Hypoxic compound exercise improves cardiac function in Drosophila high fructose diet via KHK.

Overconsumption of fructose has been linked to the development of systemic metabolic and cardiac diseases, yet few studies have focused on the link between cardiac fructose metabolism and the development of heart disease. Low-oxygen complex exercise is considered an effective means of treating and preventing metabolic diseases and improving cardiac function, however, it is unclear, the link between low-oxygen complex exercise and high-fructose-induced heart disease. Therefore, the aim of this study was to investigate the effect of hypoxic complex exercise on heart disease on a high fructose diet.

Information Propagation in Hypergraph-Based Social Networks.

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions.

Fourier phase retrieval using physics-enhanced deep learning.

Fourier phase retrieval (FPR) aims to reconstruct an object image from the magnitude of its Fourier transform. Despite its widespread utility in various fields of engineering and science, the inherent ill-posed nature of the FPR problem poses a significant challenge. Here we propose a learning-based approach that incorporates the physical model of the FPR imaging system with a deep neural network.

A hyper-distance-based method for hypernetwork comparison.

Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks; however, the comparison of the difference between two hypernetworks has received less attention. This paper proposes a hyper-distance (HD)-based method for comparing hypernetworks.

Mechanism of ceramide synthase inhibition by fumonisin B.

Ceramide synthases (CerSs) play crucial roles in sphingolipid metabolism and have emerged as promising drug targets for metabolic diseases, cancers, and antifungal therapy. However, the therapeutic targeting of CerSs has been hindered by a limited understanding of their inhibition mechanisms by small molecules. Fumonisin B (FB) has been extensively studied as a potent inhibitor of eukaryotic CerSs.

Collaborative regulation of yeast SPT-Orm2 complex by phosphorylation and ceramide.

The homeostatic regulation of serine palmitoyltransferase (SPT) activity in yeast involves N-terminal phosphorylation of Orm proteins, while higher eukaryotes lack these phosphorylation sites. Although recent studies have indicated a conserved ceramide-mediated feedback inhibition of the SPT-ORM/ORMDL complex in higher eukaryotes, its conservation and relationship with phosphorylation regulation in yeast remain unclear. Here, we determine the structure of the yeast SPT-Orm2 complex in a dephosphomimetic state and identify an evolutionarily conserved ceramide-sensing site.

Structure and mechanism of a eukaryotic ceramide synthase complex.

Ceramide synthases (CerS) catalyze ceramide formation via N-acylation of a sphingoid base with a fatty acyl-CoA and are attractive drug targets for treating numerous metabolic diseases and cancers. Here, we present the cryo-EM structure of a yeast CerS complex, consisting of a catalytic Lac1 subunit and a regulatory Lip1 subunit, in complex with C26-CoA substrate. The CerS holoenzyme exists as a dimer of Lac1-Lip1 heterodimers.

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy.

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC).

Complexity of Government response to COVID-19 pandemic: a perspective of coupled dynamics on information heterogeneity and epidemic outbreak.

This study aims at modeling the universal failure in preventing the outbreak of COVID-19 via real-world data from the perspective of complexity and network science. Through formalizing information heterogeneity and government intervention in the coupled dynamics of epidemic and infodemic spreading, first, we find that information heterogeneity and its induced variation in human responses significantly increase the complexity of the government intervention decision. The complexity results in a dilemma between the socially optimal intervention that is risky for the government and the privately optimal intervention that is safer for the government but harmful to the social welfare.

Ceramide sensing by human SPT-ORMDL complex for establishing sphingolipid homeostasis.

The ORM/ORMDL family proteins function as regulatory subunits of the serine palmitoyltransferase (SPT) complex, which is the initiating and rate-limiting enzyme in sphingolipid biosynthesis. This complex is tightly regulated by cellular sphingolipid levels, but the sphingolipid sensing mechanism is unknown. Here we show that purified human SPT-ORMDL complexes are inhibited by the central sphingolipid metabolite ceramide.

Mechanism of sphingolipid homeostasis revealed by structural analysis of SPT-ORM1 complex.

The serine palmitoyltransferase (SPT) complex catalyzes the first and rate-limiting step in sphingolipid biosynthesis in all eukaryotes. ORM/ORMDL proteins are negative regulators of SPT that respond to cellular sphingolipid levels. However, the molecular basis underlying ORM/ORMDL-dependent homeostatic regulation of SPT is not well understood.

Vital node identification in hypergraphs via gravity model.

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an appropriate representation for modeling polyadic relations in complex systems. With the recent surge of interest in researching hypergraphs, the centrality problem has attracted much attention due to the challenge of how to utilize higher-order structure for the definition of centrality metrics. In this paper, we propose a new centrality method (HGC) on the basis of the gravity model as well as a semi-local HGC, which can achieve a balance between accuracy and computational complexity.

Quantification of network structural dissimilarities based on network embedding.

Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, which considers the global structural information.

Cryo-EM structures of the human surfactant lipid transporter ABCA3.

The adenosine 5'-triphosphate (ATP)-binding cassette (ABC) transporter ABCA3 plays a critical role in pulmonary surfactant biogenesis. Mutations in human ABCA3 have been recognized as the most frequent causes of inherited surfactant dysfunction disorders. Despite two decades of research, in vitro biochemical and structural studies of ABCA3 are still lacking.

Structural basis of substrate recognition and translocation by human ABCA4.

Human ATP-binding cassette (ABC) subfamily A (ABCA) transporters mediate the transport of various lipid compounds across the membrane. Mutations in human ABCA transporters have been described to cause severe hereditary disorders associated with impaired lipid transport. However, little is known about the mechanistic details of substrate recognition and translocation by ABCA transporters.

Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network.

With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes.

A network-based deep learning methodology for stratification of tumor mutations.

Motivation: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging.

Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases.

Since December 2019, an outbreak of a novel coronavirus pneumonia (WHO named COVID-19) swept across China. In Shanxi Province, the cumulative confirmed cases finally reached 133 since the first confirmed case appeared on January 22, 2020, and most of which were imported cases from Hubei Province. Reasons for this ongoing surge in Shanxi province, both imported and autochthonous infected cases, are currently unclear and demand urgent investigation.

Safflower yellow alleviates osteoarthritis and prevents inflammation by inhibiting PGE2 release and regulating NF-κB/SIRT1/AMPK signaling pathways.

Background: Safflower yellow (SY) is the main active ingredient of safflower, with various pharmacological effects such as anticoagulating, antioxidant, and anti-arthritis effects.

Purpose: To investigate the anti-inflammatory and chondrocyte protecting role of SY, which subsequently leads to the inhibition of cartilage degradation.

Methods: Rat chondrocytes were stimulated with tumor necrosis factor α (TNF-α) with or without SY treatment.

Ursolic acid protects chondrocytes, exhibits anti-inflammatory properties via regulation of the NF-κB/NLRP3 inflammasome pathway and ameliorates osteoarthritis.

Inflammation and poor viability of chondrocytes result in the degradation of cartilage as osteoarthritis (OA) progresses. The purpose of the present study was to investigate whether ursolic acid (UA) can protect chondrocytes and alleviate OA. Following stimulation with tumor necrosis factor-α (TNF-α), 5 μM UA displayed no cytotoxicity and reversed the up-regulation of the inflammatory factors MMP13, IL-1β, IL-6 and PTGS2, and down-regulation of the cartilaginous genes/proteins type II collagen and Aggrecan.

A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links.

As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.

Prevalence of Enteropathogens in Outpatients with Acute Diarrhea from Urban and Rural Areas, Southeast China, 2010-2014.

Acute diarrhea is an important public health issue. Here, we focused on the differences of enteropathogens in acute diarrhea between urban and rural areas in southeast China. Laboratory- and sentinel-based surveillance of acute diarrhea (≥ 3 loose or liquid stools/24 hours) was conducted at 16 hospitals.

Temporal information gathering process for node ranking in time-varying networks.

Many systems are dynamic and time-varying in the real world. Discovering the vital nodes in temporal networks is more challenging than that in static networks. In this study, we proposed a temporal information gathering (TIG) process for temporal networks.

Coupling dynamics of epidemic spreading and information diffusion on complex networks.

The interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases ( and ) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes.