Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification

Social media is daily creating massive multimedia content with paired image and text, presenting the pressing need to automate the vision and language understanding for various multimodal classification tasks. Compared to the commonly researched visual-lingual data, social media posts tend to exhibit more implicit image-text relations. To better glue the cross-modal semantics therein, we capture hinting features from user comments, which are retrieved via jointly leveraging visual and lingual similarity. Afterwards, the classification tasks are explored via self-training in a teacher-student framework, motivated by the usually limited labeled data scales in existing benchmarks. Substantial experiments are conducted on four multimodal social media benchmarks for image text relation classification, sarcasm detection, sentiment classification, and hate speech detection. The results show that our method further advances the performance of previous state-of-the-art models, which do not employ comment modeling or self-training.Comment: accepted to EMNLP 202

Topic-Guided Self-Introduction Generation for Social Media Users

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user's tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user's history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling

Causal relationship between gut microbiota and immune thrombocytopenia: a Mendelian randomization study of two samples

BackgroundSome observational studies have shown that immune thrombocytopenia (ITP) is highly associated with the alteration-composition of gut microbiota. However, the causality of gut microbiota on ITP has not yet been determined.MethodsBased on accessible summary statistics of the genome-wide union, the latent connection between ITP and gut microbiota was estimated using bi-directional Mendelian randomization (MR) and multivariable MR (MVMR) analyses. Inverse variance weighted (IVW), weighted median analyses, and MR-Egger regression methods were performed to examine the causal correlation between ITP and the gut microbiota. Several sensitivity analyses verified the MR results. The strength of causal relationships was evaluated using the MR-Steiger test. MVMR analysis was undertaken to test the independent causal effect. MR analyses of reverse direction were made to exclude the potential of reverse correlations. Finally, GO enrichment analyses were carried out to explore the biological functions.ResultsAfter FDR adjustment, two microbial taxa were identified to be causally associated with ITP (PFDR < 0.10), namely Alcaligenaceae (PFDR = 7.31 × 10–2) and Methanobacteriaceae (PFDR = 7.31 × 10–2). In addition, eight microbial taxa were considered as potentially causal features under the nominal significance (P < 0.05): Actinobacteria, Lachnospiraceae, Methanobacteria, Bacillales, Methanobacteriales, Coprococcus2, Gordonibacter, and Veillonella. According to the reverse-direction MR study findings, the gut microbiota was not significantly affected by ITP. There was no discernible horizontal pleiotropy or instrument heterogeneity. Finally, GO enrichment analyses showed how the identified microbial taxa participate in ITP through their underlying biological mechanisms.ConclusionSeveral microbial taxa were discovered to be causally linked to ITP in this MR investigation. The findings improve our understanding of the gut microbiome in the risk of ITP

CCKAR is a biomarker for prognosis and asynchronous brain metastasis of non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is the most common histological type of lung cancer, and brain metastasis (BM) is the most lethal complication of NSCLC. The predictive biomarkers and risk factors of asynchronous BM are still unknown.Materials and methodsA total of 203 patients with NSCLC were enrolled into our cohort and followed up. The clinicopathological factors such as tumor size, T stage, lymphatic invasion, metastasis and asynchronous BM were investigated. CCKAR expression in NSCLC and resected BM was assessed by IHC, and CCKAR mRNAs in NSCLC and para-tumor tissues were estimated by qRT-PCR. The correlations between CCKAR expression, BM and other clinicopathological factors were assessed by chi-square test, and prognostic significance of CCKAR was estimated by univariate and multivariate analyses.ResultsCCKAR was highly expressed in NSCLC tissues compared with para-tumor tissues. CCKAR expression in NSCLC was significantly associated with asynchronous BM. The BM percentages for NSCLC patients with low and high CCKAR were surprisingly 5.2% and 66.6%, respectively. CCKAR expression and BM were unfavorable factors predicting unfavorable outcome of NSCLC. Moreover, CCKAR expression in NSCLC was an independent risk factor of asynchronous BM.ConclusionsCCKAR is a prognostic biomarker of NSCLC. CCKAR expression in NSCLC is positively associated with asynchronous BM, and is a risk factor of asynchronous BM from NSCLC

Rh(III)-Catalyzed Annulation of Boc-Protected Benzamides with Diazo Compounds: Approach to Isocoumarins

A mild rhodium-catalyzed annulation of Boc-protected benzamides with diazo compounds via C−C/C−O bond formation has been explored. In the presence of [Cp*RhCl2]2, AgSbF6 and Cs2CO3, Boc-protected benzamides can be effectively annulated to yield isocoumarins in 0.5–2 h

A simple copper-catalyzed two-step one-pot synthesis of indolo[1,2-a]quinazoline

A convenient CuI/L-proline-catalyzed, two-step one-pot method has been developed for the preparation of indolo[1,2-a]quinazoline derivatives using a sequential Ullmann-type C–C and C–N coupling. This protocol provides an operationally simple and rapid strategy for preparing indolo[1,2-a]quinazoline derivatives and displays good functional group tolerance. All the starting materials are commercial available or can be easily prepared

Sulfoximines-Assisted Rh(III)-Catalyzed C–H Activation and Intramolecular Annulation for the Synthesis of Fused Isochromeno-1,2-Benzothiazines Scaffolds under Room Temperature

A mild and facile Cp*Rh(III)-catalyzed C–H activation and intramolecular cascade annulation protocol has been proposed for the furnishing of highly fused isochromeno-1,2-benzothiazines scaffolds using S-phenylsulfoximides and 4-diazoisochroman-3-imine as substrates under room temperature. This method features diverse substituents and functional groups tolerance and relatively mild reaction conditions with moderate to excellent yields. Additionally, retentive configuration of sulfoximides in the conversion has been verified

Ruthenium-Catalyzed C–H Activations for the Synthesis of Indole Derivatives

The synthesis of substituted indoles has received great attention in the field of organic synthesis methodology. C–H activation makes it possible to obtain a variety of designed indole derivatives in mild conditions. Ruthenium catalyst, as one of the most significant transition-metal catalysts, has been contributing in the synthesis of indole scaffolds through C–H activation and C–H activation on indoles. Herein, we attempt to present an overview about the construction strategies of indole scaffold and site-specific modifications for indole scaffold via ruthenium-catalyzed C–H activations in recent years

GPBAR1 promotes proliferation and is related to poor prognosis of high-grade glioma via inducing MAFB expression

Background. Glioma is the most prevalent brain tumors with extremely poor prognosis, but the prognostic biomarkers of high-grade (grade III and IV) gliomas (HGG) are still insufficient. Materials and methods. In our study, we investigated the expression of GPBAR1 in HGG by qRT-PCR and immunohistochemistry (IHC), and evaluated the clinical significance of GPBAR1 with univariate and multivariate analyses. By retrieving the data from TCGA, we screened the genes significantly associated with GPBAR1, and identified the correlation between GPBAR1 and MAFB. By experiments in vitro, we showed the pivotal role of MAFB in GPBAR1-induced proliferation of HGG. Results. GPBAR1 expression in HGGs was significantly higher than that in normal brain tissues. GPBAR1 was an independent prognostic biomarker of HGG. GPBAR1 promoted the proliferation of HGG by inducing MAFB expression. MAFB was also a prognostic biomarker of HGG, and patients with coexpression of MAFB and GPBAR1 had worse prognosis. Conclusions. GPBAR1 promoted the proliferation of HGG by inducing MAFB expression. Both GPBAR1 and MAFB were prognostic biomarkers of HGG, and patients with co-expression of MAFB and GPBAR1 had worse prognosis than those with only GPBAR1 or MAFB expression

Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning

Visual storytelling is a task of creating a short story based on photo streams. Different from visual captions, stories contain not only factual descriptions, but also imaginary concepts that do not appear in the images. In this paper, we propose a novel imagine-reason-write generation framework (IRW) for visual storytelling, inspired by the logic of humans when they write the story. First, an imagine module is leveraged to learn the imaginative storyline explicitly, improving the coherence and reasonability of the generated story. Second, we employ a reason module to fully exploit the external knowledge (commonsense knowledge base) and task-specific knowledge (scene graph and event graph) with relational reasoning method based on the storyline. In this way, we can effectively capture the most informative commonsense and visual relationships among objects in images, which enhances the diversity and informativeness of the generated story. Finally, we integrate the imaginary concepts and relational knowledge to generate human-like story based on the original semantics of images. Extensive experiments on a benchmark dataset (i.e., VIST) demonstrate that the proposed IRW framework significantly outperforms the state-of-the-art methods across multiple evaluation metrics