Overcoming drug resistance through extracellular vesicle-based drug delivery system in cancer treatment.

Drug resistance is a major challenge in cancer therapy that often leads to treatment failure and disease relapse. Despite advancements in chemotherapeutic agents and targeted therapies, cancers often develop drug resistance, making these treatments ineffective. Extracellular vesicles (EVs) have gained attention for their potential applications in drug delivery because of their natural origin, biocompatibility, and ability to cross biological barriers.

Unified Knowledge-Guided Molecular Graph Encoder with multimodal fusion and multi-task learning.

The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.

Prevalence and drug resistance analysis of hepatitis C virus genotypes in Heilongjiang, China.

Hepatitis C still poses a threat to public safety, and there are few reports of hepatitis C virus (HCV) in Heilongjiang Province. Therefore, we aimed to study the epidemiology and resistance-associated substitutions (RASs) of HCV in Heilongjiang and explore the efficacy of treatment. 7019 specimens from Heilongjiang Province were subjected to the genotype identification.

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation.

The Segment Anything Model (SAM), a profound vision foundation model pretrained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstream applications. This paper introduces Hi-SAM, a unified model leveraging SAM for hierarchical text segmentation. Hi-SAM excels in segmentation across four hierarchies, including pixel-level text, word, text-line, and paragraph, while realizing layout analysis as well.

Rectangling and enhancing underwater stitched image via content-aware warping and perception balancing.

Single underwater images often face limitations in field-of-view and visual perception due to scattering and absorption. Numerous image stitching techniques have attempted to provide a wider viewing range, but the resulting stitched images may exhibit unsightly irregular boundaries. Unlike natural landscapes, the absence of reliable high-fidelity references in water complicates the replicability of these deep learning-based methods, leading to unpredictable distortions in cross-domain applications.

Designing a Highly Crystalline Polymer Donor with Alkylsilyl and Fluorine Substitution to Achieve Efficient Ternary Organic Solar Cells.

Adopting a ternary strategy is an effective approach to enhance the power conversion efficiency (PCE) in organic solar cells (OSCs). Previous research on highly efficient ternary systems has predominantly focused on those based on highly crystalline dual small molecule acceptors. However, limited attention has been given to ternary systems utilizing dual polymer donors.

Graph explicit pooling for graph-level representation learning.

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discarding the remaining to construct coarsened graph representations. However, this paper highlights two key issues with these methods: (1) The process of selecting nodes to discard frequently employs additional Graph Convolutional Networks or Multilayer Perceptrons, lacking a thorough evaluation of each node's impact on the final graph representation and subsequent prediction tasks.

Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning.

Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit. To reduce the requirement of labels, a semi-supervised meta-training (SSMT) setting has been proposed for FSL, which includes only a few labeled samples and numbers of unlabeled samples in base classes. However, existing methods under this setting require class-aware sample selection from the unlabeled set, which violates the assumption of unlabeled set.

Identification of novel CDH23 variants linked to hearing loss in a Chinese family: A case report.

Rationale: Deafness is associated with both environmental and genetic factors, with hereditary deafness often caused by mutations in deafness-related genes. Identifying and analyzing deafness-related genes will aid in early diagnosis and pave the way for treating inherited deafness through gene therapy in the future.

Patient Concerns: A 15-month-old girl underwent audiological examination at the outpatient clinic of the hospital due to hearing loss and her brother was diagnosed with profound bilateral sensorineural hearing loss at the age of 3.

UVaT: Uncertainty Incorporated View-Aware Transformer for Robust Multi-View Classification.

Existing multi-view classification algorithms usually assume that all examples have observations on all views, and the data in different views are clean. However, in real-world applications, we are often provided with data that have missing representations or contain noise on some views (i.e.

Fast Projected Fuzzy Clustering With Anchor Guidance for Multimodal Remote Sensing Imagery.

Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are absent, the recognition is challenging for the large data size, complex land-cover distribution and large modality spectrum variation.

MixIR: Mixing Input and Representations for Contrastive Learning.

Recently, contrastive learning has shown significant progress in learning visual representations from unlabeled data. The core idea is training the backbone to be invariant to different augmentations of an instance. While most methods only maximize the feature similarity between two augmented data, we further generate more challenging training samples and force the model to keep predicting aggregated representation on these hard samples.

Identifying factors related to pedestrian and cyclist crashes in ACT, Australia with an extended crash dataset.

As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels.

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.

Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.

Attention-based Sparse and Collaborative Spectral Abundance Learning for Hyperspectral Subpixel Target Detection.

The subpixel target detection in hyperspectral image processing persists as a formidable challenge. In this paper, we present a novel subpixel target detector termed attention-based sparse and collaborative spectral abundance learning for subpixel target detection in hyperspectral images. To help suppress background during subpixel target detection, the proposed method presents a pixel attention-based background sample selection method for background dictionary construction.

An Antibacterial, Conductive Nanocomposite Hydrogel Coupled with Electrical Stimulation for Accelerated Wound Healing.

Background: Electrical stimulation (ES) can effectively promote skin wound healing; however, single-electrode-based ES strategies are difficult to cover the entire wound area, and the effectiveness of ES is often limited by the inconsistent mechanical properties of the electrode and wound tissue. The above factors may lead to ES treatment is not ideal.

Methods: A multifunctional conductive hydrogel dressing containing methacrylated gelatin (GelMA), TiC and collagen binding antimicrobial peptides (V-Os) was developed to improve wound management.

Federated Learning With Only Positive Labels by Exploring Label Correlations.

Federated learning (FL) aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this article, we study the multilabel classification (MLC) problem under the FL setting, where trivial solution and extremely poor performance may be obtained, especially when only positive data with respect to a single class label is provided for each client. This issue can be addressed by adding a specially designed regularizer on the server side.

Evaluation of TikTok videos on acute pancreatitis: content quality and reliability analysis.

Background: Acute pancreatitis (AP) is a common acute digestive system disorder, with patients often turning to TikTok for AP-related information. However, the platform's video quality on AP has not been thoroughly investigated.

Objective: The main purpose of this study is to evaluate the quality of videos about AP on TikTok, and the secondary purpose is to study the related factors of video quality.

A Modular Neural Motion Retargeting System Decoupling Skeleton and Shape Perception.

Motion mapping between characters with different structures but corresponding to homeomorphic graphs, meanwhile preserving motion semantics and perceiving shape geometries, poses significant challenges in skinned motion retargeting. We propose M-R ET, a modular neural motion retargeting system to comprehensively address these challenges. The key insight driving M-R ET is its capacity to learn residual motion modifications within a canonical skeleton space.

Multi-tailed vision transformer for efficient inference.

Recently, Vision Transformer (ViT) has achieved promising performance in image recognition and gradually serves as a powerful backbone in various vision tasks. To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length. Then, the following self-attention layers construct the global relationship between tokens to produce useful representation for the downstream tasks.

Exploring sparsity in graph transformers.

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility of sparsifying GTs, a significant yet under-explored topic.

Label-Aware Calibration and Relation-Preserving in Visual Intention Understanding.

Visual intention understanding is a challenging task that explores the hidden intention behind the images of publishers in social media. Visual intention represents implicit semantics, whose ambiguous definition inevitably leads to label shifting and label blemish. The former indicates that the same image delivers intention discrepancies under different data augmentations, while the latter represents that the label of intention data is susceptible to errors or omissions during the annotation process.