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. Lac1 contains a hydrophilic reaction chamber and a hydrophobic tunnel for binding the CoA moiety and C26-acyl chain of C26-CoA, respectively. Lip1 interacts with both the transmembrane region and the last luminal loop of Lac1 to maintain the proper acyl chain binding tunnel. A lateral opening on Lac1 serves as a potential entrance for the sphingoid base substrate. Our findings provide a template for understanding the working mechanism of eukaryotic ceramide synthases and may facilitate the development of therapeutic CerS modulators.

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. Meanwhile, two comprehensive evaluation metrics, i.e., a complex contagion model in hypergraphs, which mimics the group influence during the spreading process and network s-efficiency based on the higher-order distance between nodes, are first proposed to evaluate the effectiveness of our methods. The results show that our methods can filter out nodes that have fast spreading ability and are vital in terms of hypergraph connectivity.

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. Second, via counterfactual analysis against the COVID-19 crisis in Wuhan, 2020, we find that the intervention dilemma becomes even worse if the initial decision time and the decision horizon vary. In the short horizon, both socially and privately optimal interventions agree with each other and require blocking the spread of all COVID-19-related information, leading to a negligible infection ratio 30 days after the initial reporting time. However, if the time horizon is prolonged to 180 days, only the privately optimal intervention requires information blocking, which would induce a catastrophically higher infection ratio than that in the counterfactual world where the socially optimal intervention encourages early-stage information spread. These findings contribute to the literature by revealing the complexity incurred by the coupled infodemic-epidemic dynamics and information heterogeneity to the governmental intervention decision, which also sheds insight into the design of an effective early warning system against the epidemic crisis in the future.

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). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph's s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

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. RESULTS: We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity-to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.

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. The TIG-process, as a node's importance metric, can be used to do the node ranking. As a framework, the TIG-process can be applied to explore the impact of temporal information on the significance of the nodes. The key point of the TIG-process is that nodes' importance relies on the importance of its neighborhood. There are four variables: temporal information gathering depth n, temporal distance matrix D, initial information c, and weighting function f. We observed that the TIG-process can degenerate to classic metrics by a proper combination of these four variables. Furthermore, the fastest arrival distance based TIG-process ( fad-tig) is performed optimally in quantifying nodes' efficiency and nodes' spreading influence. Moreover, for the fad-tig process, we can find an optimal gathering depth n that makes the TIG-process perform optimally when n is small.

Benefit of Early Initiation of Neuraminidase Inhibitor Treatment to Hospitalized Patients With Avian Influenza A(H7N9) Virus.

Background: The significance of early neuraminidase inhibitor (NAI) therapy for treating influenza A(H7N9) is currently unknown. Methods: The duration of viral shedding was monitored by reverse-transcription polymerase chain reaction after patients with confirmed H7N9 infection were admitted to the First Affiliated Hospital, Zhejiang University, during April 2013-April 2017. Indices such as the length of hospitalization and mortality were collected, and the correlation between the time of administration of NAI and the severity of disease was systematically analyzed. Results: One hundred sixty patients with confirmed H7N9 infection were divided into 3 groups according to NAI starting time. Three of 20 (15%) patients for whom NAI was administered within 2 days died compared with 12 of 52 (23.1%) patients who received treatment within 2-5 days and 33 of 88 (37.5%) patients who were treated after 5 days (P < .05). The median durations of viral shedding from NAI therapy initiation was 4.5 days (interquartile range [IQR], 3-9 days) for patients who took antiviral medication within 2 days, which was significantly different from that for patients who took medication within 2-5 days (7.5 days [IQR, 4.25-12.75 days]) or after 5 days (7 days [IQR, 5-10 days]) (P < .05). We found that the duration of viral shedding from NAI therapy was the shortest in spring 2013 (5.5 days) and the longest in winter-spring 2016-2017 (8.5 days) (P < .05), showing a prolonged trend. Conclusions: Early NAI therapy within 2 days of illness shortened the duration of viral shedding and improved survival in patients with H7N9 viral infection.

A model of spreading of sudden events on social networks.

Information spreading has been studied for decades, but its underlying mechanism is still under debate, especially for those ones spreading extremely fast through the Internet. By focusing on the information spreading data of six typical events on Sina Weibo, we surprisingly find that the spreading of modern information shows some new features, i.e., either extremely fast or slow, depending on the individual events. To understand its mechanism, we present a susceptible-accepted-recovered model with both information sensitivity and social reinforcement. Numerical simulations show that the model can reproduce the main spreading patterns of the six typical events. By this model, we further reveal that the spreading can be speeded up by increasing either the strength of information sensitivity or social reinforcement. Depending on the transmission probability and information sensitivity, the final accepted size can change from continuous to discontinuous transition when the strength of the social reinforcement is large. Moreover, an edge-based compartmental theory is presented to explain the numerical results. These findings may be of significance on the control of information spreading in modern society.

Epidemic dynamics on information-driven adaptive networks.

Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice as well as on a real-world network give visual representations about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, a continuous dynamic behavior, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading.

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 (H7N9 and Dengue fever) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.

Incidence of Norovirus-Associated Diarrhea, Shanghai, China, 2012-2013.

We conducted sentinel-based surveillance for norovirus in the Pudong area of Shanghai, China, during 2012-2013, by analyzing 5,324 community surveys, 408,024 medical records, and 771 laboratory-confirmed norovirus infections among 3,877 diarrhea cases. Our analysis indicated an outpatient incidence of 1.5/100 person-years and a community incidence of 8.9/100 person-years for norovirus-associated diarrhea.

Changing Epidemiology of Human Brucellosis, China, 1955-2014.

Brucellosis, a zoonotic disease, was made statutorily notifiable in China in 1955. We analyzed the incidence and spatial-temporal distribution of human brucellosis during 1955-2014 in China using notifiable surveillance data: aggregated data for 1955-2003 and individual case data for 2004-2014. A total of 513,034 brucellosis cases were recorded, of which 99.3% were reported in northern China during 1955-2014, and 69.1% (258, 462/374, 141) occurred during February-July in 1990-2014. Incidence remained high during 1955-1978 (interquartile range 0.42-1.0 cases/100,000 residents), then decreased dramatically in 1979-1994. However, brucellosis has reemerged since 1995 (interquartile range 0.11-0.23 in 1995-2003 and 1.48-2.89 in 2004-2014); the historical high occurred in 2014, and the affected area expanded from northern pastureland provinces to the adjacent grassland and agricultural areas, then to southern coastal and southwestern areas. Control strategies in China should be adjusted to account for these changes by adopting a One Health approach.

Malaria in China, 2011-2015: an observational study.

OBJECTIVE: To ascertain the trends and burden of malaria in China and the costs of interventions for 2011-2015. METHODS: We analysed the spatiotemporal and demographic features of locally transmitted and imported malaria cases using disaggregated surveillance data on malaria from 2011 to 2015, covering the range of dominant malaria vectors in China. The total and mean costs for malaria elimination were calculated by funding sources, interventions and population at risk. FINDINGS: A total of 17 745 malaria cases, including 123 deaths (0.7%), were reported in mainland China, with 15 840 (89%) being imported cases, mainly from Africa and south-east Asia. Almost all counties of China (2855/2858) had achieved their elimination goals by 2015, and locally transmitted cases dropped from 1469 cases in 2011 to 43 cases in 2015, mainly occurring in the regions bordering Myanmar where Anopheles minimus and An. dirus are the dominant vector species. A total of United States dollars (US$) 134.6 million was spent in efforts to eliminate malaria during 2011-2015, with US$ 57.2 million (43%) from the Global Fund to Fight AIDS, Tuberculosis and Malaria and US$ 77.3 million (57%) from the Chinese central government. The mean annual investment (US$ 27 million) per person at risk (574 million) was US$ 0.05 (standard deviation: 0.03). CONCLUSION: The locally transmitted malaria burden in China has decreased. The key challenge is to address the remaining local transmission, as well as to reduce imported cases from Africa and south-east Asia. Continued efforts and appropriate levels of investment are needed in the 2016-2020 period to achieve elimination.

Risk assessment of malaria in land border regions of China in the context of malaria elimination.

BACKGROUND: Cross-border malaria transmission poses a challenge for countries to achieve and maintain malaria elimination. Because of a dramatic increase of cross-border population movement between China and 14 neighbouring countries, the malaria epidemic risk in China's land border regions needs to be understood. METHODS: In this study, individual case-based epidemiological data on malaria in the 136 counties of China with international land borders, from 2011 to 2014, were extracted from the National Infectious Disease Information System. The Plasmodium species, seasonality, spatiotemporal distribution and changing features of imported and indigenous cases were analysed using descriptive spatial and temporal methods. RESULTS: A total of 1948 malaria cases were reported, with 1406 (72.2%) imported cases and 542 (27.8%) indigenous cases. Plasmodium vivax is the predominant species, with 1536 malaria cases occurrence (78.9%), following by Plasmodium falciparum (361 cases, 18.5%), and the others (51 cases, 2.6%). The magnitude and geographic distribution of malaria in land border counties shrunk sharply during the elimination period. Imported malaria cases were with a peak of 546 cases in 2011, decreasing yearly in the following years. The number of counties with imported cases decreased from 28 counties in 2011 to 26 counties in 2014. Indigenous malaria cases presented a markedly decreasing trend, with 319 indigenous cases in 2011 reducing to only 33 indigenous cases in 2014. The number of counties with indigenous cases reduced from 26 counties in 2011 to 10 counties in 2014. However, several bordering counties of Yunnan province adjacent to Myanmar reported indigenous malaria cases in the four consecutive years from 2011 to 2014. CONCLUSIONS: The scale and extent of malaria occurrence in the international land border counties of China decreased dramatically during the elimination period. However, several high-risk counties, especially along the China-Myanmar border, still face a persistent risk of malaria introduction and transmission. The study emphasizes the importance and urgency of cross-border cooperation between neighbouring countries to jointly face malaria threats to elimination goals.

Epidemiologic features of overseas imported malaria in the People's Republic of China.

BACKGROUND: With the dramatic increase in international travel among Chinese people, the risk of malaria importation from malaria-endemic regions threatens the achievement of the malaria elimination goal of China. METHODS: Epidemiological investigations of all imported malaria cases were conducted in nine provinces of China from 1 Nov, 2013 to 30 Oct, 2014. Plasmodium species, spatiotemporal distribution, clinical severity, preventive measures and infection history of the imported malaria cases were analysed using descriptive statistics. RESULTS: A total of 1420 imported malaria cases were recorded during the study period, with P. falciparum (723 cases, 50.9 %) and P. vivax (629 cases, 44.3 %) being the two predominant species. Among them, 81.8 % of cases were in Chinese overseas labourers. The imported cases returned from 41 countries, mainly located in Africa (58.9 %) and Southeast Asia (39.4 %). About a quarter (25.5 %, 279/1094) of counties in the nine study provinces were affected by imported malaria cases. There were 112 cases (7.9 %) developing complicated malaria, including 12 deaths (case fatality rate: 0.8 %). Only 27.8 % of the imported cases had taken prophylactic anti-malarial drugs. While staying abroad, 27.7 % of the cases had experienced two or more episodes of malaria infection. The awareness of clinical manifestations and the capacity for malaria diagnosis were weak in private clinics and primary healthcare facilities. CONCLUSIONS: Imported malaria infections among Chinese labourers, returned from various countries, poses an increasing challenge to the malaria elimination programme in China. The risk of potential re-introduction of malaria into inland malaria-free areas of China should be urgently addressed.

The changing epidemiology of dengue in China, 1990-2014: a descriptive analysis of 25 years of nationwide surveillance data.

BACKGROUND: Dengue has been a notifiable disease in China since 1 September 1989. Cases have been reported each year during the past 25 years of dramatic socio-economic changes in China, and reached a historical high in 2014. This study describes the changing epidemiology of dengue in China during this period, to identify high-risk areas and seasons and to inform dengue prevention and control activities. METHODS: We describe the incidence and distribution of dengue in mainland China using notifiable surveillance data from 1990-2014, which includes classification of imported and indigenous cases from 2005-2014. RESULTS: From 1990-2014, 69,321 cases of dengue including 11 deaths were reported in mainland China, equating to 2.2 cases per one million residents. The highest number was recorded in 2014 (47,056 cases). The number of provinces affected has increased, from a median of three provinces per year (range: 1 to 5 provinces) during 1990-2000 to a median of 14.5 provinces per year (range: 5 to 26 provinces) during 2001-2014. During 2005-2014, imported cases were reported almost every month and 28 provinces (90.3%) were affected. However, 99.8% of indigenous cases occurred between July and November. The regions reporting indigenous cases have expanded from the coastal provinces of southern China and provinces adjacent to Southeast Asia to the central part of China. Dengue virus serotypes 1, 2, 3, and 4 were all detected from 2009-2014. CONCLUSIONS: In China, the area affected by dengue has expanded since 2000 and the incidence has increased steadily since 2012, for both imported and indigenous dengue. Surveillance and control strategies should be adjusted to account for these changes, and further research should explore the drivers of these trends.

Correlation of hepatitis B surface antigen level with response to telbivudine in naive patients with chronic hepatitis B.

AIM: Hepatitis B surface antigen (HBsAg) has become a marker to judge immunological response to hepatitis B therapy. Quantified serum HBsAg levels can predict the response to pegylated interferon and entecavir. In this study, we aimed to explore the correlation of serum HBsAg levels with response to telbivudine (LdT) treatment in patients with chronic hepatitis B (CHB). METHODS: Seventy-three treatment-naive CHB patients were recruited and received LdT monotherapy for 52 weeks and serial HBsAg levels were measured at five protocol time points. According to therapeutic efficacy at week 52, three subgroups of patients were identified, including complete responders (CR), partial responders (PR) and non-responders (NR). RESULTS: After 52 weeks of treatment, CR, PR and NR represented 19 (26%), 33 (45%) and 21 (29%) patients in the sample of 73, respectively. The median values of baseline HBsAg (log10 IU/mL) were 4.05, 4.50 and 5.03 for CR, PR and NR, respectively. There was a distinct decline of HBsAg at week 52; median log10 HBsAg levels (IU/mL) were 3.61 (CR), 3.86 (PR) and 4.31 (NR). Positive correlation between HBsAg levels and HBV DNA loads was observed in the group of NR and early antiviral treatment of PR, but not in CR. CONCLUSION: Initial HBsAg level was closely correlated with the efficacy of LdT. Patients with low HBsAg levels presented satisfactory responses. Therefore, initial level and correlation with HBV DNA of the serum HBsAg levels could predict responsiveness in CHB patients receiving LdT.

Information filtering via biased random walk on coupled social network.

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.

Mechanism of ceramide synthase inhibition by fumonisin B1.

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 B1 (FB1) has been extensively studied as a potent inhibitor of eukaryotic CerSs. In this study, we characterize the inhibition mechanism of FB1 on yeast CerS (yCerS) and determine the structures of both FB1-bound and N-acyl-FB1-bound yCerS. Through our structural analysis and the observation of N-acylation of FB1 by yCerS, we propose a potential ping-pong catalytic mechanism for FB1 N-acylation by yCerS. Lastly, we demonstrate that FB1 exhibits lower binding affinity for yCerS compared to the C26- coenzyme A (CoA) substrate, suggesting that the potent inhibitory effect of FB1 on yCerS may primarily result from the N-acyl-FB1 catalyzed by yCerS, rather than through direct binding of FB1.