Slow crack growth in spinel in water

Magnesium aluminate spinel was tested in a water environment at room temperature to establish its slow crack-growth behavior. Ring specimens with artificial flaws on the outside surface were loaded hydraulically on the inside surface. The time to failure was measured. Various precracking techniques were evaluated and multiple precracks were used to minimize the scatter in the static fatigue tests. Statistical analysis techniques were developed to determine the strength and crack velocities for a single flaw. Slow crack-growth rupture was observed at stress intensities as low as 70 percent of K sub c. A strengthening effect was observed in specimens that had survived long-time static fatigue tests

Vulnerability Due to Nocturnal Tornadoes

This study investigates the human vulnerability caused by tornadoes that occurred between sunset and sunrise from 1880 to 2007. Nocturnal tornadoes are theorized to enhance vulnerability because they are difficult to spot and occur when the public tends to be asleep and in weak building structures. Results illustrate that the nocturnal tornado death rate over the past century has not shared the same pace of decline as those events transpiring during the daytime. From 1950 to 2005, a mere 27.3% of tornadoes were nocturnal, yet 39.3% of tornado fatalities and 42.1% of killer tornado events occurred at night. Tornadoes during the overnight period (local midnight to sunrise) are 2.5 times as likely to kill as those occurring during the daytime hours. It is argued that a core reason why the national tornado fatality toll has not continued to decrease in the past few decades is due to the vulnerability to these nocturnal events. This vulnerability is magnified when other factors such as escalating mobile (or “manufactured”) home stock and an increasing and spreading population are realized. Unlike other structure types that show no robust demarcation between nocturnal and daytime fatalities, nearly 61% of fatalities in mobile homes take place at night revealing this housing stock’s distinct nocturnal tornado vulnerability. Further, spatial analysis illustrates that the American South’s high nocturnal tornado risk is an important factor leading to the region’s high fatality rate. The investigation emphasizes a potential break in the tornado warning dissemination system utilized currently in the United States

Desempenho inicial de sementes de milho tratadas com biorreguladores submetidas a déficit hídrico.

Edição Especial contendo os Anais do XVIII Congresso Brasileiro de Sementes, Florianópolis, set. 2013

Atomistic structural ensemble refinement reveals non-native structure stabilizes a sub-millisecond folding intermediate of CheY

The dynamics of globular proteins can be described in terms of transitions between a folded native state and less-populated intermediates, or excited states, which can play critical roles in both protein folding and function. Excited states are by definition transient species, and therefore are difficult to characterize using current experimental techniques. Here, we report an atomistic model of the excited state ensemble of a stabilized mutant of an extensively studied flavodoxin fold protein CheY. We employed a hybrid simulation and experimental approach in which an aggregate 42 milliseconds of all-atom molecular dynamics were used as an informative prior for the structure of the excited state ensemble. This prior was then refined against small-angle X-ray scattering (SAXS) data employing an established method (EROS). The most striking feature of the resulting excited state ensemble was an unstructured N-terminus stabilized by non-native contacts in a conformation that is topologically simpler than the native state. Using these results, we then predict incisive single molecule FRET experiments as a means of model validation. This study demonstrates the paradigm of uniting simulation and experiment in a statistical model to study the structure of protein excited states and rationally design validating experiments

Intraspecific variability of popcorn S7 lines for phosphorus efficiency in the soil.

The expansion of agriculture, coupled with the need for sustainable cropping, is one of the greatest challenges of the scientific community working on the generation of new cultivars adapted to abiotic stress conditions. The aim of this study was to evaluate the variability of popcorn lines as to responsiveness and efficiency in phosphorus use, as a first step towards the implementation of a breeding program interested in the practice of sustainable agriculture. Twenty-five popcorn lines were evaluated in two locations with different phosphorus levels in the soil, using a randomized block design. The following traits were measured: plant height, ear height, female flowering date, male flowering date, male-female flowering interval, ear diameter, ear length, 100-grain weight, grain yield, popping expansion, and expanded popcorn volume per hectare. A combined analysis of variance and test of means were performed, and the lines were classified as to their phosphorus use efficiency, according to their production performance in the different environments. The genetic diversity between the lines was estimated by Tocher?s and UPGMA clustering methods, using generalized Mahalanobis distance. Lines L59, P7, P2, P3, P4, P8, P10, P9, L66, L70, L69, and P5 were efficient and responsive, whereas lines L75, L80, L61, L77, L63, L65, P1, L54, L53, L88, and L71 were inefficient and nonresponsive. Genetic variability was greater in the environments with low phosphorus in the soil, suggesting that the selection pressure exerted in the stressing environment is a decisive factor to obtain a higher expression of variability

Future changes in isoprene-epoxydiol-derived secondary organic aerosol (IEPOX SOA) under the Shared Socioeconomic Pathways: the importance of physicochemical dependency

Secondary organic aerosol (SOA) is a dominant contributor of fine particulate matter in the atmosphere, but the complexity of SOA formation chemistry hinders the accurate representation of SOA in models. Volatility-based SOA parameterizations have been adopted in many recent chemistry modeling studies and have shown a reasonable performance compared to observations. However, assumptions made in these empirical parameterizations can lead to substantial errors when applied to future climatic conditions as they do not include the mechanistic understanding of processes but are rather fitted to laboratory studies of SOA formation. This is particularly the case for SOA derived from isoprene epoxydiols (IEPOX SOA), for which we have a higher level of understanding of the fundamental processes than is currently parameterized in most models. We predict future SOA concentrations using an explicit mechanism and compare the predictions with the empirical parameterization based on the volatility basis set (VBS) approach. We then use the Community Earth System Model 2 (CESM2.1.0) with detailed isoprene chemistry and reactive uptake processes for the middle and end of the 21st century under four Shared Socioeconomic Pathways (SSPs): SSP1&ndash;2.6, SSP2&ndash;4.5, SSP3&ndash;7.0, and SSP5&ndash;8.5. With the explicit chemical mechanism, we find that IEPOX SOA is predicted to increase on average under all future SSP scenarios but with some variability in the results depending on regions and the scenario chosen. Isoprene emissions are the main driver of IEPOX SOA changes in the future climate, but the IEPOX SOA yield from isoprene emissions also changes by up to 50&thinsp;% depending on the SSP scenario, in particular due to different sulfur emissions. We conduct sensitivity simulations with and without CO2 inhibition of isoprene emissions that is highly uncertain, which results in factor of&nbsp;2 differences in the predicted IEPOX SOA global burden, especially for the high-CO2 scenarios (SSP3&ndash;7.0 and SSP5&ndash;8.5). Aerosol pH also plays a critical role in the IEPOX SOA formation rate, requiring accurate calculation of aerosol pH in chemistry models. On the other hand, isoprene SOA calculated with the VBS scheme predicts a nearly constant SOA yield from isoprene emissions across all SSP scenarios; as a result, it mostly follows isoprene emissions regardless of region and scenario. This is because the VBS scheme does not consider heterogeneous chemistry; in other words, there is no dependency on aerosol properties. The discrepancy between the explicit mechanism and VBS parameterization in this study is likely to occur for other SOA components as well, which may also have dependencies that cannot be captured by VBS parameterizations. This study highlights the need for more explicit chemistry or for parameterizations that capture the dependence on key physicochemical drivers when predicting SOA concentrations for climate studies. </div

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs