Logarithmically Slow Expansion of Hot Bubbles in Gases

We report logarithmically slow expansion of hot bubbles in gases in the process of cooling. A model problem first solved, when the temperature has compact support. Then temperature profile decaying exponentially at large distances is considered. The periphery of the bubble is shown to remain essentially static ("glassy") in the process of cooling until it is taken over by a logarithmically slowly expanding "core". An analytical solution to the problem is obtained by matched asymptotic expansion. This problem gives an example of how logarithmic corrections enter dynamic scaling.Comment: 4 pages, 1 figur

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Construction and comparison of three reference‐quality genome assemblies for soybean

We report reference‐quality genome assemblies and annotations for two accessions of soybean (Glycine max) and for one accession of Glycine soja, the closest wild relative of G. max. The G. max assemblies provided are for widely used US cultivars: the northern line Williams 82 (Wm82) and the southern line Lee. The Wm82 assembly improves the prior published assembly, and the Lee and G. soja assemblies are new for these accessions. Comparisons among the three accessions show generally high structural conservation, but nucleotide difference of 1.7 single‐nucleotide polymorphisms (snps) per kb between Wm82 and Lee, and 4.7 snps per kb between these lines and G. soja. snp distributions and comparisons with genotypes of the Lee and Wm82 parents highlight patterns of introgression and haplotype structure. Comparisons against the US germplasm collection show placement of the sequenced accessions relative to global soybean diversity. Analysis of a pan‐gene collection shows generally high conservation, with variation occurring primarily in genomically clustered gene families. We found approximately 40–42 inversions per chromosome between either Lee or Wm82v4 and G. soja, and approximately 32 inversions per chromosome between Wm82 and Lee. We also investigated five domestication loci. For each locus, we found two different alleles with functional differences between G. soja and the two domesticated accessions. The genome assemblies for multiple cultivated accessions and for the closest wild ancestor of soybean provides a valuable set of resources for identifying causal variants that underlie traits for the domestication and improvement of soybean, serving as a basis for future research and crop improvement efforts for this important crop species

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives