Fragility functions for a reinforced concrete structure subjected to earthquake and tsunami in sequence

Many coastal regions lying on subduction zones are likely to experience the catastrophic effects of cascading earthquake and tsunami observed in recent events, e.g., 2011 Tohoku Earthquake and Tsunami. The influence of earthquake on the response of the structure to tsunami is difficult to quantify through damage observations from past events, since they only provide information on the combined effects of both perils. Hence, the use of analytical methodologies is fundamental. This paper investigates the response of a reinforced concrete frame subjected to realistic ground motion and tsunami inundation time histories that have been simulated considering a seismic source representative of the M9 2011 Tohoku earthquake event. The structure is analysed via nonlinear time-history analyses under (a) tsunami inundation only and (b) earthquake ground motion and tsunami inundation in sequence. Comparison of these analyses shows that there is a small impact of the preceding earthquake ground shaking on the tsunami fragility. The fragility curves constructed for the cascading hazards show less than 15% reduction in the median estimate of tsunami capacity compared to the fragility functions for tsunami only. This outcome reflects the fundamentally different response of the structure to the two perils: while the ground motion response of the structure is governed by its strength, ductility and stiffness, the tsunami performance of the structure is dominated by its strength. It is found that the ground shaking influences the tsunami displacement response of the considered structure due to the stiffness degradation induced in the ground motion cyclic response, but this effect decreases with increasing tsunami force

Statistical Inference of Selection and Divergence from a Time-Dependent Poisson Random Field Model

We apply a recently developed time-dependent Poisson random field model to aligned DNA sequences from two related biological species to estimate selection coefficients and divergence time. We use Markov chain Monte Carlo methods to estimate species divergence time and selection coefficients for each locus. The model assumes that the selective effects of non-synonymous mutations are normally distributed across genetic loci but constant within loci, and synonymous mutations are selectively neutral. In contrast with previous models, we do not assume that the individual species are at population equilibrium after divergence. Using a data set of 91 genes in two Drosophila species, D. melanogaster and D. simulans, we estimate the species divergence time (or 1.68 million years, assuming the haploid effective population size years) and a mean selection coefficient per generation . Although the average selection coefficient is positive, the magnitude of the selection is quite small. Results from numerical simulations are also presented as an accuracy check for the time-dependent model

Correcting the Site Frequency Spectrum for Divergence-Based Ascertainment

Comparative genomics based on sequenced referenced genomes is essential to hypothesis generation and testing within population genetics. However, selection of candidate regions for further study on the basis of elevated or depressed divergence between species leads to a divergence-based ascertainment bias in the site frequency spectrum within selected candidate loci. Here, a method to correct this problem is developed that obtains maximum-likelihood estimates of the unascertained allele frequency distribution using numerical optimization. I show how divergence-based ascertainment may mimic the effects of natural selection and offer correction formulae for performing proper estimation into the strength of selection in candidate regions in a maximum-likelihood setting

The scale of population structure in Arabidopsis thaliana

The population structure of an organism reflects its evolutionary history and influences its evolutionary trajectory. It constrains the combination of genetic diversity and reveals patterns of past gene flow. Understanding it is a prerequisite for detecting genomic regions under selection, predicting the effect of population disturbances, or modeling gene flow. This paper examines the detailed global population structure of Arabidopsis thaliana. Using a set of 5,707 plants collected from around the globe and genotyped at 149 SNPs, we show that while A. thaliana as a species self-fertilizes 97% of the time, there is considerable variation among local groups. This level of outcrossing greatly limits observed heterozygosity but is sufficient to generate considerable local haplotypic diversity. We also find that in its native Eurasian range A. thaliana exhibits continuous isolation by distance at every geographic scale without natural breaks corresponding to classical notions of populations. By contrast, in North America, where it exists as an exotic species, A. thaliana exhibits little or no population structure at a continental scale but local isolation by distance that extends hundreds of km. This suggests a pattern for the development of isolation by distance that can establish itself shortly after an organism fills a new habitat range. It also raises questions about the general applicability of many standard population genetics models. Any model based on discrete clusters of interchangeable individuals will be an uneasy fit to organisms like A. thaliana which exhibit continuous isolation by distance on many scales

Ranking insertion, deletion and nonsense mutations based on their effect on genetic information

<p>Abstract</p> <p>Background</p> <p>Genetic variations contribute to normal phenotypic differences as well as diseases, and new sequencing technologies are greatly increasing the capacity to identify these variations. Given the large number of variations now being discovered, computational methods to prioritize the functional importance of genetic variations are of growing interest. Thus far, the focus of computational tools has been mainly on the prediction of the effects of amino acid changing single nucleotide polymorphisms (SNPs) and little attention has been paid to indels or nonsense SNPs that result in premature stop codons.</p> <p>Results</p> <p>We propose computational methods to rank insertion-deletion mutations in the coding as well as non-coding regions and nonsense mutations. We rank these variations by measuring the extent of their effect on biological function, based on the assumption that evolutionary conservation reflects function. Using sequence data from budding yeast and human, we show that variations which that we predict to have larger effects segregate at significantly lower allele frequencies, and occur less frequently than expected by chance, indicating stronger purifying selection. Furthermore, we find that insertions, deletions and premature stop codons associated with disease in the human have significantly larger predicted effects than those not associated with disease. Interestingly, the large-effect mutations associated with disease show a similar distribution of predicted effects to that expected for completely random mutations.</p> <p>Conclusions</p> <p>This demonstrates that the evolutionary conservation context of the sequences that harbour insertions, deletions and nonsense mutations can be used to predict and rank the effects of the mutations.</p

Evolutionary Processes Acting on Candidate cis-Regulatory Regions in Humans Inferred from Patterns of Polymorphism and Divergence

Analysis of polymorphism and divergence in the non-coding portion of the human genome yields crucial information about factors driving the evolution of gene regulation. Candidate cis-regulatory regions spanning more than 15,000 genes in 15 African Americans and 20 European Americans were re-sequenced and aligned to the chimpanzee genome in order to identify potentially functional polymorphism and to characterize and quantify departures from neutral evolution. Distortions of the site frequency spectra suggest a general pattern of selective constraint on conserved non-coding sites in the flanking regions of genes (CNCs). Moreover, there is an excess of fixed differences that cannot be explained by a Gamma model of deleterious fitness effects, suggesting the presence of positive selection on CNCs. Extensions of the McDonald-Kreitman test identified candidate cis-regulatory regions with high probabilities of positive and negative selection near many known human genes, the biological characteristics of which exhibit genome-wide trends that differ from patterns observed in protein-coding regions. Notably, there is a higher probability of positive selection in candidate cis-regulatory regions near genes expressed in the fetal brain, suggesting that a larger portion of adaptive regulatory changes has occurred in genes expressed during brain development. Overall we find that natural selection has played an important role in the evolution of candidate cis-regulatory regions throughout hominid evolution

Temporal and spatial variability in stable isotope ratios of SPM link to local hydrography and longer term SPM averages suggest heavy dependence of mussels on nearshore production

Temporal changes in hydrography affect suspended particulate matter (SPM) composition and distribution in coastal systems, potentially influencing the diets of suspension feeders. Temporal variation in SPM and in the diet of the mussel Perna perna, were investigated using stable isotope analysis. The δ13C and δ15 N ratios of SPM, mussels and macroalgae were determined monthly, with SPM samples collected along a 10 km onshore–offshore transect, over 14 months at Kenton-on-Sea, on the south coast of South Africa. Clear nearshore (0 km) to offshore (10 km) carbon depletion gradients were seen in SPM during all months and extended for 50 km offshore on one occasion. Carbon enrichment of coastal SPM in winter (June–August 2004 and May 2005) indicated temporal changes in the nearshore detrital pool, presumably reflecting changes in macroalgal detritus, linked to local changes in coastal hydrography and algal seasonality. Nitrogen patterns were less clear, with SPM enrichment seen between July and October 2004 from 0 to 10 km. Nearshore SPM demonstrated cyclical patterns in carbon over 24-h periods that correlated closely with tidal cycles and mussel carbon signatures, sampled monthly, demonstrated fluctuations that could not be correlated to seasonal or monthly changes in SPM. Macroalgae showed extreme variability in isotopic signatures, with no discernable patterns. IsoSource mixing models indicated over 50% reliance of mussel tissue on nearshore carbon, highlighting the importance of nearshore SPM in mussel diet. Overall, carbon variation in SPM at both large and small temporal scales can be related to hydrographic processes, but is masked in mussels by long-term isotope integration

Responses of marine benthic microalgae to elevated CO<inf>2</inf>

Increasing anthropogenic CO2 emissions to the atmosphere are causing a rise in pCO2 concentrations in the ocean surface and lowering pH. To predict the effects of these changes, we need to improve our understanding of the responses of marine primary producers since these drive biogeochemical cycles and profoundly affect the structure and function of benthic habitats. The effects of increasing CO2 levels on the colonisation of artificial substrata by microalgal assemblages (periphyton) were examined across a CO2 gradient off the volcanic island of Vulcano (NE Sicily). We show that periphyton communities altered significantly as CO2 concentrations increased. CO2 enrichment caused significant increases in chlorophyll a concentrations and in diatom abundance although we did not detect any changes in cyanobacteria. SEM analysis revealed major shifts in diatom assemblage composition as CO2 levels increased. The responses of benthic microalgae to rising anthropogenic CO2 emissions are likely to have significant ecological ramifications for coastal systems. © 2011 Springer-Verlag

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Do Health and Forensic DNA Databases Increase Racial Disparities?

Peter Chow-White and Troy Duster examine the question of whether the "digital divide" in health and forensic DNA databases is contributing to racial disparities