101,406 research outputs found
A Direct Comparison of Two Densely Sampled HIV Epidemics: The UK and Switzerland
Phylogenetic clustering approaches can elucidate HIV transmission dynamics. Comparisons across countries are essential for evaluating public health policies. Here, we used a standardised approach to compare the UK HIV Drug Resistance Database and the Swiss HIV Cohort Study while maintaining data-protection requirements. Clusters were identified in subtype A1, B and C pol phylogenies. We generated degree distributions for each risk group and compared distributions between countries using Kolmogorov-Smirnov (KS) tests, Degree Distribution Quantification and Comparison (DDQC) and bootstrapping. We used logistic regression to predict cluster membership based on country, sampling date, risk group, ethnicity and sex. We analysed >8,000 Swiss and >30,000 UK subtype B sequences. At 4.5% genetic distance, the UK was more clustered and MSM and heterosexual degree distributions differed significantly by the KS test. The KS test is sensitive to variation in network scale, and jackknifing the UK MSM dataset to the size of the Swiss dataset removed the difference. Only heterosexuals varied based on the DDQC, due to UK male heterosexuals who clustered exclusively with MSM. Their removal eliminated this difference. In conclusion, the UK and Swiss HIV epidemics have similar underlying dynamics and observed differences in clustering are mainly due to different population sizes
Quantification and Comparison of Degree Distributions in Complex Networks
The degree distribution is an important characteristic of complex networks.
In many applications, quantification of degree distribution in the form of a
fixed-length feature vector is a necessary step. On the other hand, we often
need to compare the degree distribution of two given networks and extract the
amount of similarity between the two distributions. In this paper, we propose a
novel method for quantification of the degree distributions in complex
networks. Based on this quantification method,a new distance function is also
proposed for degree distributions, which captures the differences in the
overall structure of the two given distributions. The proposed method is able
to effectively compare networks even with different scales, and outperforms the
state of the art methods considerably, with respect to the accuracy of the
distance function
Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks
The degree distribution is an important characteristic of complex networks.
In many data analysis applications, the networks should be represented as
fixed-length feature vectors and therefore the feature extraction from the
degree distribution is a necessary step. Moreover, many applications need a
similarity function for comparison of complex networks based on their degree
distributions. Such a similarity measure has many applications including
classification and clustering of network instances, evaluation of network
sampling methods, anomaly detection, and study of epidemic dynamics. The
existing methods are unable to effectively capture the similarity of degree
distributions, particularly when the corresponding networks have different
sizes. Based on our observations about the structure of the degree
distributions in networks over time, we propose a feature extraction and a
similarity function for the degree distributions in complex networks. We
propose to calculate the feature values based on the mean and standard
deviation of the node degrees in order to decrease the effect of the network
size on the extracted features. The proposed method is evaluated using
different artificial and real network datasets, and it outperforms the state of
the art methods with respect to the accuracy of the distance function and the
effectiveness of the extracted features.Comment: arXiv admin note: substantial text overlap with arXiv:1307.362
The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone
Osteocytes and their cell processes reside in a large, interconnected network
of voids pervading the mineralized bone matrix of most vertebrates. This
osteocyte lacuno-canalicular network (OLCN) is believed to play important roles
in mechanosensing, mineral homeostasis, and for the mechanical properties of
bone. While the extracellular matrix structure of bone is extensively studied
on ultrastructural and macroscopic scales, there is a lack of quantitative
knowledge on how the cellular network is organized. Using a recently introduced
imaging and quantification approach, we analyze the OLCN in different bone
types from mouse and sheep that exhibit different degrees of structural
organization not only of the cell network but also of the fibrous matrix
deposited by the cells. We define a number of robust, quantitative measures
that are derived from the theory of complex networks. These measures enable us
to gain insights into how efficient the network is organized with regard to
intercellular transport and communication. Our analysis shows that the cell
network in regularly organized, slow-growing bone tissue from sheep is less
connected, but more efficiently organized compared to irregular and
fast-growing bone tissue from mice. On the level of statistical topological
properties (edges per node, edge length and degree distribution), both network
types are indistinguishable, highlighting that despite pronounced differences
at the tissue level, the topological architecture of the osteocyte canalicular
network at the subcellular level may be independent of species and bone type.
Our results suggest a universal mechanism underlying the self-organization of
individual cells into a large, interconnected network during bone formation and
mineralization
Quantifying the consistency of scientific databases
Science is a social process with far-reaching impact on our modern society.
In the recent years, for the first time we are able to scientifically study the
science itself. This is enabled by massive amounts of data on scientific
publications that is increasingly becoming available. The data is contained in
several databases such as Web of Science or PubMed, maintained by various
public and private entities. Unfortunately, these databases are not always
consistent, which considerably hinders this study. Relying on the powerful
framework of complex networks, we conduct a systematic analysis of the
consistency among six major scientific databases. We found that identifying a
single "best" database is far from easy. Nevertheless, our results indicate
appreciable differences in mutual consistency of different databases, which we
interpret as recipes for future bibliometric studies.Comment: 20 pages, 5 figures, 4 table
The multi-layer network nature of systemic risk and its implications for the costs of financial crises
The inability to see and quantify systemic financial risk comes at an immense
social cost. Systemic risk in the financial system arises to a large extent as
a consequence of the interconnectedness of its institutions, which are linked
through networks of different types of financial contracts, such as credit,
derivatives, foreign exchange and securities. The interplay of the various
exposure networks can be represented as layers in a financial multi-layer
network. In this work we quantify the daily contributions to systemic risk from
four layers of the Mexican banking system from 2007-2013. We show that focusing
on a single layer underestimates the total systemic risk by up to 90%. By
assigning systemic risk levels to individual banks we study the systemic risk
profile of the Mexican banking system on all market layers. This profile can be
used to quantify systemic risk on a national level in terms of nation-wide
expected systemic losses. We show that market-based systemic risk indicators
systematically underestimate expected systemic losses. We find that expected
systemic losses are up to a factor four higher now than before the financial
crisis of 2007-2008. We find that systemic risk contributions of individual
transactions can be up to a factor of thousand higher than the corresponding
credit risk, which creates huge risks for the public. We find an intriguing
non-linear effect whereby the sum of systemic risk of all layers underestimates
the total risk. The method presented here is the first objective data driven
quantification of systemic risk on national scales that reveal its true levels.Comment: 15 pages, 6 figure
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