Environmental and economic factors have stimulated research in the area of bioenergy crops. While many plants have been identified as potential energy crops, one species in particular, Miscanthus x giganteus, appears to have the most promise. As researchers attempt to exploit and improve M. x giganteus, genome information is critical. In this study, the genome size of M. x giganteus and its two progenitor species were examined by flow cytometry and stomatal cell analyses. M. x giganteus was found to have genome size of 7.0 pg while Miscanthus sinensis and Miscanthus sacchariflorus were observed to have genome sizes of 5.5 and 4.5 pg respectively with stomatal size correlating with genome size. Upon computing the two tetraploid×diploid hybrids theoretical genome sizes, the data presented in this paper supports the hypothesis of the union of a 2x M. sacchariflorus and a 1x M. sinensis gamete for the formation of the allotriploid, M. x giganteus. Such genomic information provides basic knowledge that is important in M. x giganteus plant improvement.

Abstract. Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to compute similarity between nodes in different networks and an alignment strategy (AS) to find high-scoring alignments with respect to the total NCF over all aligned nodes (or node conservation). But, they then evaluate quality of their alignments via some other measure that is different than the node conservation measure used to guide the alignment construction process. Typically, one measures the amount of conserved edges, but only after alignments are produced. Hence, a recent attempt aimed to directly maximize the amount of conserved edges while constructing alignments, which improved alignment accuracy. Here, we aim to directly maximize both node and edge conservation during alignment construction to further improve alignment accuracy. For this, we design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs each conserved edge so that edges with highly NCF-similar end nodes are favored. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation, and which can be used with any NCF or combination of multiple NCFs. Using WAVE on top of established state-of-the-art NCFs leads to superior alignments compared to the existing methods that optimize only node conservation or only edge conservation or that treat each conserved edge the same. And while we evaluate WAVE in the computational biology domain, it is easily applicable in any domain.

Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches’ biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.

Breeding to improve biomass production of switchgrass (Panicum virgatum L.) and big bluestem (Andropogon gerardii Vitman) for conversion to bioenergy began in 1992. The purpose of this study was (i) to develop a platform for uniform regional testing of cultivars and experimental populations for these species, and (ii) to estimate the gains made by breeding during 1992 to 2012. A total of 25 switchgrass populations and 16 big bluestem populations were planted in uniform regional trials at 13 locations in 2012 and 2014. The reference region was USDA Hardiness Zones 3 through 6 in the humid temperate United States. Significant progress toward increased biomass yield was made in big bluestem and within upland‐ecotype populations, lowland‐ecotype populations, and hybrid‐derived populations of switchgrass. Four mechanisms of increasing biomass yield were documented: (i) increased biomass yield per se, (ii) later flowering to extend the growing season, (iii) combined later flowering from the lowland ecotype with survivorship of the upland ecotype in hybrid‐derived populations, and (iv) increased survivorship of late‐flowering lowland populations in hardiness zones that represent an expansion of their natural adaption zone. Switchgrass exhibited all four mechanisms in one or more improved populations, whereas improved populations of big bluestem were likely influenced by two of the four mechanisms. The uniform testing program was successful at documenting increases in biomass yield, identifying the mechanisms for increased yield, and determining adaptation characteristics and limitations of improved populations.