Correlation between centrality metrics and their application to the opinion model

In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The m order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the B_{n}, the closeness, and the components of x_{1} are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is larger than that between x_{1} and a lower order degree mass. Finally, we investigate the effect of the inflexible antagonists selected based on different centrality metrics in helping one opinion to compete with another in the inflexible antagonists opinion model. Interestingly, we find that selecting the inflexible antagonists based on the leverage, the B_{n}, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the B_{n}, as well as a high centrality similarity between the leverage and the degree.Comment: 20 page

A comparison of pedigree, genetic and genomic estimates of relatedness for informing pairing decisions in two critically endangered birds: Implications for conservation breeding programmes worldwide

Conservation management strategies for many highly threatened species include conservation breeding to prevent extinction and enhance recovery. Pairing decisions for these conservation breeding programmes can be informed by pedigree data to minimize relatedness between individuals in an effort to avoid inbreeding, maximize diversity and maintain evolutionary potential. However, conservation breeding programmes struggle to use this approach when pedigrees are shallow or incomplete. While genetic data (i.e., microsatellites) can be used to estimate relatedness to inform pairing decisions, emerging evidence indicates this approach may lack precision in genetically depauperate species, and more effective estimates will likely be obtained from genomic data (i.e., thousands of genome-wide single nucleotide polymorphisms, or SNPs). Here, we compare relatedness estimates and subsequent pairing decisions using pedigrees, microsatellites and SNPs from whole-genome resequencing approaches in two critically endangered birds endemic to New Zealand: kakī/ black stilt (Himantopus novaezelandiae) and kākāriki karaka/orange-fronted parakeet (Cyanoramphus malherbi). Our findings indicate that SNPs provide more precise estimates of relatedness than microsatellites when assessing empirical parent–offspring and full sibling relationships. Further, our results show that relatedness estimates and subsequent pairing recommendations using PMx are most similar between pedigree and SNP-based approaches. These combined results indicate that in lieu of robust pedigrees, SNPs are an effective tool for informing pairing decisions, which has important implications for many poorly pedigreed conservation breeding programmes worldwide

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

An experimental study to control bed erosion at river confluence

River confluence is a region of merging of two flows of different flow characteristics and sediment loads that result in complex hydrodynamics. The momentum transfer from lateral flow and the flow acceleration causes flow constriction resulting in bed and bank erosion. In this study, circular pile models are suggested as scour reducing structures at river confluences and are studied experimentally. From the scour depth contour maps, it was observed that the bed profiles are remarkably modified with installation of the pile models within the confluence. The scour depth was reduced by 28% with installation of pile models of 12mm diameter at a spacing of 2h. When pile models of 8mm diameter are placed at 2h spacing, the scour depth decreases by 26%. Therefore, the present study shows that pile models are effective for reducing bed scour and possibly bank erosion at confluences