Social Network Forensics Analysis Model Based on Network Representation Learning.

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks.

Clustering on heterogeneous IoT information network based on meta path.

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

Small-molecule Molephantin induces apoptosis and mitophagy flux blockage through ROS production in glioblastoma.

Glioblastoma (GBM), one of the most malignant brain tumors in the world, has limited treatment options and a dismal survival rate. Effective and safe disease-modifying drugs for glioblastoma are urgently needed. Here, we identified a small molecule, Molephantin (EM-5), effectively penetrated the blood-brain barrier (BBB) and demonstrated notable antitumor effects against GBM with good safety profiles both in vitro and in vivo. Mechanistically, EM-5 not only inhibits the proliferation and invasion of GBM cells but also induces cell apoptosis through the reactive oxygen species (ROS)-mediated PI3K/Akt/mTOR pathway. Furthermore, EM-5 causes mitochondrial dysfunction and blocks mitophagy flux by impeding the fusion of mitophagosomes with lysosomes. It is noteworthy that EM-5 does not interfere with the initiation of autophagosome formation or lysosomal function. Additionally, the mitophagy flux blockage caused by EM-5 was driven by the accumulation of intracellular ROS. In vivo, EM-5 exhibited significant efficacy in suppressing tumor growth in a xenograft model. Collectively, our findings not only identified EM-5 as a promising, effective, and safe lead compound for treating GBM but also uncovered its underlying mechanisms from the perspective of apoptosis and mitophagy.

Tolerant Self-Distillation for image classification.

Deep neural networks tend to suffer from the overfitting issue when the training data are not enough. In this paper, we introduce two metrics from the intra-class distribution of correct-predicted and incorrect-predicted samples to provide a new perspective on the overfitting issue. Based on it, we propose a knowledge distillation approach without pretraining a teacher model in advance named Tolerant Self-Distillation (TSD) for alleviating the overfitting issue. It introduces an online updating memory and selectively stores the class predictions of the samples from the past iterations, making it possible to distill knowledge across the iterations. Specifically, the class predictions stored in the memory bank serve as the soft labels for supervising the samples from the same class for the current iteration in a reverse way, i.e. the correct-predicted samples are supervised with the incorrect predictions while the incorrect-predicted samples are supervised with the correct predictions. Consequently, the premature convergence issue caused by the over-confident samples would be mitigated, which helps the model to converge to a better local optimum. Extensive experimental results on several image classification benchmarks, including small-scale, large-scale, and fine-grained datasets, demonstrate the superiority of the proposed TSD.

Balancing Feature Alignment and Uniformity for Few-Shot Classification.

In Few-Shot Learning (FSL), the objective is to correctly recognize new samples from novel classes with only a few available samples per class. Existing methods in FSL primarily focus on learning transferable knowledge from base classes by maximizing the information between feature representations and their corresponding labels. However, this approach may suffer from the "supervision collapse" issue, which arises due to a bias towards the base classes. In this paper, we propose a solution to address this issue by preserving the intrinsic structure of the data and enabling the learning of a generalized model for the novel classes. Following the InfoMax principle, our approach maximizes two types of mutual information (MI): between the samples and their feature representations, and between the feature representations and their class labels. This allows us to strike a balance between discrimination (capturing class-specific information) and generalization (capturing common characteristics across different classes) in the feature representations. To achieve this, we adopt a unified framework that perturbs the feature embedding space using two low-bias estimators. The first estimator maximizes the MI between a pair of intra-class samples, while the second estimator maximizes the MI between a sample and its augmented views. This framework effectively combines knowledge distillation between class-wise pairs and enlarges the diversity in feature representations. By conducting extensive experiments on popular FSL benchmarks, our proposed approach achieves comparable performances with state-of-the-art competitors. For example, we achieved an accuracy of 69.53% on the miniImageNet dataset and 77.06% on the CIFAR-FS dataset for the 5-way 1-shot task.

MCPIP-1 knockdown enhances endothelial colony-forming cell angiogenesis via the TFRC/AKT/mTOR signaling pathway in the ischemic penumbra of MCAO mice.

Cerebral ischemia is a serious disease characterized by brain tissue ischemia and hypoxic necrosis caused by the blockage of blood vessels within the central nervous system. Although stem cell therapy is a promising approach for treating ischemic stroke, the inflammatory, oxidative, and hypoxic environment generated by cerebral ischemia greatly reduces the survival and therapeutic effects of transplanted stem cells. Endothelial colony-forming cells (ECFCs) are a class of precursor cells with strong proliferative potential that can migrate and differentiate directly into mature vascular endothelial cells. Consequently, ECFCs can exert significant therapeutic and reparative effects in diseases associated with vascular injury. Monocyte chemoattractant protein-induced protein 1 (MCPIP-1) exerts multiple biological effects; however, no studies have yet reported its role in the angiogenic function of ECFCs. In this study, we performed Proteome Profiler™ Human Angiogenesis Antibody arrays and tandem mass tag protein profiling to investigate the effect of MCPIP-1 on ECFCs. We demonstrated that MCPIP-1 knockdown enhanced the proliferation, migration, and in vivo and in vitro angiogenic capacity of ECFCs by upregulating the transferrin receptor-activated AKT/m-TOR signaling pathway to promote cellular trophic factor secretion. Furthermore, we found that the lateral ventricular transplantation of ECFCs with lentiviral MCPIP-1 knockdown into mice with middle cerebral artery occlusion increased serum vacular endothelial growth factor(VEGF), angiopoietin-1, and HIF-1a levels, enhanced neovascularization and neurogenesis in the ischemic penumbra, reduced the size of cerebral infarcts, and promoted neurological recovery. Together, these findings suggest new avenues for enhancing the therapeutic efficacy of ECFCs.

HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction.

Accurate prediction of molecular properties is an important topic in drug discovery. Recent works have developed various representation schemes for molecular structures to capture different chemical information in molecules. The atom and motif can be viewed as hierarchical molecular structures that are widely used for learning molecular representations to predict chemical properties. Previous works have attempted to exploit both atom and motif to address the problem of information loss in single representation learning for various tasks. To further fuse such hierarchical information, the correspondence between learned chemical features from different molecular structures should be considered. Herein, we propose a novel framework for molecular property prediction, called hierarchical molecular graph neural networks (HimGNN). HimGNN learns hierarchical topology representations by applying graph neural networks on atom- and motif-based graphs. In order to boost the representational power of the motif feature, we design a Transformer-based local augmentation module to enrich motif features by introducing heterogeneous atom information in motif representation learning. Besides, we focus on the molecular hierarchical relationship and propose a simple yet effective rescaling module, called contextual self-rescaling, that adaptively recalibrates molecular representations by explicitly modelling interdependencies between atom and motif features. Extensive computational experiments demonstrate that HimGNN can achieve promising performances over state-of-the-art baselines on both classification and regression tasks in molecular property prediction.

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

Knowledge Distillation Classifier Generation Network for Zero-Shot Learning.

In this article, we present a conceptually simple but effective framework called knowledge distillation classifier generation network (KDCGN) for zero-shot learning (ZSL), where the learning agent requires recognizing unseen classes that have no visual data for training. Different from the existing generative approaches that synthesize visual features for unseen classifiers' learning, the proposed framework directly generates classifiers for unseen classes conditioned on the corresponding class-level semantics. To ensure the generated classifiers to be discriminative to the visual features, we borrow the knowledge distillation idea to both supervise the classifier generation and distill the knowledge with, respectively, the visual classifiers and soft targets trained from a traditional classification network. Under this framework, we develop two, respectively, strategies, i.e., class augmentation and semantics guidance, to facilitate the supervision process from the perspectives of improving visual classifiers. Specifically, the class augmentation strategy incorporates some additional categories to train the visual classifiers, which regularizes the visual classifier weights to be compact, under supervision of which the generated classifiers will be more discriminative. The semantics-guidance strategy encodes the class semantics into the visual classifiers, which would facilitate the supervision process by minimizing the differences between the generated and the real-visual classifiers. To evaluate the effectiveness of the proposed framework, we have conducted extensive experiments on five datasets in image classification, i.e., AwA1, AwA2, CUB, FLO, and APY. Experimental results show that the proposed approach performs best in the traditional ZSL task and achieves a significant performance improvement on four out of the five datasets in the generalized ZSL task.

Mesenchymal stem cells protect against TBI-induced pyroptosis in vivo and in vitro through TSG-6.

BACKGROUND: Pyroptosis, especially microglial pyroptosis, may play an important role in central nervous system pathologies, including traumatic brain injury (TBI). Transplantation of mesenchymal stem cells (MSCs), such as human umbilical cord MSCs (hUMSCs), has been a focus of brain injury treatment. Recently, MSCs have been found to play a role in many diseases by regulating the pyroptosis pathway. However, the effect of MSC transplantation on pyroptosis following TBI remains unknown. Tumor necrosis factor α stimulated gene 6/protein (TSG-6), a potent anti-inflammatory factor expressed in many cell types including MSCs, plays an anti-inflammatory role in many diseases; however, the effect of TSG-6 secreted by MSCs on pyroptosis remains unclear. METHODS: Mice were subjected to controlled cortical impact injury in vivo. To assess the time course of pyroptosis after TBI, brains of TBI mice were collected at different time points. To study the effect of TSG-6 secreted by hUMSCs in regulating pyroptosis, normal hUMSCs, sh-TSG-6 hUMSCs, or different concentrations of rmTSG-6 were injected intracerebroventricularly into mice 4 h after TBI. Neurological deficits, double immunofluorescence staining, presence of inflammatory factors, cell apoptosis, and pyroptosis were assessed. In vitro, we investigated the anti-pyroptosis effects of hUMSCs and TSG-6 in a lipopolysaccharide/ATP-induced BV2 microglial pyroptosis model. RESULTS: In TBI mice, the co-localization of Iba-1 (marking microglia/macrophages) with NLRP3/Caspase-1 p20/GSDMD was distinctly observed at 48 h. In vivo, hUMSC transplantation or treatment with rmTSG-6 in TBI mice significantly improved neurological deficits, reduced inflammatory cytokine expression, and inhibited both NLRP3/Caspase-1 p20/GSDMD expression and microglial pyroptosis in the cerebral cortices of TBI mice. However, the therapeutic effect of hUMSCs on TBI mice was reduced by the inhibition of TSG-6 expression in hUMSCs. In vitro, lipopolysaccharide/ATP-induced BV2 microglial pyroptosis was inhibited by co-culture with hUMSCs or with rmTSG-6. However, the inhibitory effect of hUMSCs on BV2 microglial pyroptosis was significantly reduced by TSG-6-shRNA transfection. CONCLUSION: In TBI mice, microglial pyroptosis was observed. Both in vivo and in vitro, hUMSCs inhibited pyroptosis, particularly microglial pyroptosis, by regulating the NLRP3/Caspase-1/GSDMD signaling pathway via TSG-6. Video Abstract.