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.

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.

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.

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.

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.

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.

Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision.

General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL.

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.

A novel formation pathway of N-(carboxyethyl)lysine from lactic acid during high temperature exposure in wheat sourdough bread and chemical model.

N-(carboxymethyl)lysine (CML) and N-(carboxyethyl)lysine (CEL) have been the most extensively studied advanced glycation end-products (AGEs) in foods. Their formation mechanism, especially the latter, has not been clearly delineated in fermented food. In this work, the relative contents of CEL and CML were evaluated in a sourdough-bread and a silica solid chemical model.

Indole-3-propionic acid alleviates ischemic brain injury in a mouse middle cerebral artery occlusion model.

Increasing evidence highlights the importance of gut microbiota and its metabolites as an environmental factor affecting ischemic stroke. However, the role of microbial indole metabolites in ischemic stroke remains largely unknown. Here, we evaluated the effects and the underlying mechanism of indole-3-propionic acid (IPA) in a mouse model of acute middle cerebral artery occlusion (MCAO) and the mechanisms underlying these effects.

Characterization and immunomodulatory effect of an alkali-extracted galactomannan from Morchella esculenta.

In our continuous exploration for bioactive polysaccharides, a novel polysaccharide FMP-2 was isolated and purified from the fruiting bodies of Morchella esculenta by alkali-assisted extraction. FMP-2 had an average molecular weight of 1.09 × 10 Da and contained mannose, glucuronic acid, glucose, galactose, and arabinose in a molar ratio of 4.

META-DDIE: predicting drug-drug interaction events with few-shot learning.

Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research, and a number of computational methods have been developed to predict whether two drugs interact or not. Recently, more attention has been paid to events caused by the DDIs, which is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. However, some rare events may only have few examples, hindering them from being precisely predicted.

Sparsity-control ternary weight networks.

Deep neural networks (DNNs) have been widely and successfully applied to various applications, but they require large amounts of memory and computational power. This severely restricts their deployment on resource-limited devices. To address this issue, many efforts have been made on training low-bit weight DNNs.

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.

Inhibition Mechanism of L-Cysteine on Maillard Reaction by Trapping 5-Hydroxymethylfurfural.

The Maillard reaction (MR) can affect the color, flavor, organoleptic properties, and nutritional value of food. Sometimes, MR is undesirable due to lowering the nutrient utilization, producing harmful neo-formed compounds, etc. In this case, it is necessary to control MR.

Efficient Relational Sentence Ordering Network.

In this paper, we propose a novel deep Efficient Relational Sentence Ordering Network (referred to as ERSON) by leveraging pre-trained language model in both encoder and decoder architectures to strengthen the coherence modeling of the entire model. Specifically, we first introduce a divide-and-fuse BERT (referred to as DF-BERT), a new refactor of BERT network, where lower layers in the improved model encode each sentence in the paragraph independently, which are shared by different sentence pairs, and the higher layers learn the cross-attention between sentence pairs jointly. It enables us to capture the semantic concepts and contextual information between the sentences of the paragraph, while significantly reducing the runtime and memory consumption without sacrificing the model performance.

Semantic-Guided Class-Imbalance Learning Model for Zero-Shot Image Classification.

In this article, we focus on the task of zero-shot image classification (ZSIC) that equips a learning system with the ability to recognize visual images from unseen classes. In contrast to the traditional image classification, ZSIC more easily suffers from the class-imbalance issue since it is more concerned with the class-level knowledge transferring capability. In the real world, the sample numbers of different categories generally follow a long-tailed distribution, and the discriminative information in the sample-scarce seen classes is hard to transfer to the related unseen classes in the traditional batch-based training manner, which degrades the overall generalization ability a lot.

Development of an Isotope Dilution UHPLC-QqQ-MS/MS-Based Method for Simultaneous Determination of Typical Advanced Glycation End Products and Acrylamide in Baked and Fried Foods.

In this work, a stable isotope dilution ultrahigh-performance liquid chromatography triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS) method was developed and validated for simultaneous determination of -(carboxymethyl)lysine (CML), -(carboxyethyl)lysine (CEL), and acrylamide (AA) in baked and fried foods. Ground food samples were extracted with acetone followed by two parallel assays. In assay A, a cleanup procedure based on dispersive solid-phase extraction was conducted for AA, free CML, and CEL analysis using the supernatant.

Human umbilical cord mesenchymal stem cell-derived exosomal miR-146a-5p reduces microglial-mediated neuroinflammation via suppression of the IRAK1/TRAF6 signaling pathway after ischemic stroke.

To investigate the therapeutic mechanism of action of transplanted stem cells and develop exosome-based nanotherapeutics for ischemic stroke, we assessed the effect of exosomes (Exos) produced by human umbilical cord mesenchymal stem cells (hUMSCs) on microglia-mediated neuroinflammation after ischemic stroke. Our results found that injected hUMSC-Exos were able to access the site of ischemic damage and could be internalized by cells both and . , treatment with hUMSC-Exos attenuated microglia-mediated inflammation after oxygen-glucose deprivation (OGD).

Multitask Non-Autoregressive Model for Human Motion Prediction.

Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation problem.

Local-Global Memory Neural Network for Medication Prediction.

Electronic medical records (EMRs) play an important role in medical data mining and sequential data learning. In this article, we propose to use a sequential neural network with dynamic content-based memories to predict future medications, given EMRs. The local-global memory neural network contains two layers of memories: the local memory and the global memory.

Attention-Aware Multi-Task Convolutional Neural Networks.

Multi-task deep learning methods learn multiple tasks simultaneously and share representations amongst them, so information from related tasks improves learning within one task. The generalization capabilities of the produced models are substantially enhanced. Typical multi-task deep learning models usually share representations of different tasks in lower layers of the network, and separate representations of different tasks in higher layers.

Efficient Person Search via Expert-Guided Knowledge Distillation.

The person search problem aims to find the target person in the scene images, which presents high demands for both effectiveness and efficiency. In this paper, we present a unified person search framework which jointly handles the two demands for real-world applications. We explore the technique of knowledge distillation (KD), which allows the student network to share capabilities of the deep expert networks with much fewer parameters and less computing time.