Many crops display differential geographic phenotypes and sensorial signatures, encapsulated by the concept of terroir. The drivers behind these differences remain elusive, and the potential contribution of microbes has been ignored until recently. Significant genetic differentiation between microbial communities and populations from different geographic locations has been demonstrated, but crucially it has not been shown whether this correlates with differential agricultural phenotypes or not. Using wine as a model system, we utilize the regionally genetically differentiated population of Saccharomyces cerevisiae in New Zealand and objectively demonstrate that these populations differentially affect wine phenotype, which is driven by a complex mix of chemicals. These findings reveal the importance of microbial populations for the regional identity of wine, and potentially extend to other important agricultural commodities. Moreover, this suggests that long-term implementation of methods maintaining differential biodiversity may have tangible economic imperatives as well as being desirable in terms of employing agricultural practices that increase responsible environmental stewardship.

Receptor-activator of NF-kappaB ligand (TNFSF11, also known as RANKL, OPGL, TRANCE and ODF) and its tumour necrosis factor (TNF)-family receptor RANK are essential regulators of bone remodelling, lymph node organogenesis and formation of a lactating mammary gland. RANKL and RANK are also expressed in the central nervous system. However, the functional relevance of RANKL/RANK in the brain was entirely unknown. Here we report that RANKL and RANK have an essential role in the brain. In both mice and rats, central RANKL injections trigger severe fever. Using tissue-specific Nestin-Cre and GFAP-Cre rank(floxed) deleter mice, the function of RANK in the fever response was genetically mapped to astrocytes. Importantly, Nestin-Cre and GFAP-Cre rank(floxed) deleter mice are resistant to lipopolysaccharide-induced fever as well as fever in response to the key inflammatory cytokines IL-1beta and TNFalpha. Mechanistically, RANKL activates brain regions involved in thermoregulation and induces fever via the COX2-PGE(2)/EP3R pathway. Moreover, female Nestin-Cre and GFAP-Cre rank(floxed) mice exhibit increased basal body temperatures, suggesting that RANKL and RANK control thermoregulation during normal female physiology. We also show that two children with RANK mutations exhibit impaired fever during pneumonia. These data identify an entirely novel and unexpected function for the key osteoclast differentiation factors RANKL/RANK in female thermoregulation and the central fever response in inflammation.

Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This quantity is commonly used for comparing different evolutionary models, but its calculation, even for simple models, incurs high computational cost. Another interesting challenge relates to the estimation of the posterior distribution. Often, long Markov chains are required to get sufficient samples to carry out parameter inference, especially for tree distributions. In general, these problems are addressed separately by using different procedures. Nested sampling (NS) is a Bayesian computation algorithm which provides the means to estimate marginal likelihoods together with their uncertainties, and to sample from the posterior distribution at no extra cost. The methods currently used in phylogenetics for marginal likelihood estimation lack in practicality due to their dependence on many tuning parameters and their inability of most implementations to provide a direct way to calculate the uncertainties associated with the estimates, unlike NS. In this paper, we introduce NS to phylogenetics. Its performance is analysed under different scenarios and compared to established methods. We conclude that NS is a competitive and attractive algorithm for phylogenetic inference. An implementation is available as a package for BEAST 2 under the LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.

Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization.Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator–prey interactions between the species in a community to define viability constraints.Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure.We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user-friendly software at http://www.cibiv.at/software/pda.

The "phylogenetic diversity" (PD) measure of biodiversity is evaluated using a phylogenetic tree, usually inferred from morphological or molecular data. Consequently, it is vulnerable to errors in that tree, including those resulting from sampling error, model misspecification, or conflicting signals. To improve the robustness of PD, we can evaluate the measure using either a collection (or distribution) of trees or a phylogenetic network. Recently, it has been shown that these 2 approaches are equivalent but that the problem of maximizing PD in the general concept is NP-hard. In this study, we provide an efficient dynamic programming algorithm for maximizing PD when splits in the trees or network form a circular split system. We illustrate our method using a case study of game birds ("Galliformes") and discuss the different choices of taxa based on our approach and PD.