Rapid determination of residues of pesticides in honey by µGC-ECD and GC-MS/MS: Method validation and estimation of measurement uncertainty according to document No. SANCO/12571/2013

A simple and straightforward method for simultaneous determination of residues of 13 pesticides in honey samples (acrinathrin, bifenthrin, bromopropylate, cyhalothrin-lambda, cypermethrin, chlorfenvinphos, chlorpyrifos, coumaphos, deltamethrin, fluvalinate-tau, malathion, permethrin and tetradifon) from different pesticide classes has been developed and validated. The analytical method provides dissolution of honey in water and an extraction of pesticide residues by n-Hexane followed by clean-up on a Florisil SPE column. The extract was evaporated and taken up by a solution of an injection internal standard (I-IS), ethion, and finally analyzed by capillary gas chromatography with electron capture detection (GC-µECD). Identification for qualitative purpose was conducted by gas chromatography with triple quadrupole mass spectrometer (GC-MS/MS). A matrix-matched calibration curve was performed for quantitative purposes by plotting the area ratio (analyte/I-IS) against concentration using a GC-µECD instrument. According to document No. SANCO/12571/2013, the method was validated by testing the following parameters: linearity, matrix effect, specificity, precision, trueness (bias) and measurement uncertainty. The analytical process was validated analyzing blank honey samples spiked at levels equal to and greater than 0.010 mg/kg (limit of quantification). All parameters were satisfactorily compared with the values established by document No. SANCO/12571/2013. The analytical performance was verified by participating in eight multi-residue proficiency tests organized by BIPEA, obtaining satisfactory z-scores in all 70 determinations. Measurement uncertainty was estimated according to the top-down approaches described in Appendix C of the SANCO document using the within-laboratory reproducibility relative standard deviation combined with laboratory bias using the proficiency test data.     

Changes in the phytoplankton community at Helgoland, North Sea: lessons from single spot time series analyses

In recent regime shift analyses, the phytoplankton compartment of the marine food web was essentially represented by phytoplankton color or chlorophyll concentration. A detection of changes directly at the species level is highly desirable. The Helgoland Roads data series, a collection of high frequency long-term time series comprising biological and physico-chemical components of the southern North Sea, allow such an investigation at the level of single species. Aiming at a detection and characterization of habitat and community changes in the observation period (1962 until the end of 2008), we selected six species as representatives of certain classes, for example, benthic or neritic species, and applied a combination of novel analysis methods—a fitness-based analysis of the realized niche, a bloom-triggered averaging and a Markovian analysis of co-occurrence and succession patterns—to related abundance time series and concurrent environmental parameter time series. We found a general trend toward enlargement of niche size and shifts of the niche position, interesting salinity patterns around bloom events of two species, and statistically highly significant changes of a phytoplankton community segment after 1965 and after 1998. Interpreting our observations in ecological terms leads to the formulation of testable hypotheses.     

Evaluation of the effectiveness of horizontal subsurface flow constructed wetlands for different media

Two media bed (gravel and Filtralite NR) were tested in a mesocosm to evaluate the removal of organic matter (as chemical oxygen demand (COD)),ammonia (NH4-N),nitrite,nitrate and solid matter (as total suspended solids (TSS)) for a synthetic wastewater (acetate-based) and a domestic wastewater.The use of Filtralite allowed average removal rates (6–16.8 g COD/(m2·day),0.8–1.1 g NH4-N/(m2·day) and 3.1 g TSS/(m2·day)) and removal effciencies (65%–93%,57%–85% and 78% for COD,NH4-N and TSS,respectively),higher than that observed in the experiments with gravel.The applied loads of COD,ammonia,nitrate and TSS seem to influence the respective removal rates but only for the treatment of domestic wastewater with higher correlation coefficients for Filtralite.Regardless the type of media bed and the type of wastewater,nitrate was completely removed for nitrogen loading rates up to 1.3 g NO3-N/(m2·day).There was no evidence of the influence of nitrate loads on the removal of organic matter.     

Topographic heterogeneity influences fish use of an experimentally restored tidal marsh.

Ecological theory predicts that incorporating habitat heterogeneity into restoration sites should enhance diversity and key functions, yet research is limited on how topographic heterogeneity affects higher trophic levels. Our large (8-ha) southern California restoration experiment tested effects of tidal creek networks and pools on trophic structure of salt marsh habitat and high-tide use by two regionally dominant fish species, California killifish (Fundulus parvipinnis) and longjaw mudsucker (Gillichthys mirabilis). We expected tidal creeks to function as "conduits" that would enhance connectivity between subtidal and intertidal habitat and pools to serve as microhabitat "oases" for fishes. Pools did provide abundant invertebrate prey and were a preferred microhabitat for F. parvipinnis, even when the entire marsh was inundated (catch rates were 61% higher in pools). However, G. mirabilis showed no preference for pools. At a larger scale, effects of tidal creek networks were also mixed. Areas containing creeks had 12% higher catch rates of G. mirabilis, but lower catch rates and feeding rates of F. parvipinnis. Collectively, the results indicate that restoring multiple forms of heterogeneity is required to provide opportunities for multiple target consumers.     

Estimating wildfire growth from noisy and incomplete incident data using a state space model

Wildfire behaviors are complex and are of interest to fire managers and scientists for a variety of reasons. Many of these important behaviors are directly measured from the cumulative burn area time series of individual wildfires; however, estimating cumulative burn area time series is challenging due to the magnitude of measurement errors and missing entries. To resolve this, we introduce two state space models for reconstructing wildfire burn area using repeated observations from multiple data sources that include different levels of measurement error and temporal gaps. The constant growth parameter model uses a few parameters and assumes a burn area time series that follows a logistic growth curve. The non-constant growth parameter model uses a time-varying logistic growth curve to produce detailed estimates of the burn area time series that permit sudden pauses and pulses of growth. We apply both reconstruction models to burn area data from 13 large wildfire incidents to compare the quality of the burn area time series reconstructions and computational requirements. The constant growth parameter model reconstructs burn area time series with minimal computational requirements, but inadequately fits observed data in most cases. The non-constant growth parameter model better describes burn area time series, but can also be highly computationally demanding. Sensitivity analyses suggest that in a typical application, the reconstructed cumulative burn area time series is fairly robust to minor changes in the prior distributions.     

Incorporating animal behavior into seed dispersal models: implications for seed shadows

Seed dispersal fundamentally influences plant population and community dynamics but is difficult to quantify directly. Consequently, models are frequently used to describe the seed shadow (the seed deposition pattern of a plant population). For vertebrate-dispersed plants, animal behavior is known to influence seed shadows but is poorly integrated in seed dispersal models. Here, we illustrate a modeling approach that incorporates animal behavior and develop a stochastic, spatially explicit simulation model that predicts the seed shadow for a primate-dispersed tree species (Virola calophylla, Myristicaceae) at the forest stand scale. The model was parameterized from field-collected data on fruit production and seed dispersal, behaviors and movement patterns of the key disperser, the spider monkey (Ateles paniscus), densities of dispersed and non-dispersed seeds, and direct estimates of seed dispersal distances. Our model demonstrated that the spatial scale of dispersal for this V. calophylla population was large, as spider monkeys routinely dispersed seeds >100 m, a commonly used threshold for long-distance dispersal. The simulated seed shadow was heterogeneous, with high spatial variance in seed density resulting largely from behaviors and movement patterns of spider monkeys that aggregated seeds (dispersal at their sleeping sites) and that scattered seeds (dispersal during diurnal foraging and resting). The single-distribution dispersal kernels frequently used to model dispersal substantially underestimated this variance and poorly fit the simulated seed-dispersal curve, primarily because of its multimodality, and a mixture distribution always fit the simulated dispersal curve better. Both seed shadow heterogeneity and dispersal curve multimodality arose directly from these different dispersal processes generated by spider monkeys. Compared to models that did not account for disperser behavior, our modeling approach improved prediction of the seed shadow of this V. calophylla population. An important function of seed dispersal models is to use the seed shadows they predict to estimate components of plant demography, particularly seedling population dynamics and distributions. Our model demonstrated that improved seed shadow prediction for animal-dispersed plants can be accomplished by incorporating spatially explicit information on disperser behavior and movements, using scales large enough to capture routine long-distance dispersal, and using dispersal kernels, such as mixture distributions, that account for spatially aggregated dispersal.     

Irgarol inhibits the synthesis of ATP in mitochondria from rat liver

The interactions of Irgarol with rat liver mithocondrial have been investigated. The results indicate that Irgarol inhibits the ATP synthesis. The analysis of the various steps involved in the ATP synthesis suggests that the inhibition is due to the opening of small-size pores.     

Characterization and application of the enzyme peroxidase to the degradation of the mycotoxin DON

Deoxynivalenol (DON), one of the main mycotoxins found in food matrices, has high level of toxicity. This study aimed to characterize the peroxidase enzyme extracted from rice bran to be applied to the biodegradation of DON in order to evaluate the potential peroxidase (PO) from rice bran (RB) has to degrade DON in optimal conditions. Purification and recovery factors of PO extracted from RB and purified by three-phase partitioning were 5.7% and 50%, respectively. PO had the highest level of activity in the phosphate buffer 5 mM pH 5.5 in both crude and purified forms, whose reaction temperatures were 25°C and 10°C. At the end of production, purification and characterization steps, specific activities of the bran were 115.79 U mg^?1 and 4363 U g^?1. Reduction in the mycotoxin DON in optimal conditions determined for PO from RB was 20.3%, a promising result when the aim is to adequate mycotoxicological levels to foods.