The size and polydispersity of silica nanoparticles under simulated hot spring conditions

The nucleation and growth of silica nanoparticles in supersaturated geothermal waters was simulated using a flow-through geothermal simulator system. The effect of silica concentration ([SiO2]), ionic strength (IS), temperature (T) and organic additives on the size and polydispersity of the forming silica nanoparticles was quantified. A decrease in temperature (58 to 33°C) and the addition of glucose restricted particle growth to sizes <20 nm, while varying [SiO2] or ISdid not affect the size (30-35 nm) and polydispersity (±9 nm) observed at 58°C. Conversely, the addition of xanthan gum induced the development of thin films that enhanced silica aggregation

The metagenomics of biosilicification: causes and effects

In order to determine the links between geochemical parameters controlling the formation of silica sinter in hot springs and their associated microbial diversity, a detailed characterisation of the waters and of in situ-grown silica sinters was combined with molecular phylogenetic analyses of the bacterial communities in Icelandic geothermal environments. At all but one site, the microorganisms clearly affected, and in part controlled, the formation of the macroscopic textures and structures of silica sinter edifices. In addition, the class and genera level phylogenetic diversity and distribution appeared to be closely linked to variations in temperature, salinity and pH regimes

SMARTSNP, an R package for fast multivariate analyses of big genomic data

Abstract Principal component analysis (PCA) is a powerful tool for the analysis of population structure, a genetic property that is essential to understand the evolutionary processes driving biological diversification and (pre)historical colonizations, migrations and extinctions. In the current era of high‐throughput sequencing technologies, population structure can be quantified from scores of genetic markers across hundreds to thousands of genomes. However, these big genomic datasets pose substantial computing and analytical challenges. We present the r package smartsnp for fast and user‐friendly computation of PCA on single‐nucleotide polymorphism (SNP) data. Inspired by the current field‐standard software EIGENSOFT, smartsnp includes appropriate SNP scaling for genetic drift and allows projection of ancient samples onto a modern genetic space while also providing permutation‐based multivariate tests for population differences in genetic diversity (both location and dispersion). Our extensive benchmarks show that smartsnp's PCA is 2–4 times faster than EIGENSOFT's SMARTPCA algorithm across a wide range of sample and SNP sizes. All four smartsnp functions (smart_pca, smart_permanova, smart_permdisp and smart_mva) process datasets with up to 100 samples and 1 million simulated SNPs in less than 30 s and accurately recreate previously published SMARTPCA of ancient‐human and wolf genotypes. The package smartsnp provides fast and robust multivariate ordination and hypothesis testing for big genomic data that is also suitable for ancient and low‐coverage modern DNA. The simple implementation should appeal to biological conservation, evolutionary, ecological and (palaeo)genomic researchers, and be useful for phenotype, ancestry and lineage studies

Geobase Information System Impacts on Space Image Formats

As Geobase Information Systems increase in number, size and complexity, the format compatability of satellite remote sensing data becomes increasingly more important. Because of the vast and continually increasing quantity of data available from remote sensing systems the utility of these data is increasingly dependent on the degree to which their formats facilitate, or hinder, their incorporation into Geobase Information Systems. To merge satellite data into a geobase system requires that they both have a compatible geographic referencing system. Greater acceptance of satellite data by the user community will be facilitated if the data are in a form which most readily corresponds to existing geobase data structures. The conference addressed a number of specific topics and made recommendations

Controlled biomineralization of magnetite (Fe₃O₄) by <i>Magnetospirillum gryphiswaldense</i>

Results from a study of the chemical composition and micro-structural characteristics of bacterial magnetosomes extracted from the magnetotactic bacterial strain Magnetospirillum gryphiswaldense are presented here. Using high-resolution transmission electron microscopy combined with selected-area electron diffraction and energy dispersive X-ray microanalysis, biogenic magnetite particles isolated from mature cultures were analysed for variations in crystallinity and particle size, as well as chain character and length. The analysed crystals showed a narrow size range (∼14-67 nm) with an average diameter of 46±6.8 nm, cuboctahedral morphologies and typical Gamma type crystal size distributions. The magnetite particles exhibited a high chemical purity (exclusively Fe3O4) and the majority fall within the single-magnetic-domain range

Krylov subspace methods for linear systems with tensor product structure

The numerical solution of linear systems with certain tensor product structures is considered. Such structures arise, for example, from the finite element discretization of a linear PDE on a d-dimensional hypercube. Linear systems with tensor product structure can be regarded as linear matrix equations for d = 2 and appear to be their most natural extension for d ≥ 2. A standard Krylov subspace method applied to such a linear system suffers from the curse of dimensionality and has a computational cost that grows exponentially with d. The key to breaking the curse is to note that the solution can often be very well approximated by a vector of low tensor rank. We propose and analyze a new class of methods, so-called tensor Krylov subspace methods, which exploit this fact and attain a computational cost that grows linearly with d. Copyright © 2010 Society for Industrial and Applied Mathematics

Sex-specific local life-history adaptation in surface- and cave-dwelling Atlantic mollies (Poecilia mexicana)

Cavefishes have long been used as model organisms showcasing adaptive diversification, but does adaptation to caves also facilitate the evolution of reproductive isolation from surface ancestors? We raised offspring of wild-caught surface- and cave-dwelling ecotypes of the neotropical fish Poecilia mexicana to sexual maturity in a 12-month common garden experiment. Fish were raised under one of two food regimes (high vs. low), and this was crossed with differences in lighting conditions (permanent darkness vs. 12:12 h light:dark cycle) in a 2 × 2 factorial design, allowing us to elucidate potential patterns of local adaptation in life histories. Our results reveal a pattern of sex-specific local life-history adaptation: Surface molly females had the highest fitness in the treatment best resembling their habitat of origin (high food and a light:dark cycle), and suffered from almost complete reproductive failure in darkness, while cave molly females were not similarly affected in any treatment. Males of both ecotypes, on the other hand, showed only weak evidence for local adaptation. Nonetheless, local life-history adaptation in females likely contributes to ecological diversification in this system and other cave animals, further supporting the role of local adaptation due to strong divergent selection as a major force in ecological speciation

Reduction of Energetic Demands through Modification of Body Size and Routine Metabolic Rates in Extremophile Fish

Citation: Passow, C. N., Greenway, R., Arias-Rodriguez, L., Jeyasingh, P. D., & Tobler, M. (2015). Reduction of Energetic Demands through Modification of Body Size and Routine Metabolic Rates in Extremophile Fish. Physiological and Biochemical Zoology, 88(4), 371-383. doi:10.1086/681053Variation in energy availability or maintenance costs in extreme environments can exert selection for efficient energy use, and reductions in organismal energy demand can be achieved in two ways: reducing body mass or metabolic suppression. Whether long-term exposure to extreme environmental conditions drives adaptive shifts in body mass or metabolic rates remains an open question. We studied body size variation and variation in routine metabolic rates in locally adapted populations of extremophile fish (Poecilia mexicana) living in toxic, hydrogen sulfide-rich springs and caves. We quantified size distributions and routine metabolic rates in wild-caught individuals from four habitat types. Compared with ancestral populations in nonsulfidic surface habitats, extremophile populations were characterized by significant reductions in body size. Despite elevated metabolic rates in cave fish, the body size reduction precipitated in significantly reduced energy demands in all extremophile populations. Laboratory experiments on common garden-raised fish indicated that elevated routine metabolic rates in cave fish likely have a genetic basis. The results of this study indicate that adaptation to extreme environments directly impacts energy metabolism, with fish living in cave and sulfide spring environments expending less energy overall during routine metabolism

Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia

Testing the ecological consequences of evolutionary change using elements

Understanding the ecological consequences of evolutionary change is a central challenge in contemporary biology. We propose a framework based on the ~25 elements represented in biology, which can serve as a conduit for a general exploration of poorly understood evolution-to-ecology links. In this framework, known as ecological stoichiometry, the quantity of elements in the inorganic realm is a fundamental environment, while the flow of elements from the abiotic to the biotic realm is due to the action of genomes, with the unused elements excreted back into the inorganic realm affecting ecological processes at higher levels of organization. Ecological stoichiometry purposefully assumes distinct elemental composition of species, enabling powerful predictions about the ecological functions of species. However, this assumption results in a simplified view of the evolutionary mechanisms underlying diversification in the elemental composition of species. Recent research indicates substantial intraspecific variation in elemental composition and associated ecological functions such as nutrient excretion. We posit that attention to intraspecific variation in elemental composition will facilitate a synthesis of stoichiometric information in light of population genetics theory for a rigorous exploration of the ecological consequences of evolutionary change.Peer reviewedZoolog