Bottle Nanomotors Amplify Tumor Oxidative Stress for Enhanced Calcium Overload/Chemodynamic Therapy.

Developing multifunctional, stimuli-responsive nanomedicine is intriguing because it has the potential to effectively treat cancer. Yet, poor tumor penetration of nanodrugs results in limited antitumor efficacy. Herein, an oxygen-driven silicon-based nanomotor (Si-motor) loaded with MnO and CaO2 nanoparticles is developed, which can move in tumor microenvironment (TME) by the cascade reaction of CaO2 and MnO. Under acidic TME, CaO2 reacts with acid to release Ca2+ to induce mitochondrial damage and simultaneously produces O2 and H2O2, when the loaded MnO exerts Fenton-like activity to produce ·OH and O2 based on the produced H2O2. The generated O2 drives Si-motor forward, thus endowing active delivery capability of the formed motors in TME. Meanwhile, MnO with glutathione (GSH) depletion ability further prevents reactive oxygen species (ROS) from being destroyed. Such TME actuated Si-motor with enhanced cellular uptake and deep penetration provides amplification of synergistic oxidative stresscaused by intracellular Ca2 + overloading, GSH depletion induced by Mn2+, and Mn2+ mediated chemodynamic treatment (CDT), leading to excellent tumor cell death. The created nanomotor may offer an effective platform for active synergistic cancer treatment.

Light-Driven Biomimetic Nanomotors for Enhanced Photothermal Therapy.

Nanotechnology-based strategy has recently drawn extensive attention for the therapy of malignant tumors due to its distinct strengths in cancer diagnosis and treatment. However, the limited intratumoral permeability of nanoparticles is a major hurdle to achieving the desired effect of cancer treatment. Due to their superior cargo towing and reliable penetrating property, micro-/nanomotors (MNMs) are considered as one of the most potential candidates for the coming generation of drug delivery platforms. Here, near-infrared (NIR)-actuated biomimetic nanomotors (4T1-JPGSs-IND) are fabricated successfully and we demonstrate that 4T1-JPGSs-IND selectively accumulate in homologous tumor regions due to the effective homing ability. Upon laser irradiation, hyperthermia generated by 4T1-JPGSs-IND leads to self-thermophoretic motion and photothermal therapy (PTT) to ablate tumors with a deep depth, thereby improving the photothermal therapeutic effect for cancer management. The developed nanomotor system with multifunctionalities exhibits promising potential in biomedical applications to fight against various diseases.

Control the Neural Stem Cell Fate with Biohybrid Piezoelectrical Magnetite Micromotors.

Inducing neural stem cells to differentiate and replace degenerated functional neurons represents the most promising approach for neural degenerative diseases including Parkinson's disease, Alzheimer's disease, etc. While diverse strategies have been proposed in recent years, most of these are hindered due to uncontrollable cell fate and device invasiveness. Here, we report a minimally invasive micromotor platform with biodegradable helical Spirulina plantensis (S. platensis) as the framework and superparamagnetic Fe3O4 nanoparticles/piezoelectric BaTiO3 nanoparticles as the built-in function units. With a low-strength rotational magnetic field, this integrated micromotor system can perform precise navigation in biofluid and achieve single-neural stem cell targeting. Remarkably, by tuning ultrasound intensity, thus the local electrical output by the motor, directed differentiation of the neural stem cell into astrocytes, functional neurons (dopamine neurons, cholinergic neurons), and oligodendrocytes, can be achieved. This micromotor platform can serve as a highly controllable wireless tool for bioelectronics and neuronal regenerative therapy.

Apoptotic Tumor DNA Activated Nanomotor Chemotaxis.

Inspired by the tactic organisms in Nature that can self-direct their movement following environmental stimulus gradient, we proposed a DNase functionalized Janus nanoparticle (JNP) nanomotor system for the first time, which can be powered by ultralow nM to µM levels of DNA. The system exhibited interesting chemotactic behavior toward a DNA richer area, which is physiologically related with many diseases including tumors. In the presence of the subtle DNA gradient generated by apoptotic tumor cells, the cargo loaded nanomotors were able to sense the DNA signal released by the cells and demonstrate directional motion toward tumor cells. For our system, the subtle DNA gradient by a small amount (10 µL) of tumor cells is sufficient to induce the chemotaxis behavior of self-navigating and self-targeting ability of our nanomotor system, which promises to shed new light for tumor diagnosis and therapy.

Relations Among Some Low-Rank Subspace Recovery Models.

Recovering intrinsic low-dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, a lot of work has modeled subspace recovery as low-rank minimization problems. We find that some representative models, such as robust principal component analysis (R-PCA), robust low-rank representation (R-LRR), and robust latent low-rank representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low-rank subspace recovery models. Our work has two important implications. First, R-PCA has a solid theoretical foundation. Under certain conditions, we could find globally optimal solutions to these low-rank models at an overwhelming probability, although these models are nonconvex. Second, we can obtain significantly faster algorithms for these models by solving R-PCA first. The computation cost can be further cut by applying low-complexity randomized algorithms, for example, our novel l2,1 filtering algorithm, to R-PCA. Although for the moment the formal proof of our l2,1 filtering algorithm is not yet available, experiments verify the advantages of our algorithm over other state-of-the-art methods based on the alternating direction method.

NO reduction by CO over CuO supported on CeO2-doped TiO2: the effect of the amount of a few CeO2.

This work is mainly focused on the investigation of the influence of the amount of a few CeO2 on the physicochemical and catalytic properties of CeO2-doped TiO2 catalysts for NO reduction by a CO model reaction. The obtained samples were characterized by means of XRD, N2-physisorption (BET), LRS, UV-vis DRS, XPS, (O2, CO, and NO)-TPD, H2-TPR, in situ FT-IR, and a NO + CO model reaction. These results indicate that a small quantity of CeO2 doping into the TiO2 support will cause an obvious change in the properties of the catalyst and the TC-60 : 1 (the TiO2/CeO2 molar ratio is 60 : 1) support exhibits the most extent of lattice expansion, which indicates that the band lengths of Ce-O-Ti are longer than other TC (the solid solution of TiO2 and CeO2) samples, probably contributing to larger structural distortion and disorder, more defects and oxygen vacancies. Copper oxide species supported on TC supports are much easier to be reduced than those supported on the pure TiO2 and CeO2 surface-modified TiO2 supports. Furthermore, the Cu/TC-60 : 1 catalyst shows the highest activity and selectivity due to more oxygen vacancies, higher mobility of surface and lattice oxygen at lower temperature (which contributes to the regeneration of oxygen vacancies, and the best reducing ability), the most content of Cu(+), and the strongest synergistic effect between Ti(3+), Ce(3+) and Cu(+). On the other hand, the CeO2 doping into TiO2 promotes the formation of a Cu(+)/Cu(0) redox cycle at high temperatures, which has a crucial effect on N2O reduction. Finally, in order to further understand the nature of the catalytic performances of these samples, taking the Cu/TC-60 : 1 catalyst as an example, a possible reaction mechanism is tentatively proposed.

An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm.

BACKGROUND: The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. METHODS: In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. RESULTS: Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. CONCLUSIONS: Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.

Correlated spatio-temporal data collection in wireless sensor networks based on low rank matrix approximation and optimized node sampling.

The emerging low rank matrix approximation (LRMA) method provides an energy efficient scheme for data collection in wireless sensor networks (WSNs) by randomly sampling a subset of sensor nodes for data sensing. However, the existing LRMA based methods generally underutilize the spatial or temporal correlation of the sensing data, resulting in uneven energy consumption and thus shortening the network lifetime. In this paper, we propose a correlated spatio-temporal data collection method for WSNs based on LRMA. In the proposed method, both the temporal consistence and the spatial correlation of the sensing data are simultaneously integrated under a new LRMA model. Moreover, the network energy consumption issue is considered in the node sampling procedure. We use Gini index to measure both the spatial distribution of the selected nodes and the evenness of the network energy status, then formulate and resolve an optimization problem to achieve optimized node sampling. The proposed method is evaluated on both the simulated and real wireless networks and compared with state-of-the-art methods. The experimental results show the proposed method efficiently reduces the energy consumption of network and prolongs the network lifetime with high data recovery accuracy and good stability.

Beyond low-pass filtering on large-scale graphs via Adaptive Filtering Graph Neural Networks.

Graph Neural Networks (GNNs) have emerged as a crucial deep learning framework for graph-structured data. However, existing GNNs suffer from the scalability limitation, which hinders their practical implementation in industrial settings. Many scalable GNNs have been proposed to address this limitation. However, they have been proven to act as low-pass graph filters, which discard the valuable middle- and high-frequency information. This paper proposes a novel graph neural network named Adaptive Filtering Graph Neural Networks (AFGNN), which can capture all frequency information on large-scale graphs. AFGNN consists of two stages. The first stage utilizes low-, middle-, and high-pass graph filters to extract comprehensive frequency information without introducing additional parameters. This computation is a one-time task and is pre-computed before training, ensuring its scalability. The second stage incorporates a node-level attention-based feature combination, enabling the generation of customized graph filters for each node, contrary to existing spectral GNNs that employ uniform graph filters for the entire graph. AFGNN is suitable for mini-batch training, and can enhance scalability and efficiently capture all frequency information from large-scale graphs. We evaluate AFGNN by comparing its ability to capture all frequency information with spectral GNNs, and its scalability with scalable GNNs. Experimental results illustrate that AFGNN surpasses both scalable GNNs and spectral GNNs, highlighting its superiority.

Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential of multi-modal documents incorporating images. To address this gap, we introduce an innovative approach for multi-modal long document classification based on the Hierarchical Prompt and Multi-modal Transformer (HPMT). The proposed HPMT method facilitates multi-modal interactions at both the section and sentence levels, enabling a comprehensive capture of hierarchical structural features and complex multi-modal associations of long documents. Specifically, a Multi-scale Multi-modal Transformer (MsMMT) is tailored to capture the multi-granularity correlations between sentences and images. This is achieved through the incorporation of multi-scale convolutional kernels on sentence features, enhancing the model's ability to discern intricate patterns. Furthermore, to facilitate cross-level information interaction and promote learning of specific features at different levels, we introduce a Hierarchical Prompt (HierPrompt) block. This block incorporates section-level prompts and sentence-level prompts, both derived from a global prompt via distinct projection networks. Extensive experiments are conducted on four challenging multi-modal long document datasets. The results conclusively demonstrate the superiority of our proposed method, showcasing its performance advantages over existing techniques.

Bridging the Cross-Modality Semantic Gap in Visual Question Answering.

The objective of visual question answering (VQA) is to adequately comprehend a question and identify relevant contents in an image that can provide an answer. Existing approaches in VQA often combine visual and question features directly to create a unified cross-modality representation for answer inference. However, this kind of approach fails to bridge the semantic gap between visual and text modalities, resulting in a lack of alignment in cross-modality semantics and the inability to match key visual content accurately. In this article, we propose a model called the caption bridge-based cross-modality alignment and contrastive learning model (CBAC) to address the issue. The CBAC model aims to reduce the semantic gap between different modalities. It consists of a caption-based cross-modality alignment module and a visual-caption (V-C) contrastive learning module. By utilizing an auxiliary caption that shares the same modality as the question and has closer semantic associations with the visual, we are able to effectively reduce the semantic gap by separately matching the caption with both the question and the visual to generate pre-alignment features for each, which are then used in the subsequent fusion process. We also leverage the fact that V-C pairs exhibit stronger semantic connections compared to question-visual (Q-V) pairs to employ a contrastive learning mechanism on visual and caption pairs to further enhance the semantic alignment capabilities of single-modality encoders. Extensive experiments conducted on three benchmark datasets demonstrate that the proposed model outperforms previous state-of-the-art VQA models. Additionally, ablation experiments confirm the effectiveness of each module in our model. Furthermore, we conduct a qualitative analysis by visualizing the attention matrices to assess the reasoning reliability of the proposed model.

MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion.

Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-dependent properties and the evolving nature of knowledge over time. TKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, which can often be mixed together. However, embedding TKGs into Euclidean space, as is typically done with TKG completion (TKGC) models, presents a challenge when dealing with high-dimensional nonlinear data and complex geometric structures. To address this issue, we propose a novel TKGC model called multicurvature adaptive embedding (MADE). MADE models TKGs in multicurvature spaces, including flat Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature), to handle multiple geometric structures. We assign different weights to different curvature spaces in a data-driven manner to strengthen the ideal curvature spaces for modeling and weaken the inappropriate ones. Additionally, we introduce the quadruplet distributor (QD) to assist the information interaction in each geometric space. Ultimately, we develop an innovative temporal regularization to enhance the smoothness of timestamp embeddings by strengthening the correlation of neighboring timestamps. Experimental results show that MADE outperforms the existing state-of-the-art TKGC models.

Domain Adaptation as Optimal Transport on Grassmann Manifolds.

Domain adaptation in the Euclidean space is a challenging task on which researchers recently have made great progress. However, in practice, there are rich data representations that are not Euclidean. For example, many high-dimensional data in computer vision are in general modeled by a low-dimensional manifold. This prompts the demand of exploring domain adaptation between non-Euclidean manifold spaces. This article is concerned with domain adaption over the classic Grassmann manifolds. An optimal transport-based domain adaptation model on Grassmann manifolds has been proposed. The model implements the adaption between datasets by minimizing the Wasserstein distances between the projected source data and the target data on Grassmann manifolds. Four regularization terms are introduced to keep task-related consistency in the adaptation process. Furthermore, to reduce the computational cost, a simplified model preserving the necessary adaption property and its efficient algorithm is proposed and tested. The experiments on several publicly available datasets prove the proposed model outperforms several relevant baseline domain adaptation methods.

Graph Decoupling Attention Markov Networks for Semisupervised Graph Node Classification.

Graph neural networks (GNNs) have been ubiquitous in graph node classification tasks. Most GNN methods update the node embedding iteratively by aggregating its neighbors' information. However, they often suffer from negative disturbances, due to edges connecting nodes with different labels. One approach to alleviate this negative disturbance is to use attention to learn the weights of aggregation, but current attention-based GNNs only consider feature similarity and suffer from the lack of supervision. In this article, we consider label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention. The hard attention is learned on labels for a refined graph structure with fewer interclass edges so that the aggregation's negative disturbance can be reduced. The soft attention aims to learn the aggregation weights based on features over the refined graph structure to enhance information gains during message passing. Particularly, we formulate our model under the expectation-maximization (EM) framework, and the learned attention is used to guide label propagation in the M-step and feature propagation in the E-step, respectively. Extensive experiments are performed on six well-known benchmark graph datasets to verify the effectiveness of the proposed method.

Hierarchical Graph Convolutional Networks for Structured Long Document Classification.

Long document classification (LDC) has been a focused interest in natural language processing (NLP) recently with the exponential increase of publications. Based on the pretrained language models, many LDC methods have been proposed and achieved considerable progression. However, most of the existing methods model long documents as sequences of text while omitting the document structure, thus limiting the capability of effectively representing long texts carrying structure information. To mitigate such limitation, we propose a novel hierarchical graph convolutional network (HGCN) for structured LDC in this article, in which a section graph network is proposed to model the macrostructure of a document and a word graph network with a decoupled graph convolutional block is designed to extract the fine-grained features of a document. In addition, an interaction strategy is proposed to integrate these two networks as a whole by propagating features between them. To verify the effectiveness of the proposed model, four structured long document datasets are constructed, and the extensive experiments conducted on these datasets and another unstructured dataset show that the proposed method outperforms the state-of-the-art related classification methods.

Logarithmic Schatten- p Norm Minimization for Tensorial Multi-View Subspace Clustering.

The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten- p norm ([Formula: see text]N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten- p norm minimization ([Formula: see text]NM)-based multi-view subspace clustering ([Formula: see text]NM-MSC) model is proposed. Specially, the proposed [Formula: see text]NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block [Formula: see text]NM-MSC model where the challenging [Formula: see text]NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the [Formula: see text]NM-MSC model.

A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery.

Structured clustering networks, which alleviate the oversmoothing issue by delivering hidden features from autoencoder (AE) to graph convolutional networks (GCNs), involve two shortcomings for the clustering task. For one thing, they used vanilla structure to learn clustering representations without considering feature and structure corruption; for another thing, they exhibit network degradation and vanishing gradient issues after stacking multilayer GCNs. In this article, we propose a clustering method called dual-masked deep structural clustering network (DMDSC) with adaptive bidirectional information delivery (ABID). Specifically, DMDSC enables generative self-supervised learning to mine deeper interstructure and interfeature correlations by simultaneously reconstructing corrupted structures and features. Furthermore, DMDSC develops an ABID module to establish an information transfer channel between each pairwise layer of AE and GCNs to alleviate the oversmoothing and vanishing gradient problems. Numerous experiments on six benchmark datasets have shown that the proposed DMDSC outperforms the most advanced deep clustering algorithms.

Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision.

Graph convolutional networks (GCNs) have become a popular tool for learning unstructured graph data due to their powerful learning ability. Many researchers have been interested in fusing topological structures and node features to extract the correlation information for classification tasks. However, it is inadequate to integrate the embedding from topology and feature spaces to gain the most correlated information. At the same time, most GCN-based methods assume that the topology graph or feature graph is compatible with the properties of GCNs, but this is usually not satisfied since meaningless, missing, or even unreal edges are very common in actual graphs. To obtain a more robust and accurate graph structure, we intend to construct an adaptive graph with topology and feature graphs. We propose Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision (MFGCN), which learn a connected embedding by fusing the multi-graphs and node features. We can obtain the final node embedding for semi-supervised node classification by propagating node features over multi-graphs. Furthermore, to alleviate the problem of labels missing in semi-supervised classification, a pseudo-label generation mechanism is proposed to generate more reliable pseudo-labels based on the similarity of node features. Extensive experiments on six benchmark datasets demonstrate the superiority of MFGCN over state-of-the-art classification methods.

QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion.

Temporal knowledge graph completion (TKGC) is an extension of the traditional static knowledge graph completion (SKGC) by introducing the timestamp. The existing TKGC methods generally translate the original quadruplet to the form of the triplet by integrating the timestamp into the entity/relation, and then use SKGC methods to infer the missing item. However, such an integrating operation largely limits the expressive ability of temporal information and ignores the semantic loss problem due to the fact that entities, relations, and timestamps are located in different spaces. In this article, we propose a novel TKGC method called the quadruplet distributor network (QDN), which independently models the embeddings of entities, relations, and timestamps in their specific spaces to fully capture the semantics and builds the QD to facilitate the information aggregation and distribution among them. Furthermore, the interaction among entities, relations, and timestamps is integrated using a novel quadruplet-specific decoder, which stretches the third-order tensor to the fourth-order to satisfy the TKGC criterion. Equally important, we design a novel temporal regularization that imposes a smoothness constraint on temporal embeddings. Experimental results show that the proposed method outperforms the existing state-of-the-art TKGC methods. The source codes of this article are available at https://github.com/QDN for Temporal Knowledge Graph Completion.git.