Effect of cadmium on the defense response of Pacific oyster Crassostrea gigas to Listonella anguillarum challenge

Heavy metal pollution can affect the immune capability of organisms. We evaluated the effect of cadmium (Cd) on the defense responses of the Pacific oyster Crassostrea gigas to Listonella anguillarum challenge. The activities of several important defensive enzymes, including superoxide dismutase (SOD), glutathione peroxidase (GPx), acid phosphatase (ACP), Na+, K+ -ATPase in gills and hepatopancreas, and phenoloxidase-like (POL) enzyme in hemolymph were assayed. In addition, the expression levels of several genes, including heat shock protein 90 (HSP90), metallothionein (MT), and bactericidal/permeability increasing (BPI) protein were quantified by fluorescent quantitative PCR. The enzyme activities of SOD, ACP, POL, and GPx in hepatopancreas, and the expression of HSP90 were down-regulated, whereas GPx activity in the gill, Na+, K+-ATPase activities in both tissues, and MT expression was increased in Cdexposed oysters post L. anguillarum challenge. However, BPI expression was not significantly altered by co-stress of L. anguillarum infection and cadmium exposure. Our results suggest that cadmium exposure alters the oysters' immune responses and energy metabolism following vibrio infection.Heavy metal pollution can affect the immune capability of organisms. We evaluated the effect of cadmium (Cd) on the defense responses of the Pacific oyster Crassostrea gigas to Listonella anguillarum challenge. The activities of several important defensive enzymes, including superoxide dismutase (SOD), glutathione peroxidase (GPx), acid phosphatase (ACP), Na+, K+ -ATPase in gills and hepatopancreas, and phenoloxidase-like (POL) enzyme in hemolymph were assayed. In addition, the expression levels of several genes, including heat shock protein 90 (HSP90), metallothionein (MT), and bactericidal/permeability increasing (BPI) protein were quantified by fluorescent quantitative PCR. The enzyme activities of SOD, ACP, POL, and GPx in hepatopancreas, and the expression of HSP90 were down-regulated, whereas GPx activity in the gill, Na+, K+-ATPase activities in both tissues, and MT expression was increased in Cdexposed oysters post L. anguillarum challenge. However, BPI expression was not significantly altered by co-stress of L. anguillarum infection and cadmium exposure. Our results suggest that cadmium exposure alters the oysters' immune responses and energy metabolism following vibrio infection

Differential metabolic responses of clam Ruditapes philippinarum to Vibrio anguillarum and Vibrio splendidus challenges

Clam Ruditapes philippinarum is one of the important marine aquaculture species in North China. However, pathogens can often cause diseases and lead to massive mortalities and economic losses of clam. In this work, we compared the metabolic responses induced by Vibrio anguillarum and Vibrio splendidus challenges towards hepatopancreas of clam using NMR-based metabolomics. Metabolic responses suggested that both V anguillarum and V splendidus induced disturbances in energy metabolism and osmotic regulation, oxidative and immune stresses with different mechanisms, as indicated by correspondingly differential metabolic biomarkers (e.g., amino acids, ATP, glucose, glycogen, taurine, betaine, choline and hypotaurine) and altered mRNA expression levels of related genes including ATP synthase, ATPase, glutathione peroxidase, heat shock protein 90, defensin and lysozyme. However, V. anguillarum caused more severe oxidative and immune stresses in clam hepatopancreas than V splendidus. Our results indicated that metabolomics could be used to elucidate the biological effects of pathogens to the marine clam R. philippinarum. (C) 2013 Elsevier Ltd. All rights reserved.Clam Ruditapes philippinarum is one of the important marine aquaculture species in North China. However, pathogens can often cause diseases and lead to massive mortalities and economic losses of clam. In this work, we compared the metabolic responses induced by Vibrio anguillarum and Vibrio splendidus challenges towards hepatopancreas of clam using NMR-based metabolomics. Metabolic responses suggested that both V anguillarum and V splendidus induced disturbances in energy metabolism and osmotic regulation, oxidative and immune stresses with different mechanisms, as indicated by correspondingly differential metabolic biomarkers (e.g., amino acids, ATP, glucose, glycogen, taurine, betaine, choline and hypotaurine) and altered mRNA expression levels of related genes including ATP synthase, ATPase, glutathione peroxidase, heat shock protein 90, defensin and lysozyme. However, V. anguillarum caused more severe oxidative and immune stresses in clam hepatopancreas than V splendidus. Our results indicated that metabolomics could be used to elucidate the biological effects of pathogens to the marine clam R. philippinarum. (C) 2013 Elsevier Ltd. All rights reserved

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and system design in DRS. Finally, we discuss appealing research directions and summarize the survey

A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them. Given the availability of various services like online shopping, ride-sharing, food delivery, and professional services on commercial platforms, recommendation systems in these platforms are required to make CTR predictions across multiple domains rather than just a single domain. However, multi-domain click-through rate (MDCTR) prediction remains a challenging task in online recommendation due to the complex mutual influence between domains. Traditional MDCTR models typically encode domains as discrete identifiers, ignoring rich semantic information underlying. Consequently, they can hardly generalize to new domains. Besides, existing models can be easily dominated by some specific domains, which results in significant performance drops in the other domains (i.e. the "seesaw phenomenon"). In this paper, we propose a novel solution Uni-CTR to address the above challenges. Uni-CTR leverages a backbone Large Language Model (LLM) to learn layer-wise semantic representations that capture commonalities between domains. Uni-CTR also uses several domain-specific networks to capture the characteristics of each domain. Note that we design a masked loss strategy so that these domain-specific networks are decoupled from backbone LLM. This allows domain-specific networks to remain unchanged when incorporating new or removing domains, thereby enhancing the flexibility and scalability of the system significantly. Experimental results on three public datasets show that Uni-CTR outperforms the state-of-the-art (SOTA) MDCTR models significantly. Furthermore, Uni-CTR demonstrates remarkable effectiveness in zero-shot prediction. We have applied Uni-CTR in industrial scenarios, confirming its efficiency.Comment: submited to TOI

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems

Diffusion Augmentation for Sequential Recommendation

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific information. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information. The multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that our HierRec outperforms existing models significantly

Usefulness of the Echocardiographic Multi-Parameter Score (EMPS) in Evaluating Left Ventricular Global Heart Function

In this study, we established a new index by combining several echocardiographic parameters to quantify heart failure. We selected 233 consecutive patients who underwent both echocardiographic and plasma B-type natriuretic peptide (BNP) tests within 24 hours after referral for suspected heart failure. The echocardiographic parameters included systolic function, diastolic function, left ventricular chamber remodeling, valvular lesions, systolic pulmonary arterial pressure, and regional wall-motion abnormality. Each factor was scored from 1 to 3 points according to its severity. The total point from these 6 factors is the echocardiographic multi-parameter score (EMPS). The EMPS for 37, 51, 77, and 38 patients from New York Heart Association (NYHA) functional classes I, II, III, and IV, respectively, were 1.24 ± 1.25, 2.98 ± 1.83, 5.96 ± 2.38, and 7.21 ± 1.99, which were significantly different from the mean score of our 30 normal patients (P \u3c0.001). Sensitivity, specificity, and accuracy of an EMPS ≥2 for diagnosis of NYHA classes II to IV were 93%, 83%, and 89%, respectively. The area under the receiver operating characteristic curve was 0.94 (95% confidence interval, 0.92–0.98; P \u3c0.001). There were significant correlations between logBNP and EMPS (r=0.81, P \u3c0.001) or Tei index (r=0.48, P \u3c0.001). In multilinear regression analysis, EMPS, early/late transmitral flow, and peak systolic velocity from tissue Doppler were entered into the model (P \u3c0.001). The standardized regression coefficient (r=0.68) of EMPS was much higher than those of the other 2 factors, which suggests that EMPS is a powerful predictor of BNP levels