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Background Fungal spores and plant pollen cause respiratory diseases in susceptible individuals, such as asthma, allergic rhinitis and hypersensitivity pneumonitis. Aeroallergen monitoring networks are an important part of treatment strategies, but unfortunately traditional analysis is time consuming and expensive. We have explored the use of infrared spectroscopy of pollen and spores for an inexpensive and rapid characterization of aeroallergens. Methodology The study is based on measurement of spore and pollen samples by single reflectance attenuated total reflectance Fourier transform infrared spectroscopy (SR-ATR FTIR). The experimental set includes 71 spore (Basidiomycota) and 121 pollen (Pinales, Fagales and Poales) samples. Along with fresh basidiospores, the study has been conducted on the archived samples collected within the last 50 years. Results The spectroscopic-based methodology enables clear spectral differentiation between pollen and spores, as well as the separation of confamiliar and congeneric species. In addition, the analysis of the scattering signals inherent in the infrared spectra indicates that the FTIR methodology offers indirect estimation of morphology of pollen and spores. The analysis of fresh and archived spores shows that chemical composition of spores is well preserved even after decades of storage, including the characteristic taxonomy-related signals. Therefore, biochemical analysis of fungal spores by FTIR could provide economical, reliable and timely methodologies for improving fungal taxonomy, as well as for fungal identification and monitoring. This proof of principle study shows the potential for using FTIR as a rapid tool in aeroallergen studies. In addition, the presented method is ready to be immediately implemented in biological and ecological studies for direct measurement of pollen and spores from flowers and sporocarps.

This paper deals with the precise identification of species of Pseudo-nitzschia , focusing on those which are a potential source of domoic acid, from the Krka River estuary of the Central Adriatic Sea. Domoic acid was measured in phytoplankton net samples and shellfish collected in the winter and early spring of 2011 and 2012. Domoic acid was only detected in early March 2011, both in plankton net samples and shellfish extracts, during a Pseudo-nitzschia species bloom. The measured concentrations of particulate domoic acid (DA) in filtered concentrated seawater varied from 3.1˗6.2 ng DA ml -1 . In shellfish sample DA concentration was 0.2 μg g -1 . Species belonging to the Pseudo-nitzschia delicatissima complex were more common than those from the Pseudo-nitzschia seriata complex. Morphological analyses by electron microscopy revealed the presence of three potentially toxic species: P. calliantha , P. pseudodelicatissima and P. pungens, and one non-toxic species: P. subfraudulenta . However, P. calliantha and P. pseudodelicatissima dominated during the March 2011 bloom. This study presents the first evaluation of particulate domoic acid along the Eastern Adriatic Sea and the first record of the presence of P. calliantha , P. pseudodelicatissima , P. pungens and P. subfraudulenta in the Krka River estuary.

With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model the most widely-obtained type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they felt comfortable using to model synthetic titration curves that are typical of those obtained by applying state-of-the-art electrochemical methods – anodic stripping voltammetry (ASV) and competitive ligand equilibration/adsorptive cathodic stripping voltammetry (CLE-ACSV) – to the analysis of natural waters. Herein, we compare our estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations ([Mf]), and evaluate the influence of the various methods and assumptions used in analyzing the data on these results. The ASV- type titrations were designed to test each participants’ ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameter values. For the four virtual samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate “measurements” of ambient [Mf]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one’s parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root-mean squared difference between the fitted observations and simulated curves should be comparable to the expected error of the analytical method and upon visual inspection the distribution of residuals should be unskewed. Modeling the synthetic, CLE-ACSV-type titration dataset proved to be more challenging. The participants were provided with five distinct titration curves generated at different levels of competing ligand added (analytical windows) to the virtual sample. Random measurement error was also incorporated. Comparison of the submitted results was complicated by the differing interpretations of our task. Most adopted the provided “true” instrumental sensitivity in modeling the CLE- ACSV curves, but several estimated sensitivities using internal calibration, exactly as is required for actual samples. Since the fitted sensitivities were biased low, systematic biases in inferred ambient [Mf] and in estimated weak ligand (L2) concentrations resulted from their use. The main distinction between the mathematical approaches taken by participants lies in their choice of the speciation model equation/function, with its implicit definition of independent and dependent variables. In “direct modeling”, the dependent variable is the measured [Mf] (or Ip) and the total metal concentration ([M]T) considered independent. In other, much more widely used methods of analyzing titration data – classical linearization, best known as van den Berg/Ružić, and isotherm fitting by nonlinear regression, best known as the Langmuir or Gerringa methods – [Mf] is defined as independent and the dependent variable derived from a calculation that involves both [M]T and [Mf]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [Mf] revealed that the best results were obtained by the first approach. Linear regression of transformed data yielded the largest bias and greatest variability, while non-linear isotherm fitting generated results with mean bias comparable to direct modeling, but also with greater variability. Participants that performed a unified analysis of ACSV titration curves at multiple detection windows for a sample improved their results regardless of the basic mathematical approach taken. Overall, the three most accurate sets of results were obtained using automated-unified analysis while the single most accurate set of results combined simultaneous calibration and parameter estimation. We therefore recommend that where sample volume and time permit, titration experiments for all natural water samples be designed to include two or more detection windows, especially for coastal and estuarine waters. It is vital that even more practical experimental designs for multi-window titrations be developed. In addition, while nearly every mathematical approach can prove to be adequate for some datasets, matrix-based equilibrium models are most naturally suited to the task for all datasets and can most easily handle the challenges encountered in this work, i.e., the cases where the added ligand in ACSV became titrated. The ProMCC program (Omanović et al., this issue) as well as the Excel Add-in based KINETEQL Multiwindow Solver spreadsheet (Hudson, 2014) have this capability and have been made available for public use as a result of this intercalibration exercise.

Diatoms have evolved a variety of colonial life forms in which cells are connected by organic threads, mucilage pads or silicate structures. In this study, we provide the first description of a novel strategy of colony formation among marine planktonic diatoms. Bacteriastrum jadranum forms loose but regular chains with distinct heterovalvate terminal cells. The colonial cells and their siliceous projections, the setae, are not in direct contact ; instead, they are enclosed within the optically transparent organic matrix. This cell jacket structure was detected by staining procedure with Alcian Blue, which showed that the polysaccharides are predominant matrix constituents and revealed that the jacket reaches the span of the setae. The scanning electron microscopy (SEM) observations showed distinguishable fibrillar network firmly associated with cells. Using atomic force microscopy (AFM), we were able to visualise and characterise the cell jacket structure at molecular resolution. At nanoscale resolution, the cell jacket appears as a cross-linked fibrillar network organised into a recognisable structure. The circular patches of self-repeating pattern (hexagonal pores with openings of 8–100 nm) are connected through thicker surrounding fibrils and reinforced by branching fibrils. The pore-forming fibrils within the patches are only 0.6–1.6 nm high, the surrounding fibrils connecting patches are 2.0–2.8 nm high, and the branching fibrils are considerably wider but not higher than 4.0 nm. The discovered polysaccharide fibrillar network is highly organised and delicately structured with a monomolecular fibril height of 0.6 nm. We conclude that the Bacteriastrum polysaccharide jacket represents an essential part of the cell, as the conjunction of the polymer network with the frustule appears to be extremely tight and such specific and unique patterns have never been found in self-assembled polysaccharide gel networks, which are usually encountered in the marine environment.

A system using a neutron sensor installed within a Remotely Operated Vehicle (ROV) for underwater inspection has been developed. The system can inspect objects for the presence of threat materials, such as explosives and chemical agents, by using alpha particle tagged neutrons from a sealed tube d + t neutron generator to produce characteristic gamma rays within the interrogated object. Here we show that the measured gamma spectra for commonly found ammunition charged with TNT explosives are dominated by C, O and Fe peaks enabling the underwater determination of explosives inside an ammunition shell. International audience

Biochemical and fatty acid composition of the bivalve Arca noae were investigated in the Mali Ston Bay in relation to environmental conditions. Sampling was carried out monthly, from December 2001 to November 2002. Wet shellfish meat consists on average of 77.61% water and 22.39% dry matter, while dry shellfish meat consists on average of 89.04% organic and 10.96% inorganic matter. PCA analysis identified temperature, nitrate, silicate, MICRO, Chl a and salinity as the most important environmental factors influencing biochemical composition of A. noae. An increase of dry weight content of A. noae was observed during the spring when both the sea temperature and food supply increased rapidly. Contents of protein (54.39-62.06% of dry weight), carbohydrate (4.13-8.07% of dry weight) and lipid (3.46-8.58% of dry weight) varied significantly during the year. Protein and lipid level reached the maximum value in June. The fatty acid profiles of total lipids extracted from A. noae showed high level of unsaturation (UNS/SAT 1.9-3.4). Total polyunsaturated fatty acids (PUFA) represented the majority of total fatty acids (40.3-59.9% of total fatty acids) and the most abundant were eicosapentaenoic (20:5n-3) and docosahexaenoic (22:6n-3) acid. n-3/n-6 PUFA ratio value varied between 2.1 and 5.0 and was the highest during the spring (April to June). Due to their low lipid and high percentages of healthy polyunsaturated fatty acids A. noae can be evaluated as a quality seafood product. The most suitable period of the year for its consumption is in the spring when it reaches its highest nutritional values.