Effects of sleep quality on the risk of various long COVID symptoms among older adults following infection: an observational study.

Background: The long-term sequelae of coronavirus disease 2019 (COVID-19) and its recovery have becoming significant public health concerns. Therefore, this study aimed to enhance the limited evidence regarding the relationship between sleep quality on long COVID among the older population aged 60 years or old.

Methods: Our study included 4,781 COVID-19 patients enrolled from April to May 2023, based on the Peking University Health Cohort.

Population sequencing of cherry accessions unravels the evolution of Cerasus species and the selection of genetic characteristics in edible cherries.

Cerasus is a subgenus of Prunus in the family Rosaceae that is popular owing to its ornamental, edible, and medicinal properties. Understanding the evolution of the Cerasus subgenus and identifying selective trait loci in edible cherries are crucial for the improvement of cherry cultivars to meet producer and consumer demands. In this study, we performed a de novo assembly of a chromosome-scale genome for the sweet cherry (Prunus avium L.

Information-controlled graph convolutional network for multi-view semi-supervised classification.

Graph convolutional networks have achieved remarkable success in the field of multi-view learning. Unfortunately, most graph convolutional network-based multi-view learning methods fail to capture long-range dependencies due to the over-smoothing problem. Many studies have attempted to mitigate this issue by decoupling graph convolution operations.

Covalent Modification of Selenium in Polysaccharide Enhances Immunoregulation Activity via the TLR4-Mediated MAPK/NF-κB Signaling Pathway.

Selenium (Se) is a crucial trace element that demonstrates significant immunomodulatory effects, which are attributed to the variability in its valence states and metabolic pathways. To investigate the Se-related immunoregulatory effects, locust bean gum (LBG), a typical galactomannan, was selenized by employing deep eutectic solvents (DESs) as high-efficiency solvents to obtain Se-covalent modified LBG (SeLBGs) with similar molecular mass and different Se contents (SeLBG, 1049.57 and SeLBG, 4926.

DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.

Background: Achieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions.

Heterogeneous Graph Embedding with Dual Edge Differentiation.

Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range.

Icewine fingerprint involved in latitude under climate change.

Aroma plays a crucial role in determining icewine quality and influencing the profit of growers, but the influence of climate change on icewine sustainable production and the diversity of aroma volatiles in icewine among different regions are unknown. Here, we employed aroma volatiles of 8 Vidal icewines from 2 typical premium production regions (Liaoning in China and Ontario in Canada) and an emerging low-latitude mountainous area (Yunnan in China) to project future diversity and sustainability. We found that Ontario and Yunnan's icewines were characterized with intense apricot or peach and tropical fruit aromas, which was consistent with the excellent grade icewine around the world based on 225 icewine aroma datasets from 5 countries.

Effect of electron beam irradiation treatment on microstructure, physicochemical properties, and bioactive content of areca nut.

Background: Electron beam irradiation treatment is a novel technology that uses low-dose ionizing radiation for the treatment of crops or food to enhance their quality. This study investigated the effects of electron beam irradiation on the microstructure, physicochemical properties, and bioactive compounds of areca nuts.

Results: As the irradiation dose increased, the cellulose, hemicellulose, and lignin content in the areca nuts decreased significantly, whereas the polysaccharide and pectin content increased gradually.

VvD14c-VvMAX2-VvLOB/VvLBD19 module is involved in the strigolactone-mediated regulation of grapevine root architecture.

The D14 protein, an alpha/beta hydrolase, is a key receptor in the strigolactone (SL) signaling pathway. However, the response of VvD14 to SL signals and its role in grapevine root architecture formation remain unclear. This study demonstrated that VvD14c was highly expressed in grapevine tissues and fruit stages than other VvD14 isoforms.

Attention-based stackable graph convolutional network for multi-view learning.

In multi-view learning, graph-based methods like Graph Convolutional Network (GCN) are extensively researched due to effective graph processing capabilities. However, most GCN-based methods often require complex preliminary operations such as sparsification, which may bring additional computation costs and training difficulties. Additionally, as the number of stacking layers increases in most GCN, over-smoothing problem arises, resulting in ineffective utilization of GCN capabilities.

Multi-view heterogeneous graph learning with compressed hypergraph neural networks.

Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to improve compatibility prediction. However, existing graph-based multi-view learning approaches are implemented on homogeneous assumptions and pairwise relationships, which may not adequately capture the complex interactions among real-world instances. In this paper, we design a compressed hypergraph neural network from the perspective of multi-view heterogeneous graph learning.

Metformin Regulates Cardiac Ferroptosis to Reduce Metabolic Syndrome-Induced Cardiac Dysfunction.

High-fat diet-induced metabolic syndrome (MetS) is closely associated with cardiac dysfunction. Recent research studies have indicated a potential association between MetS and ferroptosis. Furthermore, metformin can alleviate MetS-induced cardiac ferroptosis.

Inhomogeneous Diffusion-Induced Network for Multiview Semi-Supervised Classification.

The challenges posed by heterogeneous data in practical applications have made multiview semi-supervised classification a focus of attention for researchers. While several graph-based approaches have been suggested for this task, they tend to use homogeneous feature propagation, leading to even diffusion of node information to their neighbors. However, this diffusion strategy results in nodes acquiring information of equal proportion from dissimilar samples.

Integrated Transcriptomic and Metabolomic Analysis Revealed Abscisic Acid-Induced Regulation of Monoterpene Biosynthesis in Grape Berries.

Monoterpenes are a class of volatile organic compounds that play crucial roles in imparting floral and fruity aromas to Muscat-type grapes. However, our understanding of the regulatory mechanisms underpinning monoterpene biosynthesis in grapes, particularly following abscisic acid (ABA) treatment, remains elusive. This study aimed to explore the impact of exogenous ABA on monoterpene biosynthesis in Ruiduhongyu grape berries by employing Headspace Solid-Phase Micro-Extraction Gas Chromatography-Mass Spectrometry (HS-SPME/GC-MS) analysis and transcriptome sequencing.

Haplotype-resolved genome assembly for tetraploid Chinese cherry () offers insights into fruit firmness.

Chinese cherry () holds considerable importance as one of the primary stone fruit crops in China. However, artificially improving its traits and genetic analysis are challenging due to lack of high-quality genomic resources, which mainly result from difficulties associated with resolving its tetraploid and highly heterozygous genome. Herein, we assembled a chromosome-level, haplotype-resolved genome of the cultivar 'Zhuji Duanbing', comprising 993.

Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis.

Rationale And Objectives: Researchers have delved into noninvasive diagnostic methods of renal fibrosis (RF) in chronic kidney disease, including ultrasound (US), magnetic resonance imaging (MRI), and radiomics. However, the value of these diagnostic methods in the noninvasive diagnosis of RF remains contentious. Consequently, the present study aimed to systematically delineate the accuracy of the noninvasive diagnosis of RF.

Heterogeneous graph convolutional network for multi-view semi-supervised classification.

This paper proposes a novel approach to semantic representation learning from multi-view datasets, distinct from most existing methodologies which typically handle single-view data individually, maintaining a shared semantic link across the multi-view data via a unified optimization process. Notably, even recent advancements, such as Co-GCN, continue to treat each view as an independent graph, subsequently aggregating the respective GCN representations to form output representations, which ignores the complex semantic interactions among heterogeneous data. To address the issue, we design a unified framework to connect multi-view data with heterogeneous graphs.

Chromosome-level genome assembly provides insights into the genetic diversity, evolution, and flower development of Prunus conradinae.

Prunus conradinae, a valuable flowering cherry belonging to the Rosaceae family subgenus Cerasus and endemic to China, has high economic and ornamental value. However, a high-quality P. conradinae genome is unavailable, which hinders our understanding of its genetic relationships and phylogenesis, and ultimately, the possibility of mining of key genes for important traits.

Distantly Supervised Biomedical Relation Extraction Via Negative Learning and Noisy Student Self-Training.

Biomedical relation extraction aims to identify underlying relationships among entities, such as gene associations and drug interactions, within biomedical texts. Despite advancements in relation extraction in general knowledge domains, the scarcity of labeled training data remains a significant challenge in the biomedical field. This paper provides a novel approach for biomedical relation extraction that leverages a noisy student self-training strategy combined with negative learning.

Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change.

Extended growing season lengths under climatic warming suggest increased time for plant growth. However, research has focused on climatic impacts to the timing or duration of distinct phenological events. Comparatively little is known about impacts to the relative time allocation to distinct phenological events, for example, the proportion of time dedicated to leaf growth versus senescence.

Different frequencies of water deficit irrigation treatments improve fruit quality of Zitian seedless grapes under on-tree storage.

In this study, we assessed the impact of varied water deficit irrigation frequencies (T1: 2.5 L/4 days; T2: 5 L/8 days; CK: 5 L/4 days) on Zitian Seedless grapes from veraison to post-ripening. Notably, total soluble solids increased during on-tree storage compared to at maturity, while total anthocyanin content decreased, particularly in CK (60.

UMCGL: Universal Multi-View Consensus Graph Learning With Consistency and Diversity.

Existing multi-view graph learning methods often rely on consistent information for similar nodes within and across views, however they may lack adaptability when facing diversity challenges from noise, varied views, and complex data distributions. These challenges can be mainly categorized into: 1) View-specific diversity within intra-view from noise and incomplete information; 2) Cross-view diversity within inter-view caused by various latent semantics; 3) Cross-group diversity within inter-group due to data distribution differences. To this end, we propose a universal multi-view consensus graph learning framework that considers both original and generative graphs to balance consistency and diversity.

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices.