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Additional file 2. Top 10 co-cited references related to coronary microvascular dysfunction.

Additional file 1. Top 8 productive authors and co-cited authors related to coronary microvascular dysfunction.

The existing research demonstrates that the link prediction algorithm which based on community similarity has better prediction performance than that of other node similarity-based methods, and it is more suitable for predicting the probability of the missing links between node-pairs with far distance. However, the disadvantage of these community similarity-based methods is the resolution of prediction accuracy is very low, which resulting in the existence probability of the missing links between node-pairs within a community or between a specific pair of communities is the same. In addition, the link prediction algorithms which based on multi-resolution community division can calculate the existence probability of missing links under different resolutions, but the relevance between communities had not taken into account, which makes it difficult to predict the existence probability of target links if the number of interconnections between communities is small. Combining the advantages of these two algorithms, we propose a more realistic link prediction model which based on a novel quasi-local community relevance index under multi-resolution community division. The performance of our algorithms is demonstrated by comparing with other well-known methods on two kinds of networks in different scales. The experiment results indicate that our approaches are very competitive.

Hepatocellular carcinoma (HCC) is a prevalent solid cancer worldwide and sorafenib is a common treatment. Nevertheless, sorafenib resistance is a severe clinical problem. In the present study, we identified that epigenetic regulator, KAT6A, was overexpressed in clinical HCC tissues and sorafenib-resistant HCC samples. The depletion of KAT6A repressed the cell viability and Edu-positive cell numbers of HCC cells. The IC50 value of sorafenib was increased in sorafenib-resistant HCC cells. In addition, the expression of KAT6A was induced in sorafenib-resistant HCC cells. The depletion of KAT6A suppressed the IC50 of sorafenib. Mechanically, YAP was decreased by the depletion of KAT6A. KAT6A was able to enrich in the promoter of YAP. The silencing of KAT6A reduced the enrichment of histone H3 lysine 23 acetylation (H3K23ac) and RNA polymerase II (RNA pol II) on the promoter of YAP in sorafenib-resistant HCC cells. KAT6A inhibitor WM-1119 repressed the cell proliferation of sorafenib-resistant HCC cells, while overexpression of KAT6A or YAP could reverse the effect in the cells. Meanwhile, the treatment of sorafenib inhibited the viability of sorafenib-resistant HCC cells, while the co-treatment of WM-1119 could improve the effect of sorafenib. Collectively, KAT6A was associated with sorafenib resistance and contributes to progression of HCC by targeting YAP. Targeting KAT6A may be served as a promising therapeutic approach for HCC.

Woodlands, characterised by a matrix of trees, shrubs and open interspaces, are important biomes on Earth. Woodlands support a number of important ecosystem functions such as primary production, carbon fixation and nutrient cycling, and provide multiple ecosystem services that are essential for human livelihoods. Woodland structure and function are regulated by both large-scale shifts in climate, and smaller-scale variation in species interactions, resource availability and land management. Predicted changes in climate are expected to increase dryness and intensify management activities (e.g., grazing, plant removal), imposing substantial challenges on the functioning of woodlands and their dependent biota. Exploring how woodlands change across a climatic (aridity) gradient and among different management practices is essential for understanding how they adapt to drier climates and intensified woodland management, and to predict the ecological consequences of increasing aridity on their functions. This thesis examines the response of woodland structure and function to increasing aridity and woody plant removal, and the impact of biotic (e.g., plant traits, competition, grazing) and abiotic (e.g., climate, soil) drivers at both microsite and sub-continental scales, based on meta-analysis and field survey. Chapter 1 provides an overview of woodland structure and function, and their biotic and abiotic driving factors, highlighting important findings on the impact of increasing dryness on woodland structure (e.g., trees, shrubs, and open interspaces) and function (e.g., fertile islands). Chapter 2 examines the allometric response of different Australian woody plant genera to increasing dryness. Chapters 3 and 4 describe variation in biocrust cover and the fertile island effect beneath perennial vegetation across different patch types at the microsite scale, and with increasing aridity at the sub-continental scale. Chapters 5 and 6 synthesise the ecosystem outcomes of removing woody plants and the impact of woody plant traits, climatic regimes, and removal practices on the effectiveness of woody plant removal across the globe. Chapter 7 provides a synopsis of previous chapters, highlighting the adaptation strategies of woodlands to predicted drier climates, suggesting alternative woodland management under changing climates, and providing direction for future work in this field.

Traditional aviation DC contactors have some defects such as poor dynamic characteristics of switches and short electrical life when switching on or breaking off high-power loads. The new silicon carbide device has the advantages of high power density, low on-state resistance and fast switching speed. Aiming at these problems, the design scheme of SiC MOSFET based HVDC hybrid contactor is proposed. When the mechanical contact is on and off, the load is borne by SiC MOSFET triggered by digital/analog control circuit, and the working current is borne by the mechanical contact in steady state, which solves the problems of arc ablation and prolongs the life of contactor effectively. The designed HVDC hybrid contactor has the advantages of no arc, high reliability and long electrical life. It achieves the expected goal of prototype development and lays a solid foundation for the engineering research and application of HVDC hybrid contactor.

The link prediction algorithm which based on node similarity is the research hotspot in recent years. In addition, there are some methods which based on the network community structure information to predict the missing links, however, these studies only concerned about the obvious information between different communities such as direct links. We found that it is hard to predict the missing links if the two communities have little direct connections. In fact, there is similarity between communities such as the similarity between nodes and this similarity is significant for prediction. So, we define a community similarity feature which named community relevance by using not only the obvious information but also the latent information between different communities in this paper. Then a novel algorithm which based on the community relevance and ruler inference is proposed to predict missing links. In this method, we extract the community structure by using the local information of the network first. Next, calculate the relevance of each pair of communities by using the new community relevance indices. Finally, a simple prediction model which based on ruler inference is applied to estimate the probability of the missing links. It is shown that the proposed method has more effective prediction accuracy and the community relevance features improve the predictor with low time complexity, with experiments on benchmark networks and real-world networks in different scales, and compared with other ten sate of the art approaches.

One way to understand the network function and analyze the network structure is to find the communities of the network accurately. Now, there are many works about designing algorithms for community detection. Most community detection algorithms are based on modularity optimization. However, these methods not only have disadvantages in computational complexity, but also have the problem of resolution restriction. Designing a community detection algorithm that is fast and effective remains a challenge in the field. We attempt to solve the community detection problem in a new perspective in this paper, believing that the assumption used to solve the link prediction problem is useful for the problem of community detection. By using the similarity between modules of the network, we propose a new method to extract the community structure in this paper. Our algorithm consists of three steps. First, we initialize a community partition based on the distribution of the node degree; second, we calculate the similarity between different communities, where the similarity is the index to describe the closeness of the different communities. We assume that the much closer the two different communities are, the greater the likelihood of being divided together; finally, merge the pairs of communities which has the highest similarity value as possible as we can and stop when the condition is not satisfied. Because the convergence of our algorithm is very fast in the process of merging, we find that our method has advantages both in the computational complexity and in the accuracy when compared with other six classical algorithms. Moreover, we design a new measure to describe how difficulty the network division is.

In order to propagate information through the social network, how to find a seed set that can affect the maximum number of users is named as influence maximization problem. A lot of works have been done on this problem, mainly including two aspects: establishing a reasonable information diffusion model and putting forward the appropriate seeding strategy. However, there are few models in the existing ones that consider the acceptance probability of candidate seed nodes in social networks. So in this paper, we consider and solve this problem by introducing a more realistic model, which is the proposed Realistic Independent Cascade (RIC) model. Based on the RIC model, many state-of-the-art seeding algorithms perform not so well because there is no mechanism on dealing with the acceptance probability. So based on the RIC model, we propose a new seeding strategy which is called R-greedy. Furthermore, M-greedy algorithm is proposed to reduce the time complexity of R-greedy. Then, D-greedy algorithm which not only increased the performance but also reduced the time complexity of R-greedy is proposed by combining the advantages of R-greedy and M-greedy. Experiments on the real-world networks and synthetic networks demonstrate that the proposed R-greedy, M-greedy and D-greedy algorithms outperforms state-of-the-art algorithms.

The investigation of link prediction in networks is an important issue in many disciplines. The research of prediction algorithms which required short time but high accuracy is still a challenging task. Most of the existing algorithms are based on the topological information of the networks, including the local or global similarity indices. It is found that the hierarchical organization and community structure information may indeed provide insights for link prediction. In this paper, we propose a simple link prediction method, which fully explore the community structure information of the networks. Firstly, the community structure of the networks under different resolutions is extracted. Then, a simple frequency statistical model is applied to calculate how many times that a pair of nodes divided into the same community under different resolutions. Finally, the probability of the missing links is calculated. The performance of our algorithm is demonstrated by comparing with other seven well-known methods on two kinds of networks in different scales. The results indicate that our approach not only has a good performance on the accuracy, but also has a lower time complexity than any other algorithms which are based on hierarchical structure of the network.

9 páginas.- 5 figuras.- 51 referencias.- Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41561-023-01170-x, Soil mosses are among the most widely distributed organisms on land. Experiments and observations suggest that they contribute to terrestrial soil biodiversity and function, yet their ecological contribution to soil has never been assessed globally under natural conditions. Here we conducted the most comprehensive global standardized field study to quantify how soil mosses influence 8 ecosystem services associated with 24 soil biodiversity and functional attributes across wide environmental gradients from all continents. We found that soil mosses are associated with greater carbon sequestration, pool sizes for key nutrients and organic matter decomposition rates but a lower proportion of soil-borne plant pathogens than unvegetated soils. Mosses are especially important for supporting multiple ecosystem services where vascular-plant cover is low. Globally, soil mosses potentially support 6.43 Gt more carbon in the soil layer than do bare soils. The amount of soil carbon associated with mosses is up to six times the annual global carbon emissions from any altered land use globally. The largest positive contribution of mosses to soils occurs under a high cover of mat and turf mosses, in less-productive ecosystems and on sandy and salty soils. Our results highlight the contribution of mosses to soil life and functions and the need to conserve these important organisms to support healthy soils., The study work associated with this paper was funded by a Large Research Grant from the British Ecological Society (no. LRB17\1019; MUSGONET). D.J.E. is supported by the Hermon Slade Foundation. M.D.-B. was supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I), a project from the Spanish Ministry of Science and Innovation for the I + D + i (PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033a) and a project PAIDI 2020 from the Junta de Andalucía (P20_00879). E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT). M.B. is supported by a Ramón y Cajal grant from Spanish Ministry of Science (RYC2021-031797-I). A.d.l.R is supported by the AEI project PID2019-105469RB-C22. L.W. and Jianyong Wang are supported by the Program for Introducing Talents to Universities (B16011) and the Ministry of Education Innovation Team Development Plan (2013-373). The contributions of T.G. and T.U.N. were supported by the Research Program in Forest Biology, Ecology and Technology (P4-0107) and the research projects J4-3098 and J4-4547 of the Slovenian Research Agency. The contribution of P.B.R. was supported by the NSF Biological Integration Institutes grant DBI-2021898. J. Durán and A. Rodríguez acknowledge support from the FCT (2020.03670.CEECIND and SFRH/BDP/108913/2015, respectively), as well as from the MCTES, FSE, UE and the CFE (UIDB/04004/2021) research unit financed by FCT/MCTES through national funds (PIDDAC).