Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search

The relationship between IGF1 and the expression spectrum of miRNA in the placenta of preeclampsia patients

Objectives: Pre-eclampsia (PE) affects many women worldwide and remains the leading cause of morbidity and mortality in neonatal and maternal settings. Abnormal expression of placental microRNAs (miRNAs) may be associated with PE. Material and methods: This study was conducted to the relationship between IGF1 and the expression spectrum of miRNA in the placenta of preeclampsia patient. The expression of miRNA in placental tissue was compared between pre-eclampsia (n = 6) and normal pregnant women (n = 5) miRNA targets were studied by computer simulation and functional assays. The role of miRNA was verified in trophoblast cell lines by apoptosis assay and invasion assay. Results: There was a significant increase in miRNAs in the placenta of women with pre-eclampsia compared with patients with normal pregnancy. Luciferase assay confirmed direct regulation of miRNA. Conclusions: The expression of IGF1 and miRNA was significantly increased in the placenta of patients with pre-eclampsia

The Effects of Consumer Cognitive Dissonance on Trust and E-WOM: Social Media Influencers in Tourism and Hospitality

Nowadays social media influencers (SMIs) play an important role in marketing by introducing products to their followers. Yet, scholarly research on the determining factors in consumer pre-purchase phrase, post-purchase response, especially cognitive dissonance, is still in its infancy. Guided by cognitive dissonance theory, this study proposed a theoretical model to explain how consumer involvement and source credibility of SMIs affects consumer e-WOM and trust toward the SMIs. The study is based on an administrative survey and structural equation modeling (SEM) will be employed to examine the theoretical model. The findings of the study offer a fresh theoretical and practical perspective of social media influencer marketing

Physiochemical characteristics and sensory properties of plant protein isolates–konjac glucomannan compound gels

Abstract In this study, the effects of konjac glucomannan (KGM) at different concentrations on the physiochemical and sensory properties of soy protein isolate (SPI), pea protein isolate (PPI), or peanut protein isolate (PNPI) compound gels were investigated. The results revealed that when the ratio of PNPI to KGM was 90:10, the denaturation temperature of PNPI could be significantly enhanced to 119.32°C by KGM modification. Concerning the textural and microstructural features, the amount of KGM addition had positive correlation with the hardness and chewiness of each compound gel, however, too much KGM addition will cause the unstable internal structure of the PNPI/KGM compound gels (70:30 and 60:40). Furthermore, sensory results indicated that PNPI/KGM (80:20), PPI/KGM (80:20), SPI/KGM (80:20) had great potential to be considered as prototypes for novel plant‐based products, which generated the highest acceptance scores of 5.04, 5.94, and 5.36 in each group, respectively

Research on the Diffusion Behavior of Cu in Low-Carbon Steel under High Temperatures

The effective diffusion of Cu in Fe is the key to forming a stable transition layer between copper and low-carbon steel, but it is seriously affected by several factors, especially temperature, and the diffusion of Cu can only be completed at high temperatures. In order to analyze the diffusion coefficient of Cu in low-carbon steel under high temperatures, and to obtain the best diffusion temperature range of Cu in steel, the electrodeposition method was used to prepare the diffusion couple of copper and low-carbon steel, which would be annealed under different temperatures for 6 h; meanwhile, the MD models were also used to analyze the diffusion behavior of Cu in Fe at different temperatures. The results show that the diffusion of Cu in low-carbon steel could be realized by high-temperature annealing, and as the temperature increases, the thickness of the Cu/low-carbon steel transition layer shows an increasing trend. When the annealing temperature is between 900 °C and 1000 °C, the thickness of the transition layer increases the fastest. The results of the MD models show that, when the temperature is in the phase transition zone, the main restrictive link for the diffusion of Cu in Fe is the phase transition process of Fe; additionally, when the temperature is higher, the main restrictive link for the diffusion of Cu in Fe is the activity of the atom

Research on the Diffusion Behavior of Cu in Low-Carbon Steel under High Temperatures

The effective diffusion of Cu in Fe is the key to forming a stable transition layer between copper and low-carbon steel, but it is seriously affected by several factors, especially temperature, and the diffusion of Cu can only be completed at high temperatures. In order to analyze the diffusion coefficient of Cu in low-carbon steel under high temperatures, and to obtain the best diffusion temperature range of Cu in steel, the electrodeposition method was used to prepare the diffusion couple of copper and low-carbon steel, which would be annealed under different temperatures for 6 h; meanwhile, the MD models were also used to analyze the diffusion behavior of Cu in Fe at different temperatures. The results show that the diffusion of Cu in low-carbon steel could be realized by high-temperature annealing, and as the temperature increases, the thickness of the Cu/low-carbon steel transition layer shows an increasing trend. When the annealing temperature is between 900 °C and 1000 °C, the thickness of the transition layer increases the fastest. The results of the MD models show that, when the temperature is in the phase transition zone, the main restrictive link for the diffusion of Cu in Fe is the phase transition process of Fe; additionally, when the temperature is higher, the main restrictive link for the diffusion of Cu in Fe is the activity of the atom

Genome-Wide Identification, Characterization, and Expression Profiling of the Legume BZR Transcription Factor Gene Family

The BRASSINAZOLE-RESISTANT (BZR) family of transcription factors (TFs) are positive regulators in the biosynthesis of brassinosteroids. The latter is a class of steroid hormones that affect a variety of developmental and physiological processes in plants. BZR TFs play essential roles in the regulation of plant growth and development, including multiple stress-resistance functions. However, the evolutionary history and individual expression patterns of the legume BZR genes has not been determined. In this study, we performed a genome-wide investigation of the BZR gene family in seven legume species. In total, 52 BZR genes were identified and characterized. By analyzing their phylogeny, we divided these BZR genes into five groups by comparison with orthologs/paralogs in Arabidopsis thaliana. The intron/exon structural patterns and conserved protein motifs of each gene were analyzed and showed high group-specificities. Legume BZR genes were unevenly distributed among their corresponding genomes. Genome and gene sequence comparisons revealed that gene expansion of the BZR TF family in legumes mainly resulted from segmental duplications and that this family has undergone purifying selection. Synteny analysis showed that BZR genes tended to localize within syntenic blocks conserved across legume genomes. The expression patterns of BZR genes among various legume vegetative tissues and in response to different abiotic stresses were analyzed using a combination of public transcriptome data and quantitative PCR. The patterns indicated that many BZR genes regulate legume organ development and differentiation, and significantly respond to drought and salt stresses. This study may provide valuable information for understanding the evolution of BZR gene structure and expression, and lays a foundation for future functional analysis of the legume BZR genes by species and by gene