16 research outputs found
Modularity measure of networks with overlapping communities
In this paper we introduce a non-fuzzy measure which has been designed to
rank the partitions of a network's nodes into overlapping communities. Such a
measure can be useful for both quantifying clusters detected by various methods
and during finding the overlapping community-structure by optimization methods.
The theoretical problem referring to the separation of overlapping modules is
discussed, and an example for possible applications is given as well
Spectral transitions in networks
We study the level spacing distribution p(s) in the spectrum of random
networks. According to our numerical results, the shape of p(s) in the
Erdos-Renyi (E-R) random graph is determined by the average degree , and
p(s) undergoes a dramatic change when is varied around the critical point
of the percolation transition, =1. When > 1, the p(s) is described by
the statistics of the Gaussian Orthogonal Ensemble (GOE), one of the major
statistical ensembles in Random Matrix Theory, whereas at =1 it follows the
Poisson level spacing distribution. Closely above the critical point, p(s) can
be described in terms of an intermediate distribution between Poisson and the
GOE, the Brody-distribution. Furthermore, below the critical point p(s) can be
given with the help of the regularised Gamma-function. Motivated by these
results, we analyse the behaviour of p(s) in real networks such as the
Internet, a word association network and a protein protein interaction network
as well. When the giant component of these networks is destroyed in a node
deletion process simulating the networks subjected to intentional attack, their
level spacing distribution undergoes a similar transition to that of the E-R
graph.Comment: 11 pages, 5 figure
Quantifying and identifying the overlapping community structure in networks
It has been shown that the communities of complex networks often overlap with
each other. However, there is no effective method to quantify the overlapping
community structure. In this paper, we propose a metric to address this
problem. Instead of assuming that one node can only belong to one community,
our metric assumes that a maximal clique only belongs to one community. In this
way, the overlaps between communities are allowed. To identify the overlapping
community structure, we construct a maximal clique network from the original
network, and prove that the optimization of our metric on the original network
is equivalent to the optimization of Newman's modularity on the maximal clique
network. Thus the overlapping community structure can be identified through
partitioning the maximal clique network using any modularity optimization
method. The effectiveness of our metric is demonstrated by extensive tests on
both the artificial networks and the real world networks with known community
structure. The application to the word association network also reproduces
excellent results.Comment: 9 pages, 7 figure
SARS-CoV-2 Omicron-B.1.1.529 leads to widespread escape from neutralizing antibody responses
On 24th November 2021, the sequence of a new SARS-CoV-2 viral isolate Omicron-B.1.1.529 was announced, containing far more mutations in Spike (S) than previously reported variants. Neutralization titers of Omicron by sera from vaccinees and convalescent subjects infected with early pandemic Alpha, Beta, Gamma, or Delta are substantially reduced, or the sera failed to neutralize. Titers against Omicron are boosted by third vaccine doses and are high in both vaccinated individuals and those infected by Delta. Mutations in Omicron knock out or substantially reduce neutralization by most of the large panel of potent monoclonal antibodies and antibodies under commercial development. Omicron S has structural changes from earlier viruses and uses mutations that confer tight binding to ACE2 to unleash evolution driven by immune escape. This leads to a large number of mutations in the ACE2 binding site and rebalances receptor affinity to that of earlier pandemic viruses
Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.
Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways
The relation between feelings of knowing and the number of neighboring concepts linked to the test cue
Effects of target set sizeon feelings of knowing and cued recall: Implications for the cue effectiveness andpartial-retrieval hypotheses
Mapping the human genetic architecture of COVID-19
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease