Forage Management for Extending the Grazing Season

Studies were conducted to determine ways to extend grazing season with forages adequate to meet the needs of grazing animals and to reduce the cost of livestock production. Perennial grasses were swathed, windrowed and left in the field until they were winter-grazed with bred heifers. Corn (Zea mays L.) was stockpiled for winter grazing with lambs and ewes. Windrowed grasses increased in acid detergent fiber (ADF) and neutral detergent fiber (NDF) compared with stored hay, but crude protein content, heifer weight gain and body condition change was similar for both forages. An economic value of $218 ha-1 from initial grazing of corn with lambs, followed by grazing with bred ewes, without supplementation, provided a net income over expenses of$75 ha-1. Leaving windrows of perennial grasses in the field and stockpiling corn for winter grazing have potential for reducing reliance on stored forages

Which smoking cessation interventions work best?

Nicotine replacement therapy (NRT), bupropion, nortriptyline, clonidine, and varenicline are all effective, although insufficient evidence exists to recommend one intervention over another (SOR: A, systematic reviews). Effective nonpharmacologic interventions include brief physician advice and more intensive counseling, such as proactive telephone counseling, group and individual counseling, and use of quit lines (SOR: A, systematic reviews)

Is it safe to vaccinate children against varicella while they're in close contact with a pregnant woman?

All healthy children without evidence of immunity to varicella who are living in a household with a susceptible pregnant woman should be vaccinated (strength of recommendation [SOR]: C, expert opinion). The risk of transmission of vaccine virus to household contacts is very low (SOR: B, observational studies). Transmission is higher, but still rare, among contacts of immunocompromised vaccinees (SOR: B, observational studies). Varicella infection has not been reported in unborn babies of women who had contact with a recently vaccinated person

The Wild Turkey in South Dakota

This bulletin provides an extensive look at the wild turkey in South Dakota

Identifying network communities with a high resolution

Community structure is an important property of complex networks. An automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a quantity called modularity (Q). However, the problem of modularity optimization is NP-hard, and the existing approaches often suffer from prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit, i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm, Qcut, which combines spectral graph partitioning and local search to optimize Q. Using both synthetic and real networks, we show that Qcut can find higher modularities and is more scalable than the existing algorithms. Furthermore, using Qcut as an essential component, we propose a recursive algorithm, HQcut, to solve the resolution limit problem. We show that HQcut can successfully detect communities at a much finer scale and with a higher accuracy than the existing algorithms. Finally, we apply Qcut and HQcut to study a protein-protein interaction network, and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at http://cic.cs.wustl.edu/qcut/supplemental.pd

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution

Finding community structure in networks using the eigenvectors of matrices

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.Comment: 22 pages, 8 figures, minor corrections in this versio

An evolving network model with community structure

Many social and biological networks consist of communities—groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties