Single-cell RNA sequencing of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases.

Alternative splicing contributes to complex traits, but whether this differs in trait-relevant cell types across diverse genetic ancestries is unclear. Here we describe cell-type-specific, sex-biased and ancestry-biased alternative splicing in ~1 M peripheral blood mononuclear cells from 474 healthy donors from the Asian Immune Diversity Atlas. We identify widespread sex-biased and ancestry-biased differential splicing, most of which is cell-type-specific.

Enzymatic nanomotors with chemotaxis for product-based cancer therapy.

The development of an intelligent nanomotor system holds great promise for enhancing the efficiency and effectiveness of antitumor therapy. Leveraging the overexpressed substances in the tumor microenvironment as propellants and chemotactic factors for enzyme-powered nanomotors represents a versatile and compelling approach. Herein, a plasma amine oxidase (PAO)-based chemotactic nanomotor system has been successfully developed, with the ability to enzymatically produce toxic acrolein and HO from the upregulated polyamines (PAs) in the tumor microenvironment for active tumor therapy.

NIR-Actuated Ferroptosis Nanomotor for Enhanced Tumor Penetration and Therapy.

Ferroptosis nano-inducers have drawn considerable attention in the treatment of malignant tumors. However, low intratumoral hydrogen peroxide level and complex biological barriers hinder the ability of nanomedicines to generate sufficient reactive oxygen species (ROS) and achieve tumor penetration. Here a near-infrared (NIR)-driven ROS self-supplying nanomotor is successfully designed for synergistic tumor chemodynamic therapy (CDT) and photothermal therapy (PTT).

Frameless Graph Knowledge Distillation.

Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy. Recently, many attempts have been made by applying the KD mechanism to graph representation learning models such as graph neural networks (GNNs) to accelerate the model's inference speed via student models. However, many existing KD-based GNNs utilize multilayer perceptron (MLP) as a universal approximator in the student model to imitate the teacher model's process without considering the graph knowledge from the teacher model.

A novel approach for assessing fairness in deployed machine learning algorithms.

Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the current body of literature lacks a statistically validated approach that can evaluate the fairness of a deployed ML algorithm against a dataset.

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.

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.

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.

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.

Micromotor-Enabled Active Hydrogen and Tobramycin Delivery for Synergistic Sepsis Therapy.

Sepsis is a highly heterogeneous syndrome normally characterized by bacterial infection and dysregulated systemic inflammatory response that leads to multiple organ failure and death. Single anti-inflammation or anti-infection treatment exhibits limited survival benefit for severe cases. Here a biodegradable tobramycin-loaded magnesium micromotor (Mg-Tob motor) is successfully developed as a potential hydrogen generator and active antibiotic deliverer for synergistic therapy of sepsis.

Bladder microenvironment actuated proteomotors with ammonia amplification for enhanced cancer treatment.

Enzyme-driven micro/nanomotors consuming chemical fuels have attracted lots of attention for biomedical applications. However, motor systems composed by organism-derived organics that maximize the therapeutic efficacy of enzymatic products remain challenging. Herein, swimming proteomotors based on biocompatible urease and human serum albumin are constructed for enhanced antitumor therapy active motion and ammonia amplification.

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.

Cancer Cell Membrane-Coated Nanoparticle Co-loaded with Photosensitizer and Toll-like Receptor 7 Agonist for the Enhancement of Combined Tumor Immunotherapy.

Tumor immunotherapy has shown considerable therapeutic potential in the past few years, but the clinical response rate of immunotherapy is less than 20%. Encountering the high heterogeneity of tumors, it will be a general trend to apply combined therapy for cancer treatment. Photodynamic therapy (PDT) transiently kills tumor cells by producing reactive oxygen species (ROS), while residual tumor cells are prone to metastasis, leading to tumor recurrence.

Hyperthermia-triggered biomimetic bubble nanomachines.

Nanoparticle-based drug delivery systems have gained much attention in the treatment of various malignant tumors during the past decades. However, limited tumor penetration of nanodrugs remains a significant hurdle for effective tumor therapy due to the existing biological barriers of tumoral microenvironment. Inspired by bubble machines, here we report the successful fabrication of biomimetic nanodevices capable of in-situ secreting cell-membrane-derived nanovesicles with smaller sizes under near infrared (NIR) laser irradiation for synergistic photothermal/photodynamic therapy.

Self-Propelled Proteomotors with Active Cell-Free mtDNA Clearance for Enhanced Therapy of Sepsis-Associated Acute Lung Injury.

Acute lung injury (ALI) is a frequent and serious complication of sepsis with limited therapeutic options. Gaining insights into the inflammatory dysregulation that causes sepsis-associated ALI can help develop new therapeutic strategies. Herein, the crucial role of cell-free mitochondrial DNA (cf-mtDNA) in the regulation of alveolar macrophage activation during sepsis-associated ALI is identified.

Graph structure learning layer and its graph convolution clustering application.

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph.

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).

Nanorobot-Mediated Synchronized Neuron Activation.

Interactions between active materials lead to collective behavior and even intelligence beyond the capability of individuals. Such behaviors are prevalent in nature and can be observed in animal colonies, providing these species with diverse capacities for communication and cooperation. In artificial systems, however, collective intelligence systems interacting with biological entities remains unexplored.

Magnetically Actuated Biodegradable Nanorobots for Active Immunotherapy.

An efficient and cost-effective therapeutic vaccine is highly desirable for the prevention and treatment of cancer, which helps to strengthen the immune system and activate the T cell immune response. However, initiating such an adaptive immune response efficiently remains challenging, especially the deficient antigen presentation by dendritic cells (DCs) in the immunosuppressive tumor microenvironment. Herein, an efficient and dynamic antigen delivery system based on the magnetically actuated OVA-CaCO -SPIO robots (OCS-robots) is rationally designed for active immunotherapy.

Mobile mechanical signal generator for macrophage polarization.

The importance of mechanical signals in regulating the fate of macrophages is gaining increased attention recently. However, the recently used mechanical signals normally rely on the physical characteristics of matrix with non-specificity and instability or mechanical loading devices with uncontrollability and complexity. Herein, we demonstrate the successful fabrication of self-assembled microrobots (SMRs) based on magnetic nanoparticles as local mechanical signal generators for precise macrophage polarization.

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.

Exploiting Neighbor Effect: Conv-Agnostic GNN Framework for Graphs With Heterophily.

Due to the homophily assumption in graph convolution networks (GCNs), a common consensus in the graph node classification task is that graph neural networks (GNNs) perform well on homophilic graphs but may fail on heterophilic graphs with many interclass edges. However, the previous interclass edges' perspective and related homo-ratio metrics cannot well explain the GNNs' performance under some heterophilic datasets, which implies that not all the interclass edges are harmful to GNNs. In this work, we propose a new metric based on the von Neumann entropy to reexamine the heterophily problem of GNNs and investigate the feature aggregation of interclass edges from an entire neighbor identifiable perspective.