In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions. Inspired by ...the process through which the human brain handles emotions and the theory of cross-modal plasticity, we propose UMBEnet, a brain-like unified modal affective processing network. The primary design of UMBEnet includes a Dual-Stream (DS) structure that fuses inherent prompts with a Prompt Pool and a Sparse Feature Fusion (SFF) module. The design of the Prompt Pool is aimed at integrating information from different modalities, while inherent prompts are intended to enhance the system's predictive guidance capabilities and effectively manage knowledge related to emotion classification. Moreover, considering the sparsity of effective information across different modalities, the SSF module aims to make full use of all available sensory data through the sparse integration of modality fusion prompts and inherent prompts, maintaining high adaptability and sensitivity to complex emotional states. Extensive experiments on the largest benchmark datasets in the Dynamic Facial Expression Recognition (DFER) field, including DFEW, FERV39k, and MAFW, have proven that UMBEnet consistently outperforms the current state-of-the-art methods. Notably, in scenarios of Modality Missingness and multimodal contexts, UMBEnet significantly surpasses the leading current methods, demonstrating outstanding performance and adaptability in tasks that involve complex emotional understanding with rich multimodal information.
The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, ...underpinned by training on voluminous datasets. Yet, the DFER datasets encompass a substantial volume of noise data. Noise arises from low-quality captures that defy logical labeling, and instances that suffer from mislabeling due to annotation bias, engendering two principal types of uncertainty: the uncertainty regarding data usability and the uncertainty concerning label reliability. Addressing the two types of uncertainty, we have meticulously crafted a two-stage framework aiming at \textbf{S}eeking \textbf{C}ertain data \textbf{I}n extensive \textbf{U}ncertain data (SCIU). This initiative aims to purge the DFER datasets of these uncertainties, thereby ensuring that only clean, verified data is employed in training processes. To mitigate the issue of low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which assesses sample weights and prunes those deemed unusable due to their low weight. For samples with incorrect annotations, the Fine-Grained Correction (FGC) stage evaluates prediction stability to rectify mislabeled data. Moreover, SCIU is conceived as a universally compatible, plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methodologies. Rigorous experiments across prevalent DFER datasets and against numerous benchmark methods substantiates SCIU's capacity to markedly elevate performance metrics.
To study the effect of platelet lysate (PL) on chondrogenic differentiation of human umbilical cord derived mesenchymal stem cells (hUCMSCs) in vitro.
Umbilical cords were voluntarily donated by ...healthy mothers. The hUCMSCs were isolated by collagenase digestion and cultured in vitro. The surface markers of the cells were detected by flow cytometer. According to different components of inductive medium, the cultured hUCMSCs were divided into 3 groups: group A H-DMEM medium, 10% fetal bovine serum (FBS), and 10%PL; group B H-DMEM medium, 10%FBS, 10 ng/mL transforming growth factor beta1 (TGF-beta1), 1 x 10(-7) mol/L dexamethasone, 50 microg/mL Vitamin C, and 1% insulin-transferrin-selenium (ITS); and group C (H-DMEM medium, 10%FBS, 10 ng/mL TGF-beta1, 1 x 10(-7) mol/L dexamethasone, 50 microg/mL vitamin C, 1%ITS, and 10%PL). The hUCMSCs were induced in the mediums for 2 weeks. Toluidine blue staining was used to detect the secretion of chondrocyte matrix. Immunofluorescence method was used to identify the
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