Improved disease resistance in dairy cattle: Correlation of health disorders with measures of immune status; and Genetic improvement for disease resistance by identifying sires whose daughters have fewer disease problems

In Iowa, dairy cattle are most commonly raised on land having rolling topography. Because the nitrogen contributions of alfalfa, their typical forage, and manure reduce the need for commercial fertilizer application, dairy cattle make a strong contribution to a more sustainable agricultural system. The high productivity of the dairy cattle, combined with the significant savings in purchased inputs, offers some producers a financially stable, environmentally preferable alternative to more traditional row-cropping approaches

A rapid method for computing the inverse of the gametic covariance matrix between relatives for a marked Quantitative Trait Locus

The inverse of the gametic covariance matrix between relatives, G-1, for a marked quantitative trait locus (QTL) is required in best linear unbiased prediction (BLUP) of breeding values if marker data are available on a QTL. A rapid method for computing the inverse of a gametic relationship matrix for a marked QTL without building G itself is presented. The algorithm is particularly useful due to the approach taken in computing inbreeding coefficients by having to compute only few elements of G. Numerical techniques for determining, storing, and computing the required elements of G and the nonzero elements of the inverse are discussed. We show that the subset of G required for computing the inbreeding coefficients and hence the inverse is a tiny proportion of the whole matrix and can be easily stored in computer memory using sparse matrix storage techniques. We also introduce an algorithm to determine the maximum set of nonzero elements that can be found in G-1 and a strategy to efficiently store and access them. Finally, we demonstrate that the inverse can be efficiently built using the present techniques for very large and inbred populations

Resistance distance, information centrality, node vulnerability and vibrations in complex networks

We discuss three seemingly unrelated quantities that have been introduced in different fields of science for complex networks. The three quantities are the resistance distance, the information centrality and the node displacement. We first prove various relations among them. Then we focus on the node displacement, showing its usefulness as an index of node vulnerability.We argue that the node displacement has a better resolution as a measure of node vulnerability than the degree and the information centrality

Cycle-centrality in complex networks

Networks are versatile representations of the interactions between entities in complex systems. Cycles on such networks represent feedback processes which play a central role in system dynamics. In this work, we introduce a measure of the importance of any individual cycle, as the fraction of the total information flow of the network passing through the cycle. This measure is computationally cheap, numerically well-conditioned, induces a centrality measure on arbitrary subgraphs and reduces to the eigenvector centrality on vertices. We demonstrate that this measure accurately reflects the impact of events on strategic ensembles of economic sectors, notably in the US economy. As a second example, we show that in the protein-interaction network of the plant Arabidopsis thaliana, a model based on cycle-centrality better accounts for pathogen activity than the state-of-art one. This translates into pathogen-targeted-proteins being concentrated in a small number of triads with high cycle-centrality. Algorithms for computing the centrality of cycles and subgraphs are available for download

Network Landscape from a Brownian Particle's Perspective

Given a complex biological or social network, how many clusters should it be decomposed into? We define the distance $d_{i,j}$ from node $i$ to node $j$ as the average number of steps a Brownian particle takes to reach $j$ from $i$. Node $j$ is a global attractor of $i$ if $d_{i,j}\leq d_{i,k}$ for any $k$ of the graph; it is a local attractor of $i$, if $j\in E_i$ (the set of nearest-neighbors of $i$) and $d_{i,j}\leq d_{i,l}$ for any $l\in E_i$. Based on the intuition that each node should have a high probability to be in the same community as its global (local) attractor on the global (local) scale, we present a simple method to uncover a network's community structure. This method is applied to several real networks and some discussion on its possible extensions is made.Comment: 5 pages, 4 color-figures. REVTeX 4 format. To appear in PR

C-reactive protein and the risk of developing type 2 diabetes in Aboriginal Australians

OBJECTIVE: To investigate the association between C-reactive protein (CRP) and the risk of developing diabetes in Aboriginal Australians. RESEARCH DESIGN AND METHODS: High sensitivity CRP levels were measured in 620 Aboriginal participants aged 20-74 years free from diabetes at baseline in a remote community in the Northern Territory of Australia. Participants were followed for a median of 11 years to identify newly diagnosed cases of diabetes. Cox proportional hazards models were used to assess the relationship of CRP levels with the risk of developing diabetes over the follow-up period. RESULTS: A total of 109 participants were newly diagnosed with diabetes. Incident rates were 10.8, 16.6 and 28.8 per 1000 person-years for people in the lower, middle and upper tertile groups of baseline CRP levels, respectively. After adjusting for age, sex, BMI, baseline glucose regulation status, total cholesterol, urine albumin to creatinine ratio, systolic blood pressure, smoking and alcohol drinking, the association between diabetes and CRP remained significant, with a hazard ratio of 1.23 (95% confidence interval (CI) 1.05, 1.45) corresponding to a doubling in CRP values. Similarly, the adjusted hazard ratio for development of diabetes in people in the upper tertile versus the bottom two tertiles of CRP was 1.75 (95% CI 1.19, 2.56). CONCLUSIONS: CRP is independently associated with the development of diabetes in Aboriginal people. Our findings support a role of inflammation in the etiology of diabetes in the high risk population of Aboriginal Australians

Finding and evaluating community structure in networks

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.Comment: 16 pages, 13 figure

Universal Behavior of Load Distribution in Scale-free Networks

We study a problem of data packet transport in scale-free networks whose degree distribution follows a power-law with the exponent $\gamma$. We define load at each vertex as the accumulated total number of data packets passing through that vertex when every pair of vertices send and receive a data packet along the shortest path connecting the pair. It is found that the load distribution follows a power-law with the exponent $\delta \approx 2.2(1)$, insensitive to different values of $\gamma$ in the range, $2 < \gamma \le 3$, and different mean degrees, which is valid for both undirected and directed cases. Thus, we conjecture that the load exponent is a universal quantity to characterize scale-free networks.Comment: 5 pages, 5 figures, revised versio