Жидкофазные коллоиды серебра и меди, получаемые электронно-лучевым испарением металлов в вакууме

ЭЛЕКТРОННО-ЛУЧЕВАЯ ТЕХНОЛОГИЯКОЛЛОИДЫНАНОЧАСТИЦЫ МЕДИНАНОЧАСТИЦЫ СЕРЕБРАДИСПЕРСНО-УПРОЧНЕННЫЕ МАТЕРИАЛЫНАНОТЕХНОЛОГИ

Measuring water activity of aviation fuel using a polymer optical fiber Bragg grating

Poly(methyl methacrylate) (PMMA) based polymer optical fiber Bragg gratings have been used for measuring water activity of aviation fuel. Jet A-1 samples with water content ranging from 100% ERH (wet fuel) to 10 ppm (dried fuel), have been conditioned and calibrated for measurement. The PMMA based optical fiber grating exhibits consistent response and a good sensitivity of 59±3pm/ppm (water content in mass). This water activity measurement allows PMMA based optical fiber gratings to detect very tiny amounts of water in fuels that have a low water saturation point, potentially giving early warning of unsafe operation of a fuel system. © 2014 SPIE

Coupled x-ray fluorescence and x-ray absorption spectroscopy for microscale imaging and identification of sulfur species within tissues and skeletons of scleractinian corals

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Analytical Chemistry 90 (2018): 12559–12566, doi:10.1021/acs.analchem.8b02638.Identifying and mapping the wide range of sulfur species within complex matrices presents a challenge for under-standing the distribution of these important biomolecules within environmental and biological systems. Here, we present a coupled micro X-ray fluorescence (μXRF) and X-ray absorption near edge structure (XANES) spectroscopy method for determining the presence of specific sulfur species in coral tissues and skeletons at high spatial resolution. By using multiple energy stacks and principal component analysis of a large spectral database, we were able to more accurately identify sulfur species components and distinguish different species and distributions of sulfur formerly unresolved by previous studies. Specifically, coral tissues were domi-nated by more reduced sulfur species, such as glutathione disulfide, cysteine and sulfoxide, as well as organic sulfate as represented by chondroitin sulfate. Sulfoxide distributions were visually correlated with the presence of zooxanthellae endosymbionts. Coral skeletons were composed primarily of carbonate-associated sulfate (CAS), along with minor contributions from organic sulfate and a separate inorganic sulfate likely in the form of adsorbed sulfate. This coupled XRF-XANES approach allows for a more accurate and informative view of sulfur within biological systems in situ, and holds great promise for pairing with other techniques to allow for a more encompassing understanding of elemental distributions within the environment.We thank Ray Dalio for funding the Micronesian expedition and K. Hughen,This material is based upon work supported by the Na-tional Science Foundation Graduate Research Fellowship under Grant No. 1122374 and a Ford Foundation Dissertation Fellowship for Gabriela Farfan

Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States

Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R0) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley's K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R0 and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches

Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale

Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others

Water content detection in aviation fuel by using PMMA based optical fiber grating

Water in aviation fuel is a destructive contaminant and can cause serious problems that compromise aircraft’s safe operation and reduce its efficiency and lifetime. Online monitoring of water content in aviation fuel would permit the control of water content before it builds up to dangerous level. Optical fibers made of PMMA have water affinity. In a PMMA based optical fiber Bragg grating (POFBG) its refractive index and volume vary with the water content. This feature is used to detect tiny water content in aviation fuel in this work. The sensing mechanism of POFBG is analyzed. POFBG wavelength is found to be the function of both temperature and equilibrium relative humidity (ERH). POFBG response to water content in fuel can be determined by the ERH. The sensor is experimented at different environmental conditions to identify its sensitivity. As a result, a general expression of POFBG response is achieved. Water content in Jet-A1 is measured by using POFBG sensor calibrated with both environmental chamber and coulometric titration. POFBG sensor is finally tested in a simulation fuel tank, demonstrating a better performance than coulometric titration. A sensitivity of POFBG wavelength change to water content of 33 pm/ppm is achieved at room temperature, indicating detectable water content of 0.03 ppm

Spatio-temporal patterns and characteristics of swine shipments in the U.S. based on Interstate Certificates of Veterinary Inspection

Domestic swine production in the United States is a critical economic and food security industry, yet there is currently no large-scale quantitative assessment of swine shipments available to support risk assessments. In this study, we provide a national-level characterization of the swine industry by quantifying the demographic (i.e. age, sex) patterns, spatio-temporal patterns, and the production diversity within swine shipments. We characterize annual networks of swine shipments using a 30% stratified sample of Interstate Certificates of Veterinary Inspection (ICVI), which are required for the interstate movement of agricultural animals. We used ICVIs in 2010 and 2011 from eight states that represent 36% of swine operations and 63% of the U.S. swine industry. Our analyses reflect an integrated and spatially structured industry with high levels of spatial heterogeneity. Most shipments carried young swine for feeding or breeding purposes and carried a median of 330 head (range: 1–6,500). Geographically, most shipments went to and were shipped from Iowa, Minnesota, and Nebraska. This work, therefore, suggests that although the swine industry is variable in terms of its size and type of swine, counties in states historically known for breeding and feeding operations are consistently more central to the shipment network

Spatio-temporal patterns and characteristics of swine shipments in the U.S. based on Interstate Certificates of Veterinary Inspection

Domestic swine production in the United States is a critical economic and food security industry, yet there is currently no large-scale quantitative assessment of swine shipments available to support risk assessments. In this study, we provide a national-level characterization of the swine industry by quantifying the demographic (i.e. age, sex) patterns, spatio-temporal patterns, and the production diversity within swine shipments. We characterize annual networks of swine shipments using a 30% stratified sample of Interstate Certificates of Veterinary Inspection (ICVI), which are required for the interstate movement of agricultural animals. We used ICVIs in 2010 and 2011 from eight states that represent 36% of swine operations and 63% of the U.S. swine industry. Our analyses reflect an integrated and spatially structured industry with high levels of spatial heterogeneity. Most shipments carried young swine for feeding or breeding purposes and carried a median of 330 head (range: 1–6,500). Geographically, most shipments went to and were shipped from Iowa, Minnesota, and Nebraska. This work, therefore, suggests that although the swine industry is variable in terms of its size and type of swine, counties in states historically known for breeding and feeding operations are consistently more central to the shipment network

Heritability of dispersal-related larval traits in the clown anemonefish Amphiprion percula

A major goal of marine ecology is to identify the drivers of variation in larval dispersal. Larval traits are emerging as an important potential source of variation in dispersal outcomes, but little is known about how the evolution of these traits might shape dispersal patterns. Here, we consider the potential for adaptive evolution in two possibly dispersal-related traits by quantifying the heritability of larval size and swimming speed in the clown anemonefish (Amphiprion percula). Using a laboratory population of wild-caught A. percula, we measured the size and swimming speed of larvae from 24 half-sibling families. Phenotypic variance was partitioned into genetic and environmental components using a linear mixed-effects model. Importantly, by including half-siblings in the breeding design, we ensured that our estimates of genetic variance do not include nonheritable effects shared by clutches of full-siblings, which could lead to significant overestimates of heritability. We find unequivocal evidence for the heritability of larval body size (estimated between 0.21 and 0.34) and equivocal evidence for the heritability of swimming speed (between 0.05 and 0.19 depending on the choice of prior). From a methodological perspective, this work demonstrates the importance of evaluating sensitivity to prior distribution in Bayesian analysis. From a biological perspective, it advances our understanding of potential dispersal-related larval traits by quantifying the extent to which they can be inherited and thus have the potential for adaptive evolution.https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.954

Individual-Level Antibody Dynamics Reveal Potential Drivers of Influenza A Seasonality in Wild Pig Populations

Swine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM–SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January– March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data