scholarly journals A generalized configuration model with degree correlations and its percolation analysis

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Duan-Shin Lee ◽  
Cheng-Shang Chang ◽  
Miao Zhu ◽  
Hung-Chih Li
Keyword(s):  
Correlation Function ◽  
Closed Form ◽  
Computer Simulations ◽  
Degree Distribution ◽  
Configuration Model ◽  
Arbitrary Degree ◽  
Degree Correlation ◽  
Degree Correlations

AbstractIn this paper we present a generalization of the classical configuration model. Like the classical configuration model, the generalized configuration model allows users to specify an arbitrary degree distribution. In our generalized configuration model, we partition the stubs in the configuration model into b blocks of equal sizes and choose a permutation function h for these blocks. In each block, we randomly designate a number proportional to q of stubs as type 1 stubs, where q is a parameter in the range [0,1]. Other stubs are designated as type 2 stubs. To construct a network, randomly select an unconnected stub. Suppose that this stub is in block i. If it is a type 1 stub, connect this stub to a randomly selected unconnected type 1 stub in block h(i). If it is a type 2 stub, connect it to a randomly selected unconnected type 2 stub. We repeat this process until all stubs are connected. Under an assumption, we derive a closed form for the joint degree distribution of two random neighboring vertices in the constructed graph. Based on this joint degree distribution, we show that the Pearson degree correlation function is linear in q for any fixed b. By properly choosing h, we show that our construction algorithm can create assortative networks as well as disassortative networks. We present a percolation analysis of this model. We verify our results by extensive computer simulations.

scholarly journals The effects of connectivity on metapopulation persistence: network symmetry and degree correlations

2015 ◽  
Vol 282 (1806) ◽  
pp. 20150203 ◽  
Author(s):  
Elad Shtilerman ◽  
Lewi Stone
Keyword(s):  
Degree Distribution ◽  
Minor Effect ◽  
Dispersal Pattern ◽  
The Past ◽  
Degree Correlation ◽  
Degree Correlations ◽  
Dispersal Routes ◽  
Metapopulation Persistence ◽  
New Perspective ◽  
A Minor

A spatial metapopulation is a mosaic of interconnected patch populations. The complex routes of colonization between the patches are governed by the metapopulation's dispersal network. Over the past two decades, there has been considerable interest in uncovering the effects of dispersal network topology and its symmetry on metapopulation persistence. While most studies find that the level of symmetry in dispersal pattern enhances persistence, some have reached the conclusion that symmetry has at most a minor effect. In this work, we present a new perspective on the debate. We study properties of the in- and out-degree distribution of patches in the metapopulation which define the number of dispersal routes into and out of a particular patch, respectively. By analysing the spectral radius of the dispersal matrices, we confirm that a higher level of symmetry has only a marginal impact on persistence. We continue to analyse different properties of the in–out degree distribution, namely the ‘in–out degree correlation’ (IODC) and degree heterogeneity, and find their relationship to metapopulation persistence. Our analysis shows that, in contrast to symmetry, the in–out degree distribution and particularly, the IODC are dominant factors controlling persistence.

scholarly journals Rapid type 2 molybdenum(V) electron-paramagnetic resonance signals from xanthine oxidase and the structure of the active centre of the enzyme

1980 ◽  
Vol 185 (3) ◽  
pp. 767-770 ◽  
Author(s):  
J P Malthouse ◽  
S Gutteridge ◽  
R C Bray
Keyword(s):  
Electron Paramagnetic Resonance ◽  
Xanthine Oxidase ◽  
Computer Simulations ◽  
Active Centre ◽  
Geometrical Isomers ◽  
Paramagnetic Resonance ◽  
The Relationship ◽  
Electron Paramagnetic

Rapid type 2 molybdenum(V) e.p.r. signals from reduced functional xanthine oxidase have been further investigated. These signals, which show strong coupling of two protons to molybdenum, have been obtained under a variety of new conditions: specifically either at pH 8.2 in the presence of borate ions, or at pH 10.1-10.7 with or without various other additions. Parameters of the signals were obtained with the help of computer simulations. In at least some of these signals, the coupled protons must be located on the enzyme rather than on bound species. The relationship between type 1 and type 2 Rapid signals is discussed. They may represent geometrical isomers, or alternatively, hydroxyl uptake as a ligand of molybdenum may be involved in formation of type 2 species.

Type 2 Diabetes Overtakes Type 1 in Hispanic Girls

2008 ◽  
Vol 38 (15) ◽  
pp. 18
Author(s):  
SHERRY BOSCHERT
Keyword(s):  
Type 2 Diabetes

Molecular analysis of FVIII gene in severe HA patients of Costa Rica

2010 ◽  
Vol 30 (S 01) ◽  
pp. S150-S152
Author(s):  
G. Jiménez-Cruz ◽  
M. Mendez ◽  
P. Chaverri ◽  
P. Alvarado ◽  
W. Schröder ◽  
...  
Keyword(s):  
Factor Viii ◽  
Coagulation Factor ◽  
Costa Rican ◽  
Factor Viii Gene ◽  
Intron 22 Inversion ◽  
Intron 1 ◽  
Polymerase Chain ◽  
Inversion Analysis

SummaryHaemophilia A (HA) is X-chromosome linked bleeding disorders caused by deficiency of the coagulation factor VIII (FVIII). It is caused by FVIII gene intron 22 inversion (Inv22) in approximately 45% and by intron 1 inversion (Inv1) in 5% of the patients. Both inversions occur as a result of intrachromosomal recombination between homologous regions, in intron 1 or 22 and their extragenic copy located telomeric to the FVIII gene. The aim of this study was to analyze the presence of these mutations in 25 HA Costa Rican families. Patients, methods: We studied 34 HA patients and 110 unrelated obligate members and possible carriers for the presence of Inv22or Inv1. Standard analyses of the factor VIII gene were used incl. Southern blot and long-range polymerase chain reaction for inversion analysis. Results: We found altered Inv22 restriction profiles in 21 patients and 37 carriers. It was found type 1 and type 2 of the inversion of Inv22. During the screening for Inv1 among the HA patient, who were Inv22 negative, we did not found this mutation. Discussion: Our data highlight the importance of the analysis of Inv22 for their association with development of inhibitors in the HA patients and we are continuous searching of Inv1 mutation. This knowledge represents a step for genetic counseling and prevention of the inhibitor development.

Polymorphisms of the Plasminogen Activator Inhibitor- 1 Gene in Type 1 and Type 2 Diabetes, and in Patients with Diabetic Retinopathy

1994 ◽  
Vol 71 (06) ◽  
pp. 731-736 ◽  
Author(s):  
M W Mansfield ◽  
M H Stickland ◽  
A M Carter ◽  
P J Grant
Keyword(s):  
Diabetic Retinopathy ◽  
Plasminogen Activator Inhibitor ◽  
Allelic Frequency ◽  
Repeat Sequence ◽  
Dinucleotide Repeat ◽  
Plasminogen Activator Inhibitor 1 ◽  
The Difference ◽  
Pai 1

SummaryTo identify whether genotype contributes to the difference in PAI-1 levels in type 1 and type 2 diabetic subjects and whether genotype relates to the development of retinopathy, a Hind III restriction fragment length polymorphism and two dinucleotide repeat polymorphisms were studied. In 519 Caucasian diabetic subjects (192 type 1, 327 type 2) and 123 Caucasian control subjects there were no differences in the frequency of the Hind III restriction alleles (type 1 vs type 2 vs control: allele 1 0.397 vs 0.420 vs 0.448; allele 2 0.603 vs 0.580 vs 0.552) nor in the allelic frequency at either dinucleotide repeat sequence. In 86 subjects with no retinopathy at 15 years or more from diagnosis of diabetes and 190 subjects with diabetic retinopathy there was no difference in the frequency of Hind III restriction alleles (retinopathy present vs retinopathy absent: allele 1 0.400 vs 0.467; allele 2 0.600 vs 0.533) nor in the allelic frequencies at either dinucleotide repeat sequence. The results indicate that there is no or minimal influence of the PAI-1 gene on either PAI-1 levels or the development of diabetic retinopathy in patients with diabetes mellitus.

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