InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.Comment: 5 pages, 4 figure

GraphMFT: A Graph Network based Multimodal Fusion Technique for Emotion Recognition in Conversation

Multimodal machine learning is an emerging area of research, which has received a great deal of scholarly attention in recent years. Up to now, there are few studies on multimodal Emotion Recognition in Conversation (ERC). Since Graph Neural Networks (GNNs) possess the powerful capacity of relational modeling, they have an inherent advantage in the field of multimodal learning. GNNs leverage the graph constructed from multimodal data to perform intra- and inter-modal information interaction, which effectively facilitates the integration and complementation of multimodal data. In this work, we propose a novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion recognition in conversation. Multimodal data can be modeled as a graph, where each data object is regarded as a node, and both intra- and inter-modal dependencies existing between data objects can be regarded as edges. GraphMFT utilizes multiple improved graph attention networks to capture intra-modal contextual information and inter-modal complementary information. In addition, the proposed GraphMFT attempts to address the challenges of existing graph-based multimodal conversational emotion recognition models such as MMGCN. Empirical results on two public multimodal datasets reveal that our model outperforms the State-Of-The-Art (SOTA) approaches with the accuracy of 67.90% and 61.30%.Comment: Accepted by Neurocomputin

GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches.Comment: 13 page

GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin.Comment: 14 page

Hepcidin as a key iron regulator mediates glucotoxicity-induced pancreatic β-cell dysfunction

It has been well established that glucotoxicity induces pancreatic β-cells dysfunction; however, the precise mechanism remains unclear. Our previous studies demonstrated that high glucose concentrations are associated with decreased hepcidin expression, which inhibits insulin synthesis. In this study, we focused on the role of low hepcidin level-induced increased iron deposition in β-cells and the relationship between abnormal iron metabolism and β-cell dysfunction. Decreased hepcidin expression increased iron absorption by upregulating transferrin receptor 1 (TfR1) and divalent metal transporter 1 (DMT1) expression, resulting in iron accumulation within cells. Prussia blue stain and calcein-AM assays revealed greater iron accumulation in the cytoplasm of pancreatic tissue isolated from db/db mice, cultured islets and Min6 cells in response to high glucose stimulation. Increased cytosolic iron deposition was associated with greater Fe2+ influx into the mitochondria, which depolarized the mitochondria membrane potential, inhibited ATP synthesis, generated excessive ROS and induced oxidative stress. The toxic effect of excessive iron on mitochondrial function eventually resulted in impaired insulin secretion. The restricted iron content in db/db mice via reduced iron intake or accelerated iron clearance improved blood glucose levels with decreased fasting blood glucose (FBG), fasting blood insulin (FIns), HbA1c level, as well as improved intraperitoneal glucose tolerance test (IPGTT) results. Thus, our study may reveal the mechanism involved in the role of hepcidin in the glucotoxcity impaired pancreatic β cell function pathway

Preparation and Characterization of an Amphipathic Magnetic Nanosphere

The amphipathic magnetic nanospheres were synthesized using C(8) and polyethylene glycol as ligands. Their morphology, structure, and composition were characterized by transmission electron microscope, Fourier transform infrared, and elementary analysis. The prepared materials presented uniform sphere with size distribution about 200 nm. The magnetic characteristics of magnetic nanomaterials were measured by vibrating sample magnetometer. The target products had a saturation magnetization value of 50 emu g(−1) and superparamagnetism. The adsorption capability was also studied by static tests, and the material was applied to enrich benzenesulfonamide from calf serum. The results exhibited that the C(8)-PEG phase owned better adsorption capability, biocompatible property, and dispersivity in aqueous samples

Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo

PurposeThis study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic.MethodsThe analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16–24 and the linguistic characteristics of the posts.ResultsThe study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of “Affect” showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as “Negative emotion” and “Sadness” also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress.ConclusionThe findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment

Activation of PI3K/AKT and ERK MAPK signal pathways is required for the induction of lytic cycle replication of Kaposi's Sarcoma-associated herpesvirus by herpes simplex virus type 1

<p>Abstract</p> <p>Background</p> <p>Kaposi's sarcoma-associated herpesvirus (KSHV) is causally linked to several acquired immunodeficiency syndrome-related malignancies, including Kaposi's sarcoma (KS), primary effusion lymphoma (PEL) and a subset of multicentric Castleman's disease. Regulation of viral lytic replication is critical to the initiation and progression of KS. Recently, we reported that herpes simplex virus type 1 (HSV-1) was an important cofactor that activated lytic cycle replication of KSHV. Here, we further investigated the possible signal pathways involved in HSV-1-induced reactivation of KSHV.</p> <p>Results</p> <p>By transfecting a series of dominant negative mutants and protein expressing constructs and using pharmacologic inhibitors, we found that either Janus kinase 1 (JAK1)/signal transducer and activator of transcription 3 (STAT3) or JAK1/STAT6 signaling failed to regulate HSV-1-induced KSHV replication. However, HSV-1 infection of BCBL-1 cells activated phosphatidylinositol 3-kinase (PI3K)/protein kinase B (PKB, also called AKT) pathway and inactivated phosphatase and tensin homologue deleted on chromosome ten (PTEN) and glycogen synthase kinase-3β (GSK-3β). PTEN/PI3K/AKT/GSK-3β pathway was found to be involved in HSV-1-induced KSHV reactivation. Additionally, extracellular signal-regulated protein kinase (ERK) mitogen-activated protein kinase (MAPK) pathway also partially contributed to HSV-1-induced KSHV replication.</p> <p>Conclusions</p> <p>HSV-1 infection stimulated PI3K/AKT and ERK MAPK signaling pathways that in turn contributed to KSHV reactivation, which provided further insights into the molecular mechanism controlling KSHV lytic replication, particularly in the context of HSV-1 and KSHV co-infection.</p