2,543 research outputs found

    SeaWiFS calibration and validation plan, volume 3

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    The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) will be the first ocean-color satellite since the Nimbus-7 Coastal Zone Color Scanner (CZCS), which ceased operation in 1986. Unlike the CZCS, which was designed as a proof-of-concept experiment, SeaWiFS will provide routine global coverage every 2 days and is designed to provide estimates of photosynthetic concentrations of sufficient accuracy for use in quantitative studies of the ocean's primary productivity and biogeochemistry. A review of the CZCS mission is included that describes that data set's limitations and provides justification for a comprehensive SeaWiFS calibration and validation program. To accomplish the SeaWiFS scientific objectives, the sensor's calibration must be constantly monitored, and robust atmospheric corrections and bio-optical algorithms must be developed. The plan incorporates a multi-faceted approach to sensor calibration using a combination of vicarious (based on in situ observations) and onboard calibration techniques. Because of budget constraints and the limited availability of ship resources, the development of the operational algorithms (atmospheric and bio-optical) will rely heavily on collaborations with the Earth Observing System (EOS), the Moderate Resolution Imaging Spectrometer (MODIS) oceans team, and projects sponsored by other agencies, e.g., the U.S. Navy and the National Science Foundation (NSF). Other elements of the plan include the routine quality control of input ancillary data (e.g., surface wind, surface pressure, ozone concentration, etc.) used in the processing and verification of the level-0 (raw) data to level-1 (calibrated radiances), level-2 (derived products), and level-3 (gridded and averaged derived data) products

    Registration of ‘Goldmine’ Big Bluestem

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    ‘Goldmine’ big bluestem [Andropogon gerardii Vitman.] (Reg. no. CV-14, PI 641702) was released in May 2004 by the USDA-ARS and the Agricultural ResearchDivision, Institute of Agriculture and Natural Resources, University of Nebraska. Goldmine is a perennial warm-season grass cultivar that produces forage with moderately improved in vitro dry matter digestibility (IVDMD), protein concentration, and improved forage yields in some hay management systems. Animal gains from Goldmine were greater than its parent cultivar when utilized by beef cattle in well-managed grazing systems. Goldmine was tested as Kaw C3

    Registration of ‘Bonanza’ Big Bluestem

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    ‘Bonanza’ big bluestem (Andropogon gerardii Vitman.) (Reg. no. CV-13, PI 641701) was released in May 2004 by the USDA-ARS and the Agricultural Research Division, Institute of Agriculture and Natural Resources, University of Nebraska. It is a broadly adapted perennial warm-season grass cultivar that produces forage with high in vitro dry matter digestibility (IVDMD) and improved animal gains in comparison to its parent cultivar when utilized by beef cattle in well-managed grazing systems. Bonanza was tested as Pawnee C3

    The attentional boost effect and perceptual degradation: Assessing the influence of attention on recognition memory

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    Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2022.1024498/full#supplementary-materialFunding Financial support for this study was provided by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant awarded to BM (RGPIN-2019-07021) and open access to the published study was supported by funds from Goethe Universitaet awarded to JO.Researchers have suggested that the recognition memory effects resulting from two separate attentional manipulations—attentional boost and perceptual degradation—may share a common cause; namely a transient up-regulation of attention at the time of encoding that leads to enhanced memory performance at the time of retrieval. Prior research has demonstrated that inducing two similar transient shifts of attention simultaneously produces redundant performance in memory. In the present study, we sought to evaluate the combined influence of the attentional boost and perceptual degradation on recognition memory. If these two effects share a common cause, then we ought to observe a redundancy in memory performance, such that these two factors interact. Yet, across four experiments we fail to observe such a redundancy in recognition memory. We evaluate these results using the limited resource model of attention and speculate on how combining transient shifts of attention may produce redundant memory performance in the one case, but non-redundant performance in the other case.Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant awarded to BM (RGPIN-2019-07021)Open access to the published study was supported by funds from Goethe Universitae

    Electronic Structure of Dinuclear Gold(I) Complexes

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    Cyclic voltammetry (CV) experiments on LL(AuSR∗)2 complexes [LL = diphenylphosphinomethane (dppm), diphenylphosphinopentane (dpppn); R* = p-SC6H4CH3] show anodic sweeps that broaden by about 25 mV on going from the longer (dpppn) to the shorter (dppm) bidentate phosphine ligand. Changing concentrations had no effect on the shape of the waveform. The result suggests a weak intramolecular metal-metal interaction in dppm(AuSR∗)2 that correlates well with rate acceleration occurring in the reaction of dppm(AuSR∗)2 with organic disulfides. Quantum yields for cis-dppee(AuX)2 [dppee = 1,2-bis(diphenylphosphino)ethylene; X = Cl, Br, I] complexes, (disappearance) Φ , are significantly higher in complexes with a softer X ligand, a trend that correlates well with aurophilicity. This result also suggests that electronic perturbation caused by Au(I)-Au(I) interactions is important in explaining the reactivity of some dinuclear gold(I) complexes. The crystal structure for cis-dppee(Aul)2 shows short intramolecular Au(I)-Au(I) interactions of 2.9526 (6) A°, while the structure of trans-dppee(AuI)2 , shows intermolecular Au(I)-Au(I) interactions of 3.2292 (9) A°. The substitution of .As for P results in a ligand, cis-diphenylarsinoethylene (cis-dpaee), that is photochemically active, in contrast to the cis-dppee ligand. The complexes, cis-dpaee(AuX)2, are also photochemically active but with lower quantum yields than the cis-dppee(AuX)2 complexes

    Bioconversion of Pelletized Big Bluestem, Switchgrass, and Low-Diversity Grass Mixtures Into Sugars and Bioethanol

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    Three crops of warm-season grasses are being developed for biomass production on northern rain-fed marginal farmland: big bluestem (BBS), switchgrass (SG), and a low diversity mixture of grasses (LDM). In this study, biomass harvested from established fields were compared for pelletization and subsequent conversion to sugars and ethanol. Each biomass was successfully pelletized to similar bulk densities without adding a binder at a commercial feed operation. Pelletizing increased the bulk density by 407% on average and was equally effective on all three biomass samples (528–554 kg/m3). Chemical analysis of the samples indicated that glucan and xylan contents were slightly reduced during pelletizing (by 23 and 16 g/kg, respectively), as well as theoretical ethanol yields, which are based upon total carbohydrate contents. Pellets and milled straws were pre-treated with either liquid hot-water or low-moisture ammonium hydroxide (LMA) and subsequently hydrolyzed with cellulases. Glucose and total sugar yields were similar for non-pellets and pellets using either pre-treatment; carbohydrates present in pellets were more efficiently recovered compared to non-pellets. LMA pretreated samples were separately hydrolyzed and fermented to ethanol using Scheffersomyces stipitis yeast. Hydrolysis recovered 69.7–76.8% of the glucose and 66.5–73.3% of the xylose across all samples. Glucose yields were 251–279 g/kg, db and were significantly lower for SG as compared to the other biomass samples. Recovered sugars were fermented to ethanol at 77.7–86.7% of theoretical yield. Final ethanol yields (245.9–275.5 L/Mg, db) were similar for all of the grasses and estimated to equate to production levels for BBS, LDM, and SG of 1,952, 2,586, and 2,636 l of ethanol per ha, respectively

    Bioconversion of Pelletized Big Bluestem, Switchgrass, and Low-Diversity Grass Mixtures Into Sugars and Bioethanol

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    Three crops of warm-season grasses are being developed for biomass production on northern rain-fed marginal farmland: big bluestem (BBS), switchgrass (SG), and a low diversity mixture of grasses (LDM). In this study, biomass harvested from established fields were compared for pelletization and subsequent conversion to sugars and ethanol. Each biomass was successfully pelletized to similar bulk densities without adding a binder at a commercial feed operation. Pelletizing increased the bulk density by 407% on average and was equally effective on all three biomass samples (528–554 kg/m3). Chemical analysis of the samples indicated that glucan and xylan contents were slightly reduced during pelletizing (by 23 and 16 g/kg, respectively), as well as theoretical ethanol yields, which are based upon total carbohydrate contents. Pellets and milled straws were pre-treated with either liquid hot-water or low-moisture ammonium hydroxide (LMA) and subsequently hydrolyzed with cellulases. Glucose and total sugar yields were similar for non-pellets and pellets using either pre-treatment; carbohydrates present in pellets were more efficiently recovered compared to non-pellets. LMA pretreated samples were separately hydrolyzed and fermented to ethanol using Scheffersomyces stipitis yeast. Hydrolysis recovered 69.7–76.8% of the glucose and 66.5–73.3% of the xylose across all samples. Glucose yields were 251–279 g/kg, db and were significantly lower for SG as compared to the other biomass samples. Recovered sugars were fermented to ethanol at 77.7–86.7% of theoretical yield. Final ethanol yields (245.9–275.5 L/Mg, db) were similar for all of the grasses and estimated to equate to production levels for BBS, LDM, and SG of 1,952, 2,586, and 2,636 l of ethanol per ha, respectively
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