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Wine is renowned for its rich content of polyphenols, including resveratrol (Res), known for their health promoting properties. Steamed clam with wine, a popular Mediterranean delicacy that highlights the role of wine as a key ingredient. However, despite these benefits, resveratrol's low bioavailability poses challenges. Could the process of steaming together with clam alter the digestive fate of resveratrol from wine? This study explores the potential of proteoglycan-based nanoparticles from freshwater clam (CFNPs) as a delivery vehicle for enhancing the stability and bioavailability of resveratrol, compared with wine and free Res' solution, aiming to elucidate mechanisms facilitating Res' absorption. The results demonstrated that CFNPs can effectively encapsulate Res with an efficiency over 70%, leading to a uniform particle size of 70.5±0.1 nm (PDI < 0.2). Resveratrol loaded in CFNPs (CFNPs-Res) exhibited an improved antioxidant stability under various conditions, retaining over 90% of antioxidant capacity after three-day storage at room temperature. The controlled-release profile of Res loaded in CFNPs fits both first and Higuchi order kinetics and was more desirable than that of wine and the free Res. Examined by the simulated gastrointestinal digestion, CFNPs-Res showed a significantly higher bioaccessibility and antioxidant retention compared to free Res and the wines. The discovery and use of food derived nanoparticles to carry micronutrients and antioxidants could lead to a shift in functional food design and nutritional advice, advocating much more attention on these entities over solely conventional molecules. [ABSTRACT FROM AUTHOR]

Cloud and snow in remote sensing images typically block the underlying surface information and interfere with the extraction of available information, so detecting cloud and snow becomes a critical problem in remotely sensed image processing. The current methods for detecting clouds and snow are susceptible to interference from complex background, making it difficult to recover cloud edge details and causing missing and false detection phenomena. To address these issues, a cross-dimensional feature attention aggregation network is suggested to realize the segmentation of clouds and snow. To address the problem of interference induced by the similar spectral characteristics of clouds and snow, the context attention aggregation module is added to conflate feature maps of various dimensions and screen the information. Multi-scale strip convolution module (MSSCM) and its improved version MSSCMs are used to extract edge characteristics at different scales and improve the harsh segmentation border. Also, adding deep feature semantic information extraction module to deep features to guide the classification of the model to avoid the interference of complex background. Finally, a 'los beatles' module is used to replace the traditional linear combination in the decoding stage, and the feature information of different granularity is fused and extracted to enhance the model's detection efficiency. In this paper, experiments are carried out on the public datasets: CSWV, HRC _ WHU and L8 _ SPARCS. The MIOU scores on the three datasets are 89.507 % , 91.674 % and 80.722 % , respectively. Comparative experiment findings demonstrate that the network presented in this article can attain the highest detection accuracy and good detection efficiency with low parameters. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Exogenous Adenosine Antagonizes Excitatory Amino Acid Toxicity in Primary Astrocytes" published in the journal Cellular & Molecular Neurobiology. The correction addresses errors in Figure 3A and a typo in the funding section. The corrected version of Figure 3 is provided, showing the effects of gap junction intercellular communication in the OGD/R model. The funding section has also been corrected. The original article has been updated. [Extracted from the article]

Click-through rate prediction is one of the hot topics in the recommendation and advertising systems field. The existing click-through rate prediction models can be classified into feature interactions and behavior sequences. Feature interaction models form new feature combinations by fusing different features. The behavior sequence models capture the user's interests by considering the historical behavior and using an attention mechanism to model the relationship between the target item and the behavior sequence. However, the existing click-through rate prediction techniques either ignore both aspects or only consider one, limiting prediction performance. In order to solve the above problems, we propose a click-through prediction model (CFIBS) that combines feature interaction and behavioral sequence in this paper. Firstly, the Global-Local Gate Module and Post-LN Informer are proposed to extract the user's interests from user behavior sequences to improve training efficiency. In addition, we introduce auxiliary losses to supervise the extraction of user interest features. Secondly, in the interest update layer, we introduce an attention mechanism based gated recurrent unit to enhance the relationship between interest representation and the target item. Finally, for non-temporal features, we propose a Multi-Cross Layer to increase the nonlinear ability of the model. Experiments show that our model can effectively improve the click-through rate prediction accuracy of advertisements. The codes will be available at https://github.com/jihuiqin2/sequence_ctr. [ABSTRACT FROM AUTHOR]