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Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. Due to the uncertainty of knowledge acquisition, the reliability and confidence of KG based on entity recognition and relationship extraction technology need to be evaluated. On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge reasoning is also uncertain and needs to be evaluated and verified. Collaborative reasoning of multi-source knowledge includes not only inferring new knowledge from multi-source knowledge, but also conflict detection, i.e. identifying erroneous knowledge or conflicts between knowledges. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open-source knowledge fusion, multi-knowledge graphs fusion, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.

Knowledge Tracing aims to assess students’ mastery of knowledge concepts in real time, playing a crucial role in providing personalized learning services in intelligent tutoring systems. In recent years, researchers have attempted to introduce Neural Architecture Search (NAS) into knowledge tracing tasks to automatically design more efficient network structures. However, existing NAS-based methods for Knowledge Tracing suffer from excessively large search spaces and slow search efficiency, which significantly constrain their practical applications. To address these limitations, this paper proposes an Adaptive Neural Architecture Search framework based on Transformers for KT, called ANT-KT. Specifically, we design an enhanced encoder that combines convolution operations with state vectors to capture both local and global dependencies in students’ learning sequences. Moreover, an optimized decoder with a linear attention mechanism is introduced to improve the efficiency of modeling long-term student knowledge state evolution. We further propose an evolutionary NAS algorithm that incorporates a model optimization efficiency objective and a dynamic search space reduction strategy, enabling the discovery of high-performing yet computationally efficient architectures. Experimental results on two large-scale real-world datasets, EdNet and RAIEd2020, demonstrate that ANT-KT significantly reduces time costs across all stages of NAS while achieving performance improvements on multiple evaluation metrics, validating the efficiency and practicality of the proposed method.

Head and neck squamous carcinoma (HNSC) poses a significant public health challenge due to its substantial morbidity. Nevertheless, despite advances in current treatments, the prognosis for HNSC remains unsatisfactory. To address this, single-cell RNA sequencing (RNA-seq) and bulk RNA-seq data combined with in vitro studies were conducted to examine the role of MYO5A (Myosin VA) in HNSC. Our investigation revealed an overexpression of MYO5A in HNSC that promotes HNSC migration in vitro. Remarkably, knockdown of MYO5A suppressed vimentin expression. Furthermore, analyzing the TCGA database evidenced that MYO5A is a risk factor for human papillomavirus positive (HPV+) HNSC (HR = 0.81, P  < 0.001). In high MYO5A expression HNSC, there was a low count of tumor infiltrating lymphocytes (TIL), including activated CD4+ T cells, CD8+ T cells, and B cells. Of note, CD4+ T cells and B cells were positively associated with improved HPV+ HNSC outcomes. Correlation analysis demonstrated a decreased level of immunostimulators in high MYO5A-expressing HNSC. Collectively, these findings suggest that MYO5A may promote HNSC migration through vimentin and involve itself in the process of immune infiltration in HNSC, advancing the understanding of the mechanisms and treatment of HNSC.

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.

Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. Recent deep learning models generally utilize graph neural networks to model the complex relationship between information cascade graph and future popularity, and have shown better prediction results compared with traditional methods. However, existing models adopt simple graph pooling strategies, e.g., summation or average, which prone to generate inefficient cascade graph representation and lead to unsatisfactory prediction results. Meanwhile, they often overlook the temporal information in the diffusion process which has been proved to be a salient predictor for popularity prediction. To focus attention on the important users and exclude noises caused by other less relevant users when generating cascade graph representation, we learn the importance coefficient of users and adopt sample mechanism in graph pooling process. In order to capture the temporal features in the diffusion process, we incorporate the inter-infection duration time information into our model by using LSTM neural network. The results show that temporal information rather than cascade graph information is a better predictor for popularity. The experimental results on real datasets show that our model significantly improves the prediction accuracy compared with other state-of-the-art methods.

Background Glioma is the most common and lethal type of malignant brain tumor. Accumulating evidence has highlighted that RNA binding protein APOBEC1 complementation factor (A1CF) is involved in various cellular processes by modulating RNA expression, and acts as an oncogene in breast cancer. However, the function of A1CF in glioma remained unclear. Methods Quantitative RT-PCR and western blot analysis were employed to detect the expression levels of A1CF, lncRNA family with sequence similarity 224 member A (FAM224A), miR-590-3p, zinc finger protein 143 (ZNF143) and ArfGAP with SH3 domain, ankyrin repeat and PH domain 3 (ASAP3) in glioma tissues and cell lines. The Cell Counting Kit-8 assay, migration and invasion assays, and flow cytometry analysis were conducted to evaluate the function of A1CF, FAM224A, miR-590-3p, ZNF143 and ASAP3 in the malignant biological behaviors of glioma cells. Moreover, luciferase reporter, RIP and ChIP assays were used to investigate the interactions among A1CF, FAM224A, miR-590-3p, ZNF143, ASAP3 and MYB. Finally, the xenograft tumor growth assay further ascertained the biological roles of A1CF, FAM224A and miR-590-3p in glioma cells. Results A1CF was upregulated and functioned as an oncogene via stabilizing and increasing FAM224A expression; moreover, high A1CF and FAM224A expression levels indicated a poorer prognosis for glioma patients. Conversely, miR-590-3p was downregulated and exerted a tumor-suppressive function in glioma cells. Inhibition of A1CF significantly restrained cell proliferation, migration and invasion, and promoted apoptosis by upregulating miR-590-3p in a FAM224A-dependent manner. FAM224A was a molecular sponge of miR-590-3p and they were in an RNA-induced silencing complex. ZNF143 was upregulated in glioma tissues and cell lines. MiR-590-3p could negatively modulate the expression of ZNF143 via binding to the ZNF143 3′ UTR. Moreover, ZNF143 participated in miR-590-3p-induced tumor-suppressive activity on glioma cells. ASAP3 and MYB were transcriptionally activated by ZNF143, and importantly, ZNF143 could directly target the promoter of FAM224A and stimulate its expression, collectively forming a positive feedback loop. Conclusions The present study clarifies that the A1CF-FAM224A-miR-590-3p-ZNF143 positive feedback loop conducts critical regulatory effects on the malignant progression of glioma cells, which provides a novel molecular target for glioma therapy.

Abnormal brain network connectivity is strongly implicated in the pathogenesis of schizophrenia. The striatum, consisting of the caudate and putamen, is the major treatment target for antipsychotics, the primary treatments for schizophrenia; however, there are few studies on the functional connectivity (FC) of striatum in drug‐naive early‐onset schizophrenia (EOS) patients. We examined the FC values of the caudate nucleus and putamen with whole brain by resting‐state functional magnetic resonance imaging (RS‐fMRI) and the associations with indices of clinical severity. Patients demonstrated abnormal FC between subregions of the putamen and both the visual network (left middle occipital gyrus) and default mode network (bilateral anterior cingulate, left superior frontal, and right middle frontal gyri). Furthermore, FC between dorsorostral putamen and left superior frontal gyrus correlated with both positive symptom subscore and total score on the Positive and Negative Syndrome Scale (PANSS). These findings demonstrate abnormal FC between the striatum and other brain areas even in the early stages of schizophrenia, supporting neurodevelopmental disruption in disease etiology and expression. We examined the functional connectivity of the caudate nucleus and putamen with whole brain in early‐onset schizophrenia by resting‐state functional magnetic resonance imaging and the associations with indices of clinical severity.

Materials research has driven the development of modern nano-electronic devices. In particular, research in magnetic thin films has revolutionized the development of spintronic devices 1 , 2 because identifying new magnetic materials is key to better device performance and design. Van der Waals crystals retain their chemical stability and structural integrity down to the monolayer and, being atomically thin, are readily tuned by various kinds of gate modulation 3 , 4 . Recent experiments have demonstrated that it is possible to obtain two-dimensional ferromagnetic order in insulating Cr 2 Ge 2 Te 6 (ref. 5 ) and CrI 3 (ref. 6 ) at low temperatures. Here we develop a device fabrication technique and isolate monolayers from the layered metallic magnet Fe 3 GeTe 2 to study magnetotransport. We find that the itinerant ferromagnetism persists in Fe 3 GeTe 2 down to the monolayer with an out-of-plane magnetocrystalline anisotropy. The ferromagnetic transition temperature, T c , is suppressed relative to the bulk T c of 205 kelvin in pristine Fe 3 GeTe 2 thin flakes. An ionic gate, however, raises T c to room temperature, much higher than the bulk T c . The gate-tunable room-temperature ferromagnetism in two-dimensional Fe 3 GeTe 2 opens up opportunities for potential voltage-controlled magnetoelectronics 7 – 11 based on atomically thin van der Waals crystals. Monolayers of Fe 3 GeTe 2 exhibit itinerant ferromagnetism with an out-of-plane magnetocrystalline anisotropy; ionic gating raises the ferromagnetic transition temperature of few-layer Fe 3 GeTe 2 to room temperature.

Fritillaria delavayi has been widely used as a traditional Chinese medicine (TCM) to treat respiratory diseases for thousands of years. In this study, the complete chloroplast genome of F. delavayi was assembled. The circular genome is 151,938 bp in size, which is comprised of one large single-copy (LSC) region of 81,757 bp and one small single-copy (SSC) region of 17,537 bp and separated by a pair of inverted repeat (IR) regions of 26,322 bp. A total of 112 unique genes (78 protein-coding, 30 tRNA, and 4 rRNA) are predicted and 19 of them are duplicated in IR regions. The overall GC content is 37.0% while the GC content of the LSC, SSC, and IR regions are 34.8, 30.5, and 42.5%, separately. Phylogenetic analysis indicated that F. delavayi was closely related to F. cirrhosa.

Two-dimensional materials have emerged as an important research frontier for overcoming the challenges in nanoelectronics and for exploring new physics. Among them, black phosphorus, with a combination of a tunable bandgap and high mobility, is one of the most promising systems. In particular, black phosphorus nanoribbons show excellent electrostatic gate control, which can mitigate short-channel effects in nanoscale transistors. Controlled synthesis of black phosphorus nanoribbons, however, has remained an outstanding problem. Here we report large-area growth of black phosphorus nanoribbons directly on insulating substrates. We seed the chemical vapour transport growth with black phosphorus nanoparticles and obtain uniform, single-crystal nanoribbons oriented exclusively along the [100] crystal direction. With comprehensive structural calculations, we discover that self-passivation at the zigzag edges holds the key to the preferential one-dimensional growth. Field-effect transistors based on individual nanoribbons exhibit on/off ratios up to ~10 4 , confirming the good semiconducting behaviour of the nanoribbons. These results demonstrate the potential of black phosphorus nanoribbons for nanoelectronic devices and also provide a platform for investigating the exotic physics in black phosphorus. Single-crystal black phosphorus nanoribbons are grown uniformly on insulating substrates by chemical vapour transport growth with black phosphorus nanoparticles as seeds, demonstrating potential for application in nanoelectronic devices and the exploration of the exotic physics in black phosphorus.