Distribution and transfer of trace metals in the Aegean Seawater (Eastern Mediterranean Basin)

Recent measurements of dissolved Cd, Cu, Ni and Mn in 324 water samples of the Aegean Sea fill the gap of missing knowledge in this part of the Eastern Mediterranean and try to identify their main input sources and spreading pathways. The analyses indicate that trace metal concentrations in the North and South Aegean Sea are generally in good agreement with those reported for the Western Mediterranean Sea. In the North Aegean Sea the trace metal distribution patterns differentiate mainly according to the existing water masses. Hence, a strong influence of the Black Sea Water, enriched in trace metals, is clearly recorded for Mn. Concentrations of this metal are one order of magnitude higher in the surface layer than those of the deeper waters. This feature is followed to a lesser degree also by Cd, Cu and Ni. Trace metal concentrations in the South Aegean Sea reveal almost constant values throughout the watercolumn similar to those observed in the North Aegean Sea below the depth of 100 m. Manganese values in the South Aegean Sea are considerably lower comparing with the North Aegean ones, showing relatively enhanced surface values which decrease with depth

Dissolved organic matter cycling in eastern Mediterranean rivers experiencing multiple pressures. The case of the trans-boundary Evros River

The objective of our study was to provide a comprehensive evaluation on C, N, P cycling in medium sized Mediterranean rivers, such as the Evros, experiencing multiple pressures (intensive agriculture, industrial activities, population density). Our work aims also to contribute to the development of integrated management policies. Dissolved organic matter (DOM) cycling were investigated, during a one-year study. It was shown that the organic component of N and P was comparable to those of large Mediterranean rivers (Rhone, Po). In the lower parts of the river where all point and non-point inputs converge, the high inorganic N input favour elevated assimilation rates by phytoplankton and result in increased chl-a concentrations and autochthonous dissolved organic matter (DOM) production during the dry season with limited water flow. Moreover, carbohydrate distribution revealed that there is a constant background of soil derived mono-saccharides on top of which are superimposed impulses of poly-saccharides during blooms. During the dry season, inorganic nutrients and DOM are trapped in the lower parts of the river, whereas during high flow conditions DOM is flushed towards the sea and organic nitrogen forms can become an important TDN constituent (at least 40%) transported to shelf waters. The co-existence of terrigenous material with autochthonous and some anthropogenic is supported by the relatively low DOC:DON and DOC:DOP ratios, the positive correlation of DOC vs chl-a and the decoupling between DOC and DON. Overall, this study showed that in medium size Mediterranean rivers, such as the Evros, intensive agriculture and pollution sources in combination with water management practices and climatic variability are important factors determining C, N, P dynamics and export to coastal seas. Also, it highlights the importance of the organic fraction of N and P when considering management practices

Pattern of reading eye movements during monovision contact lens wear in presbyopes

Monovision can be used as a method to correct presbyopia with contact lenses (CL) but its effect on reading behavior is still poorly understood. In this study eye movements (EM) were recorded in fifteen presbyopic participants, naïve to monovision, whilst they read arrays of words, non-words, and text passages to assess whether monovision affected their reading. Three conditions were compared, using daily disposable CLs: baseline (near correction in both eyes), conventional monovision (distance correction in the dominant eye, near correction in the non-dominant eye), and crossed monovision (the reversal of conventional monovision). Behavioral measures (reading speed and accuracy) and EM parameters (single fixation duration, number of fixations, dwell time per item, percentage of regressions, and percentage of skipped items) were analyzed. When reading passages, no differences in behavioral and EM measures were seen in any comparison of the three conditions. The number of fixations and dwell time significantly increased for both monovision and crossed monovision with respect to baseline only with word and non-word arrays. It appears that monovision did not appreciably alter visual processing when reading meaningful texts but some limited stress of the EM pattern was observed only with arrays of unrelated or meaningless items under monovision, which require the reader to have more in-depth controlled visual processing

A machine learning and chemometrics assisted interpretation of spectroscopic data: a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, allowing to obtain relevant information as to the importance of a given set of features, mostly related to the structural fingerprint of aliphatic and aromatic compounds typically found in propolis, e.g., fatty acids and phenolic compounds. The feature selection and decision tree-based algorithms used appear to be suitable tools for building classification models for the Brazilian propolis metabolomics regarding its geographic origin, with consistency, high accuracy, and avoiding redundant information as to the metabolic signature of relevant compounds.The work is partially funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEstOE/ EEI/UI0752/2011. RC's work is funded by a PhD grant from the Portuguese FCT ( ref. SFRH/BD/66201/2009)

An Investigation of the Role of Macular Pigment in Attenuating Photostress through Comparison between Blue and Green Photostress Recovery Times

Purpose: Photostress recovery time (PSRT) is the time required for the macula to return to its normal functioning after the bleaching of cone photopigments due to light exposure, usually white. This work investigates the role of macular pigment (MP) as an optical filter that attenuates photostress by analyses of PSRT at different wavelengths. Methods: Thirty-nine subjects (19–28 years) were exposed to blue/green photostress varying in irradiance. During photostress, pupil constriction (Cp) was measured. Twenty-seven subjects (20–27 years) were exposed to white photostress. After 25 s of photostress, the time (PSRT) required to read correctly a 0.2 logMAR letter was measured. Correlation was studied between PSRT, CP, and irradiance. Statistical significance of differences between PSRTs was evaluated at Log(irradiance(quanta s−1 cm−2)) = 14 by Student’s t statistics. Results: Cp and PSRT were found linearly correlated to Log(irradiance) for blue, green, and white. At Log(irradiance(quanta s−1 cm−2)) = 14, blue and green mean PSRTs resulted different (p 0.05). Conclusions: MP plays the role of an optical filter attenuating photostress. PSRT was substantially proportional to the number of incident photons corrected for the MP optical absorption, regardless of their wavelength

A 1H-NMR-based metabolomic analysis of propolis from Santa Catarina state

16th IUFoST World Congress of Food Science and Technology: Addressing Global Food Security and Wellness through Food Science and TechnologyPropolis is a resinous biomass produced by honeybees from exudates of local flora. It has been used since ancient times in folk medicine and in recent years has been added to foods and beverages to improve health and prevent diseases. The chemical composition of propolis is highly variable and depends on the climate, season, specie of bee, and mainly the local flora visited by bees to collect resin. In order to identify groups of chemical similarity among samples (n=20 autumn, n=16 winter, n=19 spring, n=17 summer) of propolis produced in Santa Catarina (SC) state (southern Brazil - 2010), lyophilized ethanolic extracts (200 mg/ml, EtOH 70%, v/v) were solubilized in MeOD3 (700l) and analyzed by NMR spectroscopy. One-dimensional 1HNMR spectra were acquired at a magnetic field strength of 500,13/125,03 MHz using a Varian Inova 500 MHz equipment and standard conditions of data acquisition. The 1H-NMR peak list data set was processed under MetaboAnalyst 2.0. suite, computing the resonances at 0.80- 12ppm spectral window. Principal Components Analysis (PCA) score scatter plots (PC1 88.2% x PC2 2.2%) clearly demonstrated samples discriminated mainly according to the season of production. These results suggest that not only geographical origin is important for the classification of propolis, but the seasonal effects as well. Since seasons directly influence the flora available from where bees collect resin, the propolis chemical profile can be significantly modified over the seasons even from a same geographical origin.info:eu-repo/semantics/publishedVersio

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.Financial support for this investigation by National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), Brazilian Biosciences National Laboratory (LNBioCNPEM/MCTI), Foundation for Support of Scientific and Technological Research in the State of Santa Catarina (FAPESC), and Portuguese Foundation for Science and Technology (FCT) is acknowledged. The research fellowship granted by CNPq to the first author is also acknowledged. The work was partially funded by a CNPq and FCT agreement through the PropMine grant

Assessment of the environmental status in Hellenic coastal waters (Eastern Mediterranean): from the Water Framework Directive to the Marine Strategy Water Framework Directive.

A  methodology is presented to assess the environmental status sensu the Marine Strategy Water Framework Directive (MSFD) based on data obtained from the monitoring of water quality in the Hellenic coastal waters within the Water Framework Directive (WFD).   An adapted decision tree used for integrating the results of the WFD in the Basque country was applied. Modifications lie to the evaluation of the physicochemical status based on a eutrophication index developed for Eastern Mediterranean waters. Results on hydromorphological, physicochemical and biological elements are presented. The chemical status was evaluated based on measurements of heavy metals in water. The evaluation of the biological quality was based on the use of metrics developed for phytoplankton biomass, benthic macroinvertebrates and macroalgae updated to accommodate MSFD needs. Results on the integrative status of the water bodies were validated by correlating classification results with a pressure index and environmental indicators in water column and sediment. Following this decision tree the majority of stations expected to be at risk of achieving the good status were found in moderate status. Benthos was found to be the element with the closest agreement with the integrated final status having an increased weighting in the decision tree. The quality of benthos and in some  limited cases  the eutrophication index determined largely the final status. The highest disagreement with the integrative classification was produced by macroalgae. All indicators used correlated with water and sediment parameters but benthos correlated better with sediment factors while phytoplankton and eutrophication index with water column parameters