Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval

Current image-text retrieval methods have demonstrated impressive performance in recent years. However, they still face two problems: the inter-modal matching missing problem and the intra-modal semantic loss problem. These problems can significantly affect the accuracy of image-text retrieval. To address these challenges, we propose a novel method called Cross-modal and Uni-modal Soft-label Alignment (CUSA). Our method leverages the power of uni-modal pre-trained models to provide soft-label supervision signals for the image-text retrieval model. Additionally, we introduce two alignment techniques, Cross-modal Soft-label Alignment (CSA) and Uni-modal Soft-label Alignment (USA), to overcome false negatives and enhance similarity recognition between uni-modal samples. Our method is designed to be plug-and-play, meaning it can be easily applied to existing image-text retrieval models without changing their original architectures. Extensive experiments on various image-text retrieval models and datasets, we demonstrate that our method can consistently improve the performance of image-text retrieval and achieve new state-of-the-art results. Furthermore, our method can also boost the uni-modal retrieval performance of image-text retrieval models, enabling it to achieve universal retrieval. The code and supplementary files can be found at https://github.com/lerogo/aaai24_itr_cusa.Comment: 9 pages, Accepted by AAAI202

Construction and immunological characterization of CD40L or GM-CSF incorporated Hantaan virus like particle

Infection of Hantaan virus (HTNV) usually causes hemorrhagic fever with renal syndrome (HFRS). China has the worst epidemic incidence of HFRS as well as high fatality. Inactivated whole virus has been used for HFRS vaccination, however there are still problems such as safety concerns. CD40 ligand (CD40L) and granulocyte macrophage colony-stimulating factor (GM-CSF) are well-known immune stimulating molecules that can enhance antigen presenting, lymphocytes activation and maturation, incorporation of CD40L and GM-CSF to the surface of virus like particles (VLPs) can greatly improve the vaccination effect. We constructed eukaryotic vectors expressing HTNV M segment and S segment, as well as vectors expressing HTNV M segment with CD40L or GM-CSF, our results showed successful production of CD40L or GM-CSF incorporated HTNV VLPs. In vitro stimulation with CD40L or GM-CSF anchored HTNV VLP showed enhanced activation of macrophages and DCs. CD40L/GM-CSF incorporated VLP can induce higher level of HTNV specific antibody and neutralizing antibody in mice. Immunized mice splenocytes showed higher ability of secreting IFN-γ and IL-2, as well as enhancing CTL activity. These results suggest CD40L/GM-CSF incorporated VLP can serve as prospective vaccine candidate

Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning

Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance. However, researchers have yet been able to effectively leverage the intrinsic connection between the two tasks as they train these tasks in a separate or pipeline manner, which means their performance can not be well balanced. In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task. More specifically, we explicitly exploit the probabilistic correlation between code summarization and code generation with dual learning, and utilize the two encoders for code summarization and code generation to train the code retrieval task via multi-task learning. We have carried out extensive experiments on an existing dataset of SQL and Python, and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.Comment: Published at The Web Conference (WWW) 2020, full pape

Boosting with an aerosolized Ad5-nCoV elicited robust immune responses in inactivated COVID-19 vaccines recipients

IntroductionThe SARS-CoV-2 Omicron variant has become the dominant SARS-CoV-2 variant and exhibits immune escape to current COVID-19 vaccines, the further boosting strategies are required.MethodsWe have conducted a non-randomized, open-label and parallel-controlled phase 4 trial to evaluate the magnitude and longevity of immune responses to booster vaccination with intramuscular adenovirus vectored vaccine (Ad5-nCoV), aerosolized Ad5-nCoV, a recombinant protein subunit vaccine (ZF2001) or homologous inactivated vaccine (CoronaVac) in those who received two doses of inactivated COVID-19 vaccines. ResultsThe aerosolized Ad5-nCoV induced the most robust and long-lasting neutralizing activity against Omicron variant and IFNg T-cell response among all the boosters, with a distinct mucosal immune response. SARS-CoV-2-specific mucosal IgA response was substantially generated in subjects boosted with the aerosolized Ad5-nCoV at day 14 post-vaccination. At month 6, participants boosted with the aerosolized Ad5-nCoV had remarkably higher median titer and seroconversion of the Omicron BA.4/5-specific neutralizing antibody than those who received other boosters. DiscussionOur findings suggest that aerosolized Ad5-nCoV may provide an efficient alternative in response to the spread of the Omicron BA.4/5 variant.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=152729, identifier ChiCTR2200057278

Effects of Biochar Application Pyrolyzed at Different Temperatures on Soil Properties, Growth and Leaf Secondary Metabolite Accumulation in Cyclocarya paliurus

Cyclocarya paliurus is a well-known multifunctional tree species and its leaves are in especially high demand for tea production and medical utilization in China. To meet the enormous requirements of its leaf production, lots of C. paliurus plantations have been established for harvesting the leaves, producing a large quantity of pruning residues during their management. In this study, biochar at different pyrolysis temperatures (300 °C, 500 °C and 700 °C) were prepared, utilizing the pruning residues, and the effects of biochar additions pyrolyzed at different temperatures on soil properties, growth and leaf secondary metabolite accumulation in C. paliurus were investigated. The results showed that the chemical properties and FT-IR spectra of wheel wingnut-based biochar were significantly influenced by the pyrolysis temperatures, and the application of biochars pyrolyzed at different temperatures significantly affected soil pH and nutrient availability, as well as the growth, nutrient uptake and secondary metabolite accumulation of C. paliurus seedlings (p < 0.05). Correlation analysis indicated that the total contents of polyphenols, flavonoids and triterpenoids in C. paliurus leaves were negatively correlated with the contents of total phosphorus (P) and total potassium (K) in the leaves, but positively correlated with the ratios of carbon (C)/nitrogen (N) and C/P. After 200 days of biochar treatment, the highest biomass production and leaf secondary metabolite accumulation in C. paliurus were obtained in the addition of biochar pyrolyzed at 500 °C. The findings from this pot experiment provide a potential application in C. paliurus plantations, though long-term field experiments are required to optimize the quantity of biochar addition, based on soil conditions and stand age at the planting sites

IterDE: An Iterative Knowledge Distillation Framework for Knowledge Graph Embeddings

Knowledge distillation for knowledge graph embedding (KGE) aims to reduce the KGE model size to address the challenges of storage limitations and knowledge reasoning efficiency. However, current work still suffers from the performance drops when compressing a high-dimensional original KGE model to a low-dimensional distillation KGE model. Moreover, most work focuses on the reduction of inference time but ignores the time-consuming training process of distilling KGE models. In this paper, we propose IterDE, a novel knowledge distillation framework for KGEs. First, IterDE introduces an iterative distillation way and enables a KGE model to alternately be a student model and a teacher model during the iterative distillation process. Consequently, knowledge can be transferred in a smooth manner between high-dimensional teacher models and low-dimensional student models, while preserving good KGE performances. Furthermore, in order to optimize the training process, we consider that different optimization objects between hard label loss and soft label loss can affect the efficiency of training, and then we propose a soft-label weighting dynamic adjustment mechanism that can balance the inconsistency of optimization direction between hard and soft label loss by gradually increasing the weighting of soft label loss. Our experimental results demonstrate that IterDE achieves a new state-of-the-art distillation performance for KGEs compared to strong baselines on the link prediction task. Significantly, IterDE can reduce the training time by 50% on average. Finally, more exploratory experiments show that the soft-label weighting dynamic adjustment mechanism and more fine-grained iterations can improve distillation performance

Effects of Biochar Application Pyrolyzed at Different Temperatures on Soil Properties, Growth and Leaf Secondary Metabolite Accumulation in <i>Cyclocarya paliurus</i>

Cyclocarya paliurus is a well-known multifunctional tree species and its leaves are in especially high demand for tea production and medical utilization in China. To meet the enormous requirements of its leaf production, lots of C. paliurus plantations have been established for harvesting the leaves, producing a large quantity of pruning residues during their management. In this study, biochar at different pyrolysis temperatures (300 °C, 500 °C and 700 °C) were prepared, utilizing the pruning residues, and the effects of biochar additions pyrolyzed at different temperatures on soil properties, growth and leaf secondary metabolite accumulation in C. paliurus were investigated. The results showed that the chemical properties and FT-IR spectra of wheel wingnut-based biochar were significantly influenced by the pyrolysis temperatures, and the application of biochars pyrolyzed at different temperatures significantly affected soil pH and nutrient availability, as well as the growth, nutrient uptake and secondary metabolite accumulation of C. paliurus seedlings (p C. paliurus leaves were negatively correlated with the contents of total phosphorus (P) and total potassium (K) in the leaves, but positively correlated with the ratios of carbon (C)/nitrogen (N) and C/P. After 200 days of biochar treatment, the highest biomass production and leaf secondary metabolite accumulation in C. paliurus were obtained in the addition of biochar pyrolyzed at 500 °C. The findings from this pot experiment provide a potential application in C. paliurus plantations, though long-term field experiments are required to optimize the quantity of biochar addition, based on soil conditions and stand age at the planting sites

Towards Continual Knowledge Graph Embedding via Incremental Distillation

Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To address this issue, the continual knowledge graph embedding (CKGE) task has been proposed to train the KGE model by learning emerging knowledge efficiently while simultaneously preserving decent old knowledge. However, the explicit graph structure in KGs, which is critical for the above goal, has been heavily ignored by existing CKGE methods. On the one hand, existing methods usually learn new triples in a random order, destroying the inner structure of new KGs. On the other hand, old triples are preserved with equal priority, failing to alleviate catastrophic forgetting effectively. In this paper, we propose a competitive method for CKGE based on incremental distillation (IncDE), which considers the full use of the explicit graph structure in KGs. First, to optimize the learning order, we introduce a hierarchical strategy, ranking new triples for layer-by-layer learning. By employing the inter- and intra-hierarchical orders together, new triples are grouped into layers based on the graph structure features. Secondly, to preserve the old knowledge effectively, we devise a novel incremental distillation mechanism, which facilitates the seamless transfer of entity representations from the previous layer to the next one, promoting old knowledge preservation. Finally, we adopt a two-stage training paradigm to avoid the over-corruption of old knowledge influenced by under-trained new knowledge. Experimental results demonstrate the superiority of IncDE over state-of-the-art baselines. Notably, the incremental distillation mechanism contributes to improvements of 0.2%-6.5% in the mean reciprocal rank (MRR) score. More exploratory experiments validate the effectiveness of IncDE in proficiently learning new knowledge while preserving old knowledge across all time steps

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy

Background Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.Methods Clinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.Results 151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).Conclusion The promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model

CircNUP54 promotes hepatocellular carcinoma progression via facilitating HuR cytoplasmic export and stabilizing BIRC3 mRNA

Abstract Circular RNAs (circRNAs) have been implicated in tumorigenesis and progression of various cancers. However, the underlying mechanisms of circRNAs in hepatocellular carcinoma (HCC) have not been fully elucidated. Herein, a new oncogenic circRNA, hsa_circ_0070039 (circNUP54), was identified to be significantly upregulated in HCC through circRNA sequencing. As verified in 68 HCC samples, circNUP54 overexpression was correlated with aggressive cancerous behaviors and poor outcomes. Moreover, the function experiments showed that knockdown of circNUP54 inhibited the malignant progression of HCC in vitro and in vivo, whereas overexpression of circNUP54 had the opposite role. Mechanistic investigations carried out by RNA pull-down, RNA immunoprecipitation, and immunofluorescence revealed that circNUP54 interacted with the RNA-binding protein Hu-antigen R (HuR) and promoted its cytoplasmic export. The cytoplasmic accumulation of HuR stabilized the downstream BIRC3 mRNA through its binding to the 3′ UTR region. Consequently, the encoded protein of BIRC3, cellular inhibitor of apoptosis 2 (cIAP2), proceeded to activate the NF-κB signal pathway and ultimately contributed to HCC progression. In addition, depletion of BIRC3 rescued the pro-tumorigenic effect of circNUP54 on HCC cells. Overall, this study demonstrated that circNUP54 facilitates HCC progression via regulating the HuR/BIRC3/NF-κB axis, which may serve as a promising therapeutic target for HCC treatment