Marine diatoms grown in chemostats under silicate or ammonium limitation. III. Cellular chemical composition and morphology of Chaetoceros debilis, Skeletonema costatum , and Thalassiosira gravida

Three marine diatoms, Skeletonema costatum, Chaetoceros debilis , and Thalassiosira gravida were grown under no limitation and ammonium or silicate limitation or starvation. Changes in cell morphology were documented with photomicrographs of ammonium and silicate-limited and non-limited cells, and correlated with observed changes in chemical composition. Cultures grown under silicate starvation or limitation showed an increase in particulate carbon, nitrogen and phosporus and chlorophyll a per unit cell volume compared to non-limited cells; particulate silica per cell volume decreased. Si-starved cells were different from Si-limited cells in that the former contained more particulate carbon and silica per cell volume. The most sensitive indicator of silicate limitation or starvation was the ratio C:Si, being 3 to 5 times higher than the values for non-limited cells. The ratios Si:chlorophyll a and S:P were lower and N:Si was higher than non-limited cells by a factor of 2 to 3. The other ratios, C:N, C:P, C:chlorophyll a , N:chlorophyll a , P:chlorophyll a and N:P were considered not to be sensitive indicators of silicate limitation or starvation. Chlorophyll a , and particulate nitrogen per unit cell volume decreased under ammonium limitation and starvation. NH 4 -starved cells contained more chlorophyll a , carbon, nitrogen, silica, and phosphorus per cell volume than NH 4 -limited cells. N:Si was the most sensitive ratio to ammonium limitation or starvation, being 2 to 3 times lower than non-limited cells. Si:chlorophyll a , P:chlorophyll a and N:P were less sensitive, while the ratios C:N, C:chlorophyll a , N:chlorophyll a , C:Si, C:P and Si:P were the least sensitive. Limited cells had less of the limiting nutrient per unit cell volume than starved cells and more of the non-limiting nutrients (i.e., silica and phosphorus for NH 4 -limited cells). This suggests that nutrient-limited cells rather than nutrient-starved cells should be used along with non-limited cells to measure the full range of potential change in cellular chemical composition for one species under nutrient limitation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46631/1/227_2004_Article_BF00392568.pd

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

Galvanic interactions during the dissolution of gold in cyanide and thiourea solutions

Galvanic interactions between gold and associated substances are amongst the many factors that could influence the rate of dissolution of gold in a cyanide or an acidic thiourea solution. Some of these galvanic influences were investigated here by short-circuiting two rotating disc electrodes either in the same container or in two separate containers linked by a salt bridge. In a cyanide solution it was established that the largest decrease in the rate of gold dissolution was obtained when gold was in contact with copper, chalcopyrite and pyrite in the same container. On the other hand, in an acidic thiourea solution, chalcopyrite and pyrite increased the rate of dissolution when both electrodes were in the same container. This was ascribed to sulphur species in solution that inhibited the degradation of thiourea to formamidine disulphide, one of the by-products of the dissolution of gold in thiourea. Galena produced a very marked increase in the rate of dissolution of gold in cyanide solutions when both electrodes were in the same container. This could be ascribed to the action of Pb(II) ions on the surface of the gold. However, galena had little or no effect on the rate of dissolution of gold when in the same container in an acidic thiourea medium. © 1990.Articl

On the Manifold Structure of the Space of Brain Images

Abstract. This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain images is proposed. We hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.e. a parametrization of the set of images. The approach builds on recent advances in manifold learning that allow to uncover nonlinear trends in data. We combine this manifold learning with distance measures between images that capture shape, in order to learn the underlying structure of a database of brain images. The proposed method is generative. New images can be created from the manifold parametrization and existing images can be projected onto the manifold. By measuring projection distance of a held out set of brain images we evaluate the fit of the proposed manifold model to the data and we can compute statistical properties of the data using this manifold structure. We demonstrate this technology on a database of 436 MR brain images.