Publications by authors named "Ju Wei"

The heteroepitaxy of 2D materials with engineered bandgaps are crucial to broaden the spectral response for their integrated optoelectronic devices. However, it is a challenge to achieve the high-oriented epitaxy and integration of multicomponent 2D materials with varying lattice constants on the same substrate due to the limitation of lattice matching. Here, in-plane adaptive heteroepitaxy of a series of high-oriented 2D cesium bismuth halide (CsBiX X = I, Br, Cl) single crystals with varying lattice constants from 8.

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  • Nonlinear optical responses in 2D materials can enable advanced free-space optical neuromorphic computing, offering a blend of high performance and tunability for diverse functions.
  • Challenges arise from conventional methods that struggle to balance performance and flexibility, often leading to compromises.
  • A new approach using bare molybdenum disulfide arrays enhances modulation performance and energy efficiency while improving tunability, showcasing the potential for optical artificial neural networks and digital processing in neuromorphic applications.
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Genetically encoded green fluorescent protein (GFP) and its brighter and redder variants have tremendously revolutionized modern molecular biology and life science by enabling direct visualization of gene regulated protein functions on microscopic and nanoscopic scales. However, the current fluorescent proteins (FPs) only emit a few colors with an emission width of about 30-50 nm. Here, we engineer novel vibrational proteins (VPs) that undergo much finer vibrational transitions and emit rather narrow vibrational spectra (0.

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Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in the real world, labeled graph data are always limited or scarce. To address this issue, we focus on the semi-supervised graph classification task, which involves both supervised and unsupervised models learning from labeled and unlabeled data.

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As an oleanolic acid derivative, CDDO-Me lacks selectivity for tumors. Based on the high reactive oxygen species (ROS) level in cancer cells, compound was selected from 17 new CDDO arylboronate ester derivatives. A preliminary study revealed that displayed the highest selectivity for cancer cells.

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Recognizing an individual's preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology's practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction.

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Membrane fouling is a bottleneck issue that hindered the further application of ultrafiltration technology. To alleviate membrane fouling, coagulation-ultrafiltration (C-UF) process using polyaluminum chloride (PACl) and PACl-Al with high proportion of AlO(OH) as coagulants, respectively, were investigated at various pH conditions. Results indicated that an increase in solution pH contributed to larger floc size and looser floc structure for both PACl and PACl-Al.

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  • Source-free domain adaptation is an important area in machine learning that addresses issues related to data privacy, mainly focusing on image and video data while neglecting graph data.
  • The proposed GALA method utilizes a graph diffusion model to align graphs from different domains, reconstructing source-style graphs from target data, and addresses domain shift and label scarcity challenges.
  • Extensive experiments demonstrate that GALA outperforms existing methods, showcasing techniques like score-based diffusion and graph mixing to enhance graph adaptation performance, with the source code available for further exploration.
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Catalytic ozonation is an effective wastewater purification process. However, the low ozone mass transfer in packed bubble columns leads to low ozone utilization efficiency (OUE), poor organic degradation performance, and high energy consumption. Therefore, there is an urgent need to develop efficient supported catalysts that can enhance mass transfer and performance.

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For metal-based phosphate adsorbents, the dispersity and utilization of surface metal active sites are crucial factors in their adsorption performance and synthesis cost. In this study, a biochar material modified with amorphous Zr-Ce (carbonate) oxides (BZCCO-13) was synthesized for the phosphate uptake, and the adsorption process was enhanced by magnetic field. The beside-magnetic field was shown to have a better influence than under-magnetic field on adsorption, with maximum adsorption capacities (123.

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In this paper, a carbon dot hydrogel composite (CDs-Hy) capable of efficiently removing Pb(II) was prepared by hydrogen bonding self-assembly in combination with carbon dots and a hydrogel. CDs-Hy was characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS), and the effect of the adsorption conditions on the adsorption efficiency of CDs-Hy was studied. The results of the study showed that the incorporation of carbon dots, on the one hand, significantly increased the adsorption capacity of the material.

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Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced.

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Background: Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC.

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Malignant melanoma (MM) is a highly aggressive tumour that can easily metastasize through the lymphatic system at the early stages. Lymph node (LN) involvement and lymphatic vessel (LV) density (LVD) represent a harbinger of an adverse prognosis, indicating a strong link between the state of the lymphatic system and the advancement of MM. Permeable capillary lymphatic vessels are the optimal conduits for melanoma cell (MMC) invasion, and lymphatic endothelial cells (LECs) can also release a variety of chemokines that actively attract MMCs expressing chemokine ligands through a gradient orientation.

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  • Tendinopathy is a serious condition caused by the degeneration of tendon tissue, which can lead to loss of function and tendon ruptures, and currently has no effective treatments to stop its progression.
  • Exosomes are tiny vesicles that carry important substances like proteins and RNAs between cells and are produced by mesenchymal stem cells (MSCs), playing a key role in cell communication and body regulation.
  • Recent studies indicate that exosomes derived from MSCs may be promising in promoting tendon healing and could offer new treatment options for tendinopathy.
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  • Graph clustering is a crucial data analysis task that uses graph neural networks but often fails to consider the relationships between nodes, leading to subpar clustering results.
  • The authors introduce a new method called relational redundancy-free graph clustering (RFGC) that captures both attribute and structural relationships in graphs, aiming to improve node representation and clustering effectiveness.
  • RFGC uses an autoencoder and a graph autoencoder to preserve important relationships while reducing redundant ones, and it also addresses oversmoothing issues, demonstrating better performance on benchmark datasets compared to existing methods.
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Objective: The aim of this study was to investigate angiopoietin-2 (Ang-2/ANGPT2) expression and its relationship with lymphangiogenesis and clinicopathological characteristics in cutaneous malignant melanoma (CMM).

Methods: Gene expression differences between metastatic melanoma and melanoma in 472 patients from the TCGA database were analyzed. The target gene Ang-2 was screened.

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Accurate assessment of Respiratory Rate (RR) is the most important mechanism in detecting pneumonia in low-resource settings. Pneumonia is a disease with one of the highest mortality rates among young children under five. However, the diagnosis of pneumonia for infants remains challenging, especially in low- and middle-income countries (LMIC).

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Plant metaxylem vessels provide physical support to promote upright growth and the transport of water and nutrients. A detailed characterization of the molecular network controlling metaxylem development is lacking. However, knowledge of the events that regulate metaxylem development could contribute to the development of germplasm with improved yield.

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Objective: To clarify the effects on the development, position and morphology of the permanent successors of primary molars affected by apical periodontitis (AP).

Method: A total of 132 panoramic radiographs of children aged from 4 to 10 were screened out and a total of 159 mandibular second primary molars with chronic apical periodontitis(AP)(93 males and 66 females) were analyzed. The maturation values of permanent successors were interpreted and scored according to Nolla's method and compared with normal ones'.

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Background: Renal interstitial fibrosis (RIF) is a common pathway to end-stage renal disease regardless of the initial etiology. Currently, the molecular mechanisms for RIF remains not fully elucidated. Nuclear receptor subfamily 4 group A member 1(Nr4a1), a member of the NR4A subfamily of nuclear receptors, is a ligand-activated transcription factor.

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  • This paper addresses the challenge of few-shot molecular property prediction, crucial for cheminformatics and drug discovery, focusing on the limitations of current graph neural network methods due to the lack of available molecules with desired properties.
  • The authors introduce a new framework called Hierarchically Structured Learning on Relation Graphs (HSL-RG), which effectively captures molecular structure at both global and local levels using graph kernels and self-supervised learning techniques.
  • Experimental results demonstrate that HSL-RG outperforms existing state-of-the-art methods across multiple benchmark datasets, highlighting its effectiveness in few-shot learning scenarios.
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Direct counting and mapping the chain lengths of fatty acids on a microscopic scale are of particular importance but remain an unsolvable challenge. Although the current hyperspectral stimulated Raman scattering (SRS) microscopy has gained exceptional capability in chemical imaging of the degree of desaturation, the complete lipid characterization, including the carbon chain length quantification, is awaiting a major breakthrough. Here, we pushed the spectral resolution limit of hyperspectral SRS microscopy to 5.

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  • Sebaceous carcinoma of the submandibular gland is a rare condition, with only five reported cases, often leading to delayed diagnosis and increased risk of metastasis and mortality.
  • The documented case involved a 36-year-old woman who had a non-tender, swollen mass in the left submandibular gland, which was confirmed as sebaceous carcinoma after surgical resection and pathological examination.
  • The study emphasizes the importance of considering sebaceous carcinoma in differential diagnoses for head and neck masses, and highlights that early diagnosis and surgical treatment can lead to a favorable outcome.
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Saliva is a noninvasive biofluid that contains the metabolic signature of severe periodontitis (SP, Stage IV and Grade C). Conductive polymer spray ionization mass spectrometry (CPSI-MS) was used to record a wide range of metabolites within a few seconds, making this technique a promising point-of-care method for the early detection of SP (Stage IV and Grade C). Saliva samples from 31 volunteers, consisting of 16 healthy controls (HC) and 15 patients with SP (Stage IV and Grade C), were collected to identify dysregulated metabolites.

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