Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation

The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can significantly improve the performance of recommendations. Previous works model users' intentions by considering the predefined label in auxiliary information or introducing stochastic data augmentation to learn purposes in the latent space. However, the auxiliary information is sparse and not always available for recommender systems, and introducing stochastic data augmentation may introduce noise and thus change the intentions hidden in the sequence. Therefore, leveraging user intentions for sequential recommendation (SR) can be challenging because they are frequently varied and unobserved. In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions. Specifically, ICSRec first segments a user's sequential behaviors into multiple subsequences by using a dynamic sliding operation and takes these subsequences into the encoder to generate the representations for the user's intentions. To tackle the problem of no explicit labels for purposes, ICSRec assumes different subsequences with the same target item may represent the same intention and proposes a coarse-grain intent contrastive learning to push these subsequences closer. Then, fine-grain intent contrastive learning is mentioned to capture the fine-grain intentions of subsequences in sequential behaviors. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of the proposed ICSRec model compared with baseline methods.Comment: 10pages, 5figures, WSDM2024. arXiv admin note: text overlap with arXiv:2304.0776

Meta-optimized Contrastive Learning for Sequential Recommendation

Contrastive Learning (CL) performances as a rising approach to address the challenge of sparse and noisy recommendation data. Although having achieved promising results, most existing CL methods only perform either hand-crafted data or model augmentation for generating contrastive pairs to find a proper augmentation operation for different datasets, which makes the model hard to generalize. Additionally, since insufficient input data may lead the encoder to learn collapsed embeddings, these CL methods expect a relatively large number of training data (e.g., large batch size or memory bank) to contrast. However, not all contrastive pairs are always informative and discriminative enough for the training processing. Therefore, a more general CL-based recommendation model called Meta-optimized Contrastive Learning for sequential Recommendation (MCLRec) is proposed in this work. By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation. Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the model augmenters, which helps to improve the quality of contrastive pairs without enlarging the amount of input data. Finally, a contrastive regularization term is considered to encourage the augmentation model to generate more informative augmented views and avoid too similar contrastive pairs within the meta updating. The experimental results on commonly used datasets validate the effectiveness of MCLRec.Comment: 11 Pages,8 figure

Mechanisms and applications of radiation-induced oxidative stress in regulating cancer immunotherapy

Radiotherapy (RT) is an effective treatment option for cancer patients, which induces the production of reactive oxygen species (ROS) and causes oxidative stress (OS), leading to the death of tumor cells. OS not only causes apoptosis, autophagy and ferroptosis, but also affects tumor immune response. The combination of RT and immunotherapy has revolutionized the management of various cancers. In this process, OS caused by ROS plays a critical role. Specifically, RT-induced ROS can promote the release of tumor-associated antigens (TAAs), regulate the infiltration and differentiation of immune cells, manipulate the expression of immune checkpoints, and change the tumor immune microenvironment (TME). In this review, we briefly summarize several ways in which IR induces tumor cell death and discuss the interrelationship between RT-induced OS and antitumor immunity, with a focus on the interaction of ferroptosis with immunogenic death. We also summarize the potential mechanisms by which ROS regulates immune checkpoint expression, immune cells activity, and differentiation. In addition, we conclude the therapeutic opportunity improving radiotherapy in combination with immunotherapy by regulating OS, which may be beneficial for clinical treatment

Endoplasmic Reticulum Aminopeptidase 1 Is Involved in Anti-viral Immune Response of Hepatitis B Virus by Trimming Hepatitis B Core Antigen to Generate 9-Mers Peptides

Endoplasmic reticulum aminopeptidase 1 (ERAP1) is a processing enzyme of antigenic peptides presented to major histocompatibility complex (MHC) class I molecules. ERAP1-dependent trimming of epitope repertoire determines an efficacy of adoptive CD8+ T-cell responses in several viral diseases; however, its role in hepatitis B virus (HBV) infection remains unknown. Here, we show that the serum level of ERAP1 in patients with chronic hepatitis B (CHB) (n = 128) was significantly higher than that of healthy controls (n = 44) (8.78 ± 1.82 vs. 3.52 ± 1.61, p < 0.001). Furthermore, peripheral ERAP1 level is moderately correlated with HBV DNA level in patients with CHB (r = 0.731, p < 0.001). HBV-transfected HepG2.2.15 cells had substantially increased ERAP1 expression and secretion than the germline HepG2 cells (p < 0.001). The co-culture of ERAP1-specific inhibitor ERAP1-IN-1 pretreated HepG2.2.15 cells or ERAP1 knockdown HepG2.2.15 cells with CD8+ T cells led to 14–24% inhibition of the proliferation of CD8+ T cells. Finally, liquid chromatography tandem mass spectrometry (LC-MS/MS) test demonstrated that ERAP1-IN-1 blocks completely the production of a 9-mers peptide (30–38, LLDTASALY) derived from Hepatitis B core antigen (HBcAg). The predictive analysis by NetMHCpan-4.1 server showed that human leukocyte antigen (HLA)-C*04:01 is a strong binder for the 9-mers peptide in HepG2.2.15 cells. Taken together, our results demonstrated that ERAP1 trims HBcAg to produce 9-mers LLDTASALY peptides for binding onto HLA-C*04:01 in HepG2.2.15 cells, facilitating the potential activation of CD8+ T cells

A Wall-Associated Kinase Gene CaWAKL20 From Pepper Negatively Modulates Plant Thermotolerance by Reducing the Expression of ABA-Responsive Genes

Heat stress has become a major threat to crop production due to global warming; however, the mechanisms underlying plant high-temperature sensing are not well known. In plants, the membrane-anchored receptor-like kinases (RLKs) relay environmental signals into the cytoplasm. In a previous study, we isolated a wall-associated RLK-like (WAKL) gene CaWAKL20 from pepper (Capsicum annuum L.). Here, the amino acid sequence of CaWAKL20 was characterized and found to consist of conserved domains of WAK/WAKL family, including an extracellular region containing a GUB-WAK binding domain and a degenerated EGF2-like domain; a transmembrane region; and an intercellular region with an STKc catalytic domain. Moreover, CaWAKL20 transcription was inhibited by heat stress, whereas it was induced by both ABA and H2O2 treatments. Silencing of CaWAKL20 enhanced pepper thermotolerance, while overexpression decreased Arabidopsis thermotolerance. Additionally, Arabidopsis lines overexpressing CaWAKL20 showed less sensitivity to ABA during seed germination and root growth. Finally, the survival rate of Arabidopsis seedlings under heat stress treatment was enhanced by ABA pre-treatment, while it was compromised by the overexpression of CaWAKL20. Furthermore, the heat-induced expression of several ABA-responsive genes and some key regulator genes for thermotolerance was decreased in Arabidopsis CaWAKL20-overexpression lines. These results suggest that CaWAKL20 negatively modulates plant thermotolerance by reducing the expression of ABA-responsive genes, laying a foundation for further investigation into the functional mechanisms of WAKs/WAKLs in plants undergoing environmental stresses

The expression pattern, subcellular localization and function of three sterol 14α-demethylases in Aspergillus oryzae

Sterol 14α-demethylase catalyzes lanosterol hydroxylation, which is one of the key reactions in the biosynthetic pathway of sterols. There is only one sterol 14α-demethylases gene named Erg11 in Saccharomyces cerevisiae genome. In this study, three sterol 14α-demethylases genes named AoErg11A, AoErg11B and AoErg11C were identified in Aspergillus oryzae genome through bioinformatics analysis. The function of these three genes were studied by yeast complementation, and the expression pattern/subcellular localization of these genes/proteins were detected. The results showed that the three AoErg11s were expressed differently at different growth times and under different abiotic stresses. All of the three proteins were located in endoplasmic reticulum. The AoErg11s could not restore the temperature-sensitive phenotype of S. cerevisiae erg11 mutant. Overexpression of the three AoErg11s affected both growth and sporulation, which may be due to the effect of AoErg11s on ergosterol content. Therefore, this study revealed the functions of three AoErg11s and their effects on the growth and ergosterol biosynthesis of A. oryzae, which may contribute to the further understanding of the ergosterol biosynthesis and regulation mechanism in this important filamentous fungus, A. oryzae

Comprehensive Evaluation of Tea Cultivars Suitable for Matcha Production Using Multivariate Statistical Analysis

Matcha was prepared from 36 tea cultivars grown in the same tea garden according to the shading requirements for fresh leaves to be used for the production of matcha and its 11 quality indexes such as sensory quality, major physicochemical properties and chroma values were analyzed. In order to select tea cultivars suitable for the manufacturing of matcha, a comprehensive evaluation model of matcha quality was established by cluster analysis (CA), principal component analysis (PCA) and multiple linear regression analysis. The CA results showed that the 36 cultivars could be divided into three groups. Matcha from group I had the best quality with green color, fresh and mellow taste, and low phenol/ammonia ratio. Matcha from group II had high phenol/ammonia ratio and strong astringent taste. Matcha from Group III, consisting of etiolated and albino cultivars, had poor color and aroma quality. The PCA results showed that the cumulative contribution rate of the first five principal components was 88.152%. Comprehensive evaluation of matcha using the evaluation function constructed based on the first five principal components showed that the top 10 cultivars were Zhongcha 102, Taicha 12, Zhongcha 108, Fuding Dahao, Meizhan, Fuding Dabai, Fuyun 6, Zi Mudan, Maolv and Yingshuang. The model describing the relationship between sensory quality and physicochemical properties established by multiple linear regression analysis was as follows: y = 3.167|a*| + 46.850 (R2 = 0.710, P < 0.001). The scores of matcha cultivars evaluated by this model were highly consistent with the comprehensive evaluation results based on principal components, indicating that the a* value of dried tea could be used as a representative index to evaluate the quality of matcha. The results of this study can provide a reference for evaluating the suitability of tea cultivars for matcha manufacturing

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

BackgroundComprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.MethodsClinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsThe predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.ConclusionsThe predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy

Evaluation of Suitability for Green Tea Processing of Different Tea Cultivars Based on Multivariate Statistical Analysis

Principal component analysis (PCA) and multiple linear regression analysis (MLRA) were used to analyze and investigate the suitability of green tea for tea tree varieties in different tea cultivars. The fresh leaves of 63 tea cultivars planted in the same garden were processed into roasted green tea by the same method. The sensory and major physicochemical qualities (tea polyphenols, free amino acids, water extracts and chlorophyll) and the color (L*, a* and b*) of green tea were analyzed. To evaluate the suitability of the tested cultivars for green tea processing and its major influential factors, the data obtained were analyzed by PCA and MLRA. The results showed that the coefficient of variation (CV) of chlorophyll content in different cultivars was the highest (26.7%), followed by tea a* value (22.2%), sensory score for tea infusion color (17.1%) and sensory score for tea color (16.2%). A significant correlation was found between the polyphenol content of green tea and its color, tea infusion color and the color of infused tea leaves; between the chlorophyll content, |a*| and b* values of green tea and its color; between the chlorophyll content and |a*| value of green tea and the color of infused tea leaves; and between the |a*| and b* values of green tea and tea infusion color (P < 0.01). The PCA results showed that the contribution rate of the first five principal components (PCs) was 76.895%, and that of the first principal component was 31.918%, mainly pointing to the color quality. According to the evaluation model constructed based on the first five PCs, the top 10 cultivars were Zhongcha 108, Wuniuzao, Pingyangtezao, Mengshan 9, Soubeizhong, Fudingdabai, Jiukeng 16, Fudingdahao, Maolv, and Longjing 43. Using MLRA, the regression function between overall sensory score (Y) and |a*| value (x) was obtained as Y = 68.668 + 5.174x (R2 = 0.313) (P < 0.001). The top 10 varieties determined from this equation were highly consistent with the results of PCA, indicating that a* value is an important indicator for the evaluation of the suitability of tea cultivars for green tea processing