Feasibility of using sea surface temperature imagery to mitigate cheloniid sea turtle–fishery interactions off the coast of northeastern USA

As sea turtles migrate along the Atlantic coast of the USA, their incidental capture in fisheries is a significant source of mortality. Because distribution of marine cheloniid turtles appears to be related, in part, to sea surface temperature (SST), the ability to predict water temperature over the continental shelf could be useful in minimizing turtle–fishery interactions. We analyzed 10 yr of advanced very high resolution radiometer (AVHRR) SST imagery to estimate the proportion of 18 spatial zones, nearshore and offshore of Hatteras, North Carolina, USA (35° N), to north of Cape Sable, Nova Scotia (44° N), at temperatures >10 to 15°C, by week. Detailed examples for 11°C, the temperature employed by some management actions in the study area, and for 14°C, the lowest temperature at which turtles were sighted by some studies in the area, demonstrate a predictable pattern of rapid warming in March and April, followed by rapid cooling in October and November, with nearshore waters warming more rapidly than those offshore. Of those loggerhead turtles Caretta caretta that stranded, were sighted, or were incidentally captured between Cape Hatteras, North Carolina, and Cape Cod, Massachusetts, those at lower latitudes occurred when 25% or more of the area reached a water temperature of 11°C, while those in the northern zones did not occur until 50% or more of the area had reached a water temperature of 14°C. This analysis provides a means of predicting marine cheloniid turtle presence, which can be helpful in regulating fisheries that seasonally interact with turtles.

Gold nanoparticle-based surface enhanced Raman scattering spectroscopic assay for the detection of protein-protein interactions

In the present work, a sensitive spectroscopic assay based on surface-enhanced Raman spectroscopy (SERS) using gold nanoparticles as substrates was developed for the rapid detection protein-protein interactions. Detection is achieved by specific binding biotin-modification antibodies with protein-stabilized 30 nm gold nanoparticles, followed by the attachment of avidin-modification Raman-active dyes. As a proof-of-principle experiment, a well-known biomolecular recognition system, IgG with protein A, was chosen to establish this new spectroscopic assay. Highly selective recognition of IgG down to 1 ng/ml in solution has been demonstrated.

Surface morphology evolution of a thin polymeric supramolecular film by tuning interactions

A polymeric supramolecule consisting of symmetric polystyrene-block-poly(4-vinylpytidine) (PS-b-P4VP), dodecylbenzenesulfonic acid (DBSA), and 3-pentadecylphenol (PDP) was formed by proton transfer and hydrogen bonding. The surface morphology,of a thin film of the polymeric supramolecule has been investigated. The spherical PS microdomains embedded in a P4VP(DBSA)(1.0)(PDP)(1.0) matrix are observed for the as-cast film because the weight fraction, f(comb), of the P4VP(DBSA) (1.0)(PDP)(1.0) blocks is much higher than that of PS as a result of the non-covalent interactions of P4VP and DBSA and DBSA and PDR Upon annealing the PS-b-P4VP(1:1)(DBSA)(1.0)(PDP)(1.0) film at high temperatures, the hydrogen bonding between the DBSA and PDP diminishes, which leads to a change of overall morphology from an ordered sphere to a pitted structure.

A Neural Theory of Attentive Visual Search: Interactions of Boundary, Surface, Spatial, and Object Representations

Visual search data are given a unified quantitative explanation by a model of how spatial maps in the parietal cortex and object recognition categories in the inferotemporal cortex deploy attentional resources as they reciprocally interact with visual representations in the prestriate cortex. The model visual representations arc organized into multiple boundary and surface representations. Visual search in the model is initiated by organizing multiple items that lie within a given boundary or surface representation into a candidate search grouping. These items arc compared with object recognition categories to test for matches or mismatches. Mismatches can trigger deeper searches and recursive selection of new groupings until a target object io identified. This search model is algorithmically specified to quantitatively simulate search data using a single set of parameters, as well as to qualitatively explain a still larger data base, including data of Aks and Enns (1992), Bravo and Blake (1990), Chellazzi, Miller, Duncan, and Desimone (1993), Egeth, Viri, and Garbart (1984), Cohen and Ivry (1991), Enno and Rensink (1990), He and Nakayarna (1992), Humphreys, Quinlan, and Riddoch (1989), Mordkoff, Yantis, and Egeth (1990), Nakayama and Silverman (1986), Treisman and Gelade (1980), Treisman and Sato (1990), Wolfe, Cave, and Franzel (1989), and Wolfe and Friedman-Hill (1992). The model hereby provides an alternative to recent variations on the Feature Integration and Guided Search models, and grounds the analysis of visual search in neural models of preattentive vision, attentive object learning and categorization, and attentive spatial localization and orientation.

Analysis of Biological Interactions and Recognitions by Surface Plasmon Resonance

This chapter contains sections titled:

•Introduction to Surface Plasmon Resonance Technology

•Working Principle of SPR

•Sensor Surface Chemistry and Its Fabrications

•Important Factors Impacting on the Performance of SPR-Based Analyses of Biological Interactions on the Nonbiological Transducer Surface

•Localized SPR of Inorganic Nanoparticles for Analyses of Biological Interaction

•References

Indicators of nitrate in wetland surface and soil-waters: interactions of vegetation and environmental factors

This paper describes a new bio-indicator method for assessing wetland ecosystem health: as such, the study is particularly relevant to current legislation such as the EU Water Framework Directive, which provides a baseline of the current status Of Surface waters. Seven wetland sites were monitored across northern Britain, with model construction data for predicting, eco-hydroloplical relationships collected from five sites during 1999, Two new sites and one repeat site were monitored during 2000 to provide model test data. The main growing season for the vegetation, and hence the sampling period, was May-August during both years. Seasonal mean concentrations of nitrate (NO3-) in surface and soil water samples during 1999 ranged from 0.01 to 14.07 mg N 1(-1), with a mean value of 1.01 mg N 1(-1). During 2000, concentrations ranged from trace level (<0.01 m- N 1(-1)) to 9.43 mg N 1(-1), with a mean of 2.73 mg N 1(.)(-1) Surface and soil-water nitrate concentrations did not influence plant species composition significantly across representative tall herb fen and mire communities. Predictive relationships were found between nitrate concentrations and structural characteristics of the wetland vegetation, and a model was developed which predicted nitrate concentrations from measures of plant diversity, canopy structure and density of reproductive structures. Two further models, which predicted stem density and density of reproductive structures respectively, utilised nitrate concentration as one of the independent predictor variables. Where appropriate, the models were tested using data collected during 2000. This approach is complementary to species-based monitoring, representing a useful and simple too] to assess ecological status in target wetland systems and has potential for bio-indication purposes.

An assessment of aerosol‐cloud interactions in marine stratus clouds based on surface remote sensing

An assessment of aerosol-cloud interactions (ACI) from ground-based remote sensing under coastal stratiform clouds is presented. The assessment utilizes a long-term, high temporal resolution data set from the Atmospheric Radiation Measurement (ARM) Program deployment at Pt. Reyes, California, United States, in 2005 to provide statistically robust measures of ACI and to characterize the variability of the measures based on variability in environmental conditions and observational approaches. The average ACIN (= dlnNd/dlna, the change in cloud drop number concentration with aerosol concentration) is 0.48, within a physically plausible range of 0–1.0. Values vary between 0.18 and 0.69 with dependence on (1) the assumption of constant cloud liquid water path (LWP), (2) the relative value of cloud LWP, (3) methods for retrieving Nd, (4) aerosol size distribution, (5) updraft velocity, and (6) the scale and resolution of observations. The sensitivity of the local, diurnally averaged radiative forcing to this variability in ACIN values, assuming an aerosol perturbation of 500 c-3 relative to a background concentration of 100 cm-3, ranges betwee-4 and -9 W -2. Further characterization of ACI and its variability is required to reduce uncertainties in global radiative forcing estimates.

Surface science studies of strong metal-oxide interactions on model catalysts

The strong metal support interaction (SMSI) was first described in 1978 by Tauster [1-4]. The effect was observed as a severely negative effect on CO and H2 uptake on the catalyst after high temperature calcination under reducing conditions (heating above ~ 700 K) [1,2]. It also had a negative effect on the reaction rate for reactions, such as alkane hydrogenolysis [5,6]. It appeared that the effect occurred for catalysts comprised of reducible supports which were treated at elevated temperature in reducing conditions [2-4]. A classic support which has manifested this behaviour in many studies is TiO2. Over the years following the first discovery of SMSI it has been recognised that the effect is not always negative – for instance for the CO-H2 reaction for which it appears to have a positive effect [5,6]. Further it was noted that hydrogen reduction was not necessary to observe the effect of CO adsorption suppression, it also occurs by vacuum treatment [7], though it should be noted that vacuum treatment at elevated temperature is, in effect, a reducing environment.

Adsorption of organic molecules at the TiO2(110) surface: the effect of van der Waals interactions

Understanding the interaction of organic molecules with TiO2 surfaces is important for a wide range of technological applications. While density functional theory (DFT) calculations can provide valuable insight about these interactions, traditional DFT approaches with local exchange-correlation functionals suffer from a poor description of non-bonding van der Waals (vdW) interactions. We examine here the contribution of vdW forces to the interaction of small organic molecules (methane, methanol, formic acid and glycine) with the TiO2 (110) surface, based on DFT calculations with the optB88-vdW functional. The adsorption geometries and energies at different configurations were also obtained in the standard generalized gradient approximation (GGA-PBE) for comparison. We find that the optB88-vdW consistently gives shorter surface adsorbate-to-surface distances and slightly stronger interactions than PBE for the weak (physisorbed) modes of adsorption. In the case of strongly adsorbed (chemisorbed) molecules both functionals give similar results for the adsorption geometries, and also similar values of the relative energies between different chemisorption modes for each molecule. In particular both functionals predict that dissociative adsorption is more favourable than molecular adsorption for methanol, formic acid and glycine, in general agreement with experiment. The dissociation energies obtained from both functionals are also very similar, indicating that vdW interactions do not affect the thermodynamics of surface deprotonation. However, the optB88-vdW always predicts stronger adsorption than PBE. The comparison of the methanol adsorption energies with values obtained from a Redhead analysis of temperature programmed desorption data suggests that optB88-vdW significantly overestimates the adsorption strength, although we warn about the uncertainties involved in such comparisons.

Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1)

The Land surface Processes and eXchanges (LPX) model is a fire-enabled dynamic global vegetation model that performs well globally but has problems representing fire regimes and vegetative mix in savannas. Here we focus on improving the fire module. To improve the representation of ignitions, we introduced a reatment of lightning that allows the fraction of ground strikes to vary spatially and seasonally, realistically partitions strike distribution between wet and dry days, and varies the number of dry days with strikes. Fuel availability and moisture content were improved by implementing decomposition rates specific to individual plant functional types and litter classes, and litter drying rates driven by atmospheric water content. To improve water extraction by grasses, we use realistic plant-specific treatments of deep roots. To improve fire responses, we introduced adaptive bark thickness and post-fire resprouting for tropical and temperate broadleaf trees. All improvements are based on extensive analyses of relevant observational data sets. We test model performance for Australia, first evaluating parameterisations separately and then measuring overall behaviour against standard benchmarks. Changes to the lightning parameterisation produce a more realistic simulation of fires in southeastern and central Australia. Implementation of PFT-specific decomposition rates enhances performance in central Australia. Changes in fuel drying improve fire in northern Australia, while changes in rooting depth produce a more realistic simulation of fuel availability and structure in central and northern Australia. The introduction of adaptive bark thickness and resprouting produces more realistic fire regimes in Australian savannas. We also show that the model simulates biomass recovery rates consistent with observations from several different regions of the world characterised by resprouting vegetation. The new model (LPX-Mv1) produces an improved simulation of observed vegetation composition and mean annual burnt area, by 33 and 18% respectively compared to LPX.

Interparticle Interactions Effects on the Magnetic Order in Surface of Fe(3)O(4) Nanoparticles

We report interparticle interactions effects on the magnetic structure of the surface region in Fe(3)O(4) nanoparticles. For that, we have studied a desirable system composed by Fe(3)O(4) nanoparticles with (d) = 9.3 nm and a narrow size distribution. These particles present an interesting morphology constituted by a crystalline core and a broad (similar to 50% vol.) disordered superficial shell. Two samples were prepared with distinct concentrations of the particles: weakly-interacting particles dispersed in a polymer and strongly-dipolar-interacting particles in a powder sample. M(H, T) measurements clearly show that strong dipolar interparticle interaction modifies the magnetic structure of the structurally disordered superficial shell. Consequently, we have observed drastically distinct thermal behaviours of magnetization and susceptibility comparing weakly- and strongly-interacting samples for the temperature range 2 K < T < 300 K. We have also observed a temperature-field dependence of the hysteresis loops of the dispersed sample that is not observed in the hysteresis loops of the powder one.