential role in maintaining neuronal function and homeostasis. Furthermore, it exerts regulatory functions in reproductive system development and fertility and in non-neoplastic pathologies, including cardiovascular diseases, autoimmune disorders, and viral hepatitis. Given its pathophysiological significance, hnRNPH1 has emerged as a promising biomarker and therapeutic target. This review provides an overview of the structural basis and core molecular function of hnRNPH1. Its mechanisms of action and pathological significance in various diseases have also been detailed. Additionally, this review summarizes the current therapeutic strategies targeting hnRNPH1, discusses the associated challenges, outlines optimization approaches, and considers future research directions. Overall, this review aims to deepen our understanding of hnRNPH1 biology and inspire the development of novel diagnostic and therapeutic interventions.

To address the problem that the performance of hyperspectral target tracking will be degraded when facing background clutter, this paper proposes a novel hyperspectral target tracking algorithm based on the deep edge convolution feature (DECF) and an improved context filter (ICF). DECF is a fusion feature via deep features convolving 3D edge features, which makes targets easier to distinguish under complex backgrounds. In order to reduce background clutter interference, an ICF is proposed. The ICF selects eight neighborhoods around the target as the context areas. Then the first four areas that have a greater interference in the context areas are regarded as negative samples to train the ICF. To reduce the tracking drift caused by target deformation, an adaptive scale estimation module, named the region proposal module, is proposed for the adaptive estimation of the target box. Experimental results show that the proposed algorithm has satisfactory tracking performance against background clutter challenges.