Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph

Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, in this paper we propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene graphs to extract both global and local fine-grained image information and considers them as elements of the cross-modal semantic graph along with tokens from tweets. The sequential cross-modal semantic graph is represented as a sequence with a multi-modal adjacency matrix indicating relationships between elements. Experimental results show that the approach outperforms existing methods and achieves state-of-the-art performance on two standard datasets. Further analysis has demonstrated that the model can implicitly learn the correlation between fine-grained information of the image and the text with the given target. Our code is available at https://github.com/zjukg/SeqCSG.Comment: ICANN 2023, https://github.com/zjukg/SeqCS

Fabrication and Investigation of Two-Component Film of 2,5-Diphenyloxazole and Octafluoronaphthalene Exhibiting Tunable Blue/Bluish Violet Fluorescence Based on Low Vacuum Physical Vapor Deposition Method

Organic luminescent materials play an important role in the fields of light-emitting diodes and fluorescent imaging. Moreover, new synthetic approaches towards π-conjugated molecular systems with high fluorescence quantum efficiency are highly desired. Herein, different 2,5-diphenyloxazole-octafluoronaphthalene (DPO-OFN) films with tunable fluorescence have been prepared by Low Vacuum Physical Vapor Deposition (LVPVD) method. DPO-OFN films showed some changed properties, such as molecular vibration and fluorescence. All films exhibited blue/bluish violet fluorescence and showed blue shift, in comparison with pristine DPO. This work introduced a new method to fabricate two-component molecular materials with tunable blue/bluish violet luminescence properties and provided a new perspective to prepare organic luminescent film materials, layer film materials, cocrystal materials, and cocrystal film materials. Importantly, these materials have potential applications in the fields of next generation of photofunctional materials

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-shot image classification are attributes which are annotations for class-level visual characteristics. However, the current methods often fail to discriminate those subtle visual distinctions between images due to not only the shortage of fine-grained annotations, but also the attribute imbalance and co-occurrence. In this paper, we present a transformer-based end-to-end ZSL method named DUET, which integrates latent semantic knowledge from the pre-trained language models (PLMs) via a self-supervised multi-modal learning paradigm. Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives. We find that our DUET can achieve state-of-the-art performance on three standard ZSL benchmarks and a knowledge graph equipped ZSL benchmark. Its components are effective and its predictions are interpretable.Comment: AAAI 2023 (Oral). Repository: https://github.com/zjukg/DUE

Study of the correlation between vitamin D level and liver function in children with infectious mononucleosis

Objective To investigate the correlation between the serum level of 25-hydroxyvitamin D （25（OH）D） and liver function in children with infectious mononucleosis （IM）. Methods Ninety children with acute IM were enrolled into the IM group， and 40 healthy children who underwent physical examination during the same period were allocated into the control group. Serum level of 25（OH）D was determined by electrochemiluminescence and the viral load of Epstein-Barr virus DNA （EBV-DNA） in plasma was determined by quantitative fluorescent RT-PCR. Clinical data and serum 25（OH） D levels were compared between two groups. The correlation between 25（OH）D level and atypical lymphocytes， liver function parameters and plasma EBV-DNA in children with IM was analyzed. Results Serum 25（OH）D level in the IM group was significantly lower， whereas the 25（OH）D inadequacy rate was significantly higher than those in the control group （both P < 0.05）. Serum 25（OH）D level was negatively correlated with atypical lymphocytes in the IM group （P < 0.05）. The levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， glutamyl transpeptidase （GGT）， and adenosine deaminase （ADA） in IM children with serum 25（OH）D inadequacy were higher than those in their counterparts with adequate serum 25（OH）D （all P < 0.05）. No significant differences were found in total bilirubin （TBIL）， direct bilirubin （DBIL） and plasma EBV-DNA load between the adequate and inadequate subgroups （all P > 0.05）. In the IM group， serum 25（OH）D level was negatively correlated with ALT， GGT， and ADA （all P < 0.05）， whereas positively correlated with CHE （P < 0.05） and had no correlation with AST， TBIL or DBIL （all P > 0.05）. Conclusions Serum vitamin D insufficiency occurs in children with IM. Vitamin D may be involved in the incidence and development of the course of IM， which is probably associated with liver function impairment

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with relevant images attached. We noticed that current MMEA algorithms all globally adopt the KG-level modality fusion strategies for multi-modal entity representation but ignore the variation in modality preferences for individual entities, hurting the robustness to potential noise involved in modalities (e.g., blurry images and relations). In this paper, we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for entity-level feature aggregation. A modal-aware hard entity replay strategy is further proposed for addressing vague entity details. Experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has a comparable number of parameters, optimistic speed, and good interpretability. Our code and data are available at https://github.com/zjukg/MEAformer.Comment: Repository: https://github.com/zjukg/MEAforme

Monocarboxylate transporter upregulation in induced regulatory T cells promotes resistance to anti-PD-1 therapy in hepatocellular carcinoma patients

BackgroundProgrammed cell death-1 (PD-1) immune checkpoint inhibitors are not effective in treating all patients with hepatocellular carcinoma (HCC), and regulatory T cells (Tregs) may determine the resistance to anti-PD-1 therapy.MethodsPatients were divided into two groups based on the clinical efficacy of anti-PD-1 therapy. Flow cytometry was used to determine the phenotype of CD4+, CD8+, and Tregs in peripheral blood mononuclear cells (PBMCs). CD4+CD45RA+T cells were sorted to analyze Treg differentiation and function.ResultsNo significant differences were found between resistant and sensitive patients in the percentage of CD4+ T cells and Tregs in PBMCs or the differentiation and function of induced Tregs (iTregs). However, iTregs from resistant patients presented higher monocarboxylate transporter (MCT) expression. Lactate induced more iTregs and improved OXPHOS levels in the resistant group. MCT1 and MCT2 were highly expressed in tumor-infiltrating Tregs, and patients with higher MCT1 expression had worse clinical outcomes. Combinatorial therapy with MCT antibody and anti-PD-1 therapy effectively inhibited tumor growth.ConclusionMCT and its downstream lactate signal in Tregs can confer anti-PD-1 resistance and may be a marker of poor prognosis in HCC

Fabrication and Investigation of Two-Component Film of 2,5-Diphenyloxazole and Octafluoronaphthalene Exhibiting Tunable Blue/Bluish Violet Fluorescence Based on Low Vacuum Physical Vapor Deposition Method

Organic luminescent materials play an important role in the fields of light-emitting diodes and fluorescent imaging. Moreover, new synthetic approaches towards -conjugated molecular systems with high fluorescence quantum efficiency are highly desired. Herein, different 2,5-diphenyloxazole-octafluoronaphthalene (DPO-OFN) films with tunable fluorescence have been prepared by Low Vacuum Physical Vapor Deposition (LVPVD) method. DPO-OFN films showed some changed properties, such as molecular vibration and fluorescence. All films exhibited blue/bluish violet fluorescence and showed blue shift, in comparison with pristine DPO. This work introduced a new method to fabricate two-component molecular materials with tunable blue/bluish violet luminescence properties and provided a new perspective to prepare organic luminescent film materials, layer film materials, cocrystal materials, and cocrystal film materials. Importantly, these materials have potential applications in the fields of next generation of photofunctional materials

Final report on project SP1210: Lowland peatland systems in England and Wales – evaluating greenhouse gas fluxes and carbon balances

Lowland peatlands represent one of the most carbon-rich ecosystems in the UK. As a result of widespread habitat modification and drainage to support agriculture and peat extraction, they have been converted from natural carbon sinks into major carbon sources, and are now amongst the largest sources of greenhouse gas (GHG) emissions from the UK land-use sector. Despite this, they have previously received relatively little policy attention, and measures to reduce GHG emissions either through re-wetting and restoration or improved management of agricultural land remain at a relatively early stage. In part, this has stemmed from a lack of reliable measurements on the carbon and GHG balance of UK lowland peatlands. This project aimed to address this evidence gap via an unprecedented programme of consistent, multi year field measurements at a total of 15 lowland peatland sites in England and Wales, ranging from conservation managed ‘near-natural’ ecosystems to intensively managed agricultural and extraction sites. The use of standardised measurement and data analysis protocols allowed the magnitude of GHG emissions and removals by peatlands to be quantified across this heterogeneous data set, and for controlling factors to be identified. The network of seven flux towers established during the project is believed to be unique on peatlands globally, and has provided new insights into the processes the control GHG fluxes in lowland peatlands. The work undertaken is intended to support the future development and implementation of agricultural management and restoration measures aimed at reducing the contribution of these important ecosystems to UK GHG emissions

A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins

Designing protein mutants with high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, an innovative deep learning approach for the zero-shot prediction of both protein stability and enzymatic activity. PRIME leverages temperature-guided language modelling, providing robust and precise predictions without relying on prior experimental mutagenesis data. Tested against 33 protein datasets, PRIME demonstrated superior predictive performance and generalizability compared to current state-of-the-art modelsComment: arXiv admin note: text overlap with arXiv:2304.0378