Domestication to crop improvement: Genetic resources for sorghum and saccharum (Andropogoneae)

Background Both sorghum (Sorghum bicolor) and sugarcane (Saccharum officinarum) are members of the Andropogoneae tribe in the Poaceae and are each other's closest relatives amongst cultivated plants. Both are relatively recent domesticates and comparatively little of the genetic potential of these taxa and their wild relatives has been captured by breeding programmes to date. This review assesses the genetic gains made by plant breeders since domestication and the progress in the characterization of genetic resources and their utilization in crop improvement for these two related species. Genetic Resources The genome of sorghum has recently been sequenced providing a great boost to our knowledge of the evolution of grass genomes and the wealth of diversity within S. bicolor taxa. Molecular analysis of the Sorghum genus has identified close relatives of S. bicolor with novel traits, endosperm structure and composition that may be used to expand the cultivated gene pool. Mutant populations (including TILLING populations) provide a useful addition to genetic resources for this species. Sugarcane is a complex polyploid with a large and variable number of copies of each gene. The wild relatives of sugarcane represent a reservoir of genetic diversity for use in sugarcane improvement. Techniques for quantitative molecular analysis of gene or allele copy number in this genetically complex crop have been developed. SNP discovery and mapping in sugarcane has been advanced by the development of high-throughput techniques for ecoTILLING in sugarcane. Genetic linkage maps of the sugarcane genome are being improved for use in breeding selection. The improvement of both sorghum and sugarcane will be accelerated by the incorporation of more diverse germplasm into the domesticated gene pools using molecular tools and the improved knowledge of these genomes.

Hot Summers and Heart Failure: Seasonal Variations in Morbidity and Mortality in Australian Heart Failure Patients (1994-2005)

Background There are minimal reports of seasonal variations in chronic heart failure (CHF)-related morbidity and mortality beyond the northern hemisphere. Aims and methods We examined potential seasonal variations with respect to morbidity and all-cause mortality over more than a decade in a cohort of 2961 patients with CHF from a tertiary referral hospital in South Australia subject to mild winters and hot summers. Results Seasonal variation across all event-types was observed. CHF-related morbidity peaked in winter (July) and was lowest in summer (February): 70 (95% CI: 65 to 76) vs. 33 (95% CI: 30 to 37) admissions/1000 at risk (p<0.005). All-cause admissions (113 (95% CI: 107 to 120) vs. 73 (95% CI 68 to 79) admissions/1000 at risk, p<0.001) and concurrent respiratory disease (21% vs. 12%,p<0.001) were consistently higher in winter. 2010 patients died, mortality was highest in August relative to February: 23 (95% CI: 20 to 27) vs. 12 (95% CI: 10 to 15) deaths per 1000 at risk, p<0.001. Those aged 75 years or older were most at risk of seasonal variations in morbidity and mortality. Conclusion Seasonal variations in CHF-related morbidity and mortality occur in the hot climate of South Australia, suggesting that relative (rather than absolute) changes in temperature drive this global phenomenon.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).