Fish size effect on sagittal otolith outer shape variability in round goby Neogobius melanostomus (Pallas 1814).

Round goby Neogobius melanostomus (Pallas 1814) has become a significant component in the diet of piscivorous fish from the Pomeranian Bay (Bornholm Basin, Baltic Sea). Proper identification of fish species in the diet of predators is significant in biological studies of fish and other aquatic animal species, and, with regard to N. melanostomus, it is important to the knowledge of trophic web structures in areas this species has invaded. A total of 142 individuals of N. melanostomus, measuring 16-174 mm standard length, were examined. Seventy-two fishes were caught during monitoring surveys in fishing grounds, whereas 70 were found in the stomachs of European perch Perca fluviatilis, pike-perch Sander lucioperca and Baltic cod Gadus morhua. The objective of the present study was to analyse the sagittal otoliths to identify variations in outer shape with increases in fish length; expand and correct descriptions of the sagitta, lapillus and asteriscus otoliths; and evaluate the relationships among otolith dimensions and fish standard length. The otoliths were described morphologically. The analysis of the outer shape of sagittal otoliths using Fourier analysis and multivariate statistics exhibited great phenotypic variability that was associated with fish length, including within pairs in individuals and/or among individuals in length classes. In addition, the asterisci and lapilli of N. melanostomus from selected specimens, which were described for the first time with regard to fish length, were found to be less variable compared to sagittal otoliths. This study presents the first analysis of intrapopulation phenotypic plasticity of N. melanostomus sagittal otolith morphology as it is linked to fish size.

Dew point temperature affects ascospore release of allergenic genus Leptosphaeria.

The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded from 1 March to 31 October between 2006 and 2012. The experiment was conducted in three European countries of the temperate climate, i.e., Ukraine, Poland, and the UK. Out of over 150 forecast models produced using artificial neural networks (ANNs) and multivariate regression trees (MRTs), we selected the best model for each site, as well as for joint two-site combinations. The performance of all computed models was tested against records from 1 year which had not been used for model construction. The statistical analysis of the fungal spore data was supported by a comprehensive study of both climate and land cover within a 30-km radius from the air sampler location. High-performance forecasting models were obtained for individual sites, showing that the local micro-climate plays a decisive role in biology of the fungi. Based on the previous epidemiological studies, we hypothesized that dew point temperature (DPT) would be a critical factor in the models. The impact of DPT was confirmed only by one of the final best neural models, but the MRT analyses, similarly to the Spearman's rank test, indicated the importance of DPT in all but one of the studied cases and in half of them ranked it as a fundamental factor. This work applies artificial neural modeling to predict the Leptosphaeria airborne spore concentration in urban areas for the first time.

Contribution of Leptosphaeria species ascospores to autumn asthma in areas of oilseed rape production.

BACKGROUND: An increase in the number of hospital admissions from September to November in the northern hemisphere has been frequently reported. At this time, some species of fungal genus Leptosphaeria produce numerous ascospores, which are easily airborne. However, we lack knowledge about whether Leptosphaeria produces allergenic proteins. OBJECTIVE: To evaluate the potential of Leptosphaeria ascospores to contribute to autumn asthma. METHODS: Detailed bioinformatic analysis of proteins produced by Leptosphaeria maculans available in databases was performed and the data compared with allergens found in other airborne fungi. The concentrations of Leptosphaeria ascospores detected at 2 sites were compared to these obtained in other environments worldwide. RESULTS: We found that Leptosphaeria species produce proteins with a high identity to commonly known aeroallergens of several well-characterized molds. The level of amino acid identity significantly exceeded the allergen identity thresholds recommended by the Food and Agricultural Organization/World Health Organization (35%), which indicates allergenic properties of L maculans and ensures the same properties in the other Leptosphaeria species. CONCLUSION: High concentrations of Leptosphaeria species ascospores in the autumn and postulated allergenicity of their proteins strongly suggest that this genus contributes to worldwide reported autumn asthma. The finding opens the question of allergenicity of the other never studied fungal species present in aeroplankton.

A 10-year study of Alternaria and Cladosporium in two Polish cities (Szczecin and Cracow) and relationship with the meteorological parameters.

Alternaria and Cladosporium spores belong to the most frequent and allergenic particles in bioaerosol in the temperate climate. The investigation of Alternaria and Cladosporium spore concentrations was performed in two cities in Poland, Szczecin and Cracow, in 2004-2013. The meteorological parameters taken to assess their impact on fungal spores were average, maximum and minimum temperature, relative humidity and average wind velocity. In order to reveal whether changes in dynamics of spore seasons are driven by meteorological conditions, ordination methods were applied. Canonical correspondence analysis was used to explore redundancy among the predictors (meteorological parameters). Prior to ordination analyses, the data were log(x)-transformed. Concentrations of Alternaria and Cladosporium spores were significantly higher in Szczecin comparing to Cracow, but it was also observed the decreasing trend in the spore concentrations in Szczecin. As regards temperature, it was higher in Cracow and was still increasing in the studied years. Relative humidity and wind velocity were significantly lower in Cracow. In Szczecin meteorological conditions did not explain changes in spore season characteristics (insignificant redundancy analysis models), while in Cracow's redundancy analysis models indicated that spore season parameters were in over 40 % determined by meteorological conditions, mainly air temperature and wind velocity. If they increase, the peak value, total number of spores and their average concentrations in a season will also increase.

Application of redundancy analysis for aerobiological data.

An aerobiological survey was conducted through five consecutive years (2006-2010) at Worcester (England). The concentration of 20 allergenic fungal spore types was measured using a 7-day volumetric spore trap. The relationship between investigated fungal spore genera and selected meteorological parameters (maximum, minimum, mean and dew point temperatures, rainfall, relative humidity, air pressure, wind direction) was examined using an ordination method (redundancy analysis) to determine which environmental factors favoured their most abundance in the air and whether it would be possible to detect similarities between different genera in their distribution pattern. Redundancy analysis provided additional information about the biology of the studied fungi through the results of the Spearman's rank correlation. Application of the variance inflation factor in canonical correspondence analysis indicated which explanatory variables were auto-correlated and needed to be excluded from further analyses. Obtained information will be consequently implemented in the selection of factors that will be a foundation for forecasting models for allergenic fungal spores in the future.

Changes in concentration of Alternaria and Cladosporium spores during summer storms.

Fungal spores are known to cause allergic sensitization. Recent studies reported a strong association between asthma symptoms and thunderstorms that could be explained by an increase in airborne fungal spore concentrations. Just before and during thunderstorms the values of meteorological parameters rapidly change. Therefore, the goal of this study was to create a predictive model for hourly concentrations of atmospheric Alternaria and Cladosporium spores on days with summer storms in Szczecin (Poland) based on meteorological conditions. For this study we have chosen all days of June, July and August (2004-2009) with convective thunderstorms. There were statistically significant relationships between spore concentration and meteorological parameters: positive for air temperature and ozone content while negative for relative humidity. In general, before a thunderstorm, air temperature and ozone concentration increased, which was accompanied by a considerable increase in spore concentration. During and after a storm, relative humidity increased while both air temperature ozone concentration along with spore concentrations decreased. Artificial neural networks (ANN) were used to assess forecasting possibilities. Good performance of ANN models in this study suggest that it is possible to predict spore concentrations from meteorological variables 2 h in advance and, thus, warn people with spore-related asthma symptoms about the increasing abundance of airborne fungi on days with storms.

The effect of metals accumulated in reed (Phragmites australis) on the structure of periphyton.

Studies on trace elements in reed stands and limiting effect of the reed substrate on the periphyton structure were performed in various aquatic ecosystems of Greece during the summer and autumn of 2006. The analysed factors were concentrations of chemical elements (cadmium, lead, zinc, chromium, nickel, copper, cobalt, iron, manganese, potassium, sodium, calcium, magnesium) in reed shoots as well as the density of zooperiphyton and phytoperiphyton taxa. The relationships between metal concentrations and periphyton structure were determined with the use of the multivariate methods Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA) and RDA (Redundancy Analysis). The results showed that bioaccumulation of lead and cadmium in the reed had the most negative influence on zooperiphyton species, while low concentrations of alkali metals favoured the occurrence of Cyclopoida, Cladocera (Chydorus sp.) and Oligochaeta (Neis sp.). A considerable resistance to toxic heavy metals characterised Cyanophyta representatives and, partly, colonial Bacillariophyta. High concentrations of alkali metals supported the presence of unicellular Bacillariophyta but diminished the densities of colonial Bacillariophyta and Chlorophyta of the genus Scenedesmus.

The effects of meteorological factors on the occurrence of Ganoderma sp. spores in the air.

Ganoderma sp. is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we analysed fungal spore circulation in Szczecin, Poland, and its dependence on meteorological conditions. Statistical models for the airborne spore concentrations of Ganoderma sp.-one of the most abundant fungal taxa in the area-were developed. Aerobiological sampling was conducted over 2004-2008 using a volumetric Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity and maximum, minimum, average and dew point temperatures. These data were used as the explaining variables. Due to the non-linearity and non-normality of the data set, the applied modelling techniques were artificial neural networks (ANN) and mutlivariate regression trees (MRT). The obtained classification and MRT models predicted threshold conditions above which Ganoderma sp. appeared in the air. It turned out that dew point temperature was the main factor influencing the presence or absence of Ganoderma sp. spores. Further analysis of spore seasons revealed that the airborne fungal spore concentration depended only slightly on meteorological factors.

Influence of anthropogenic transformations of river bed on plant and macrozoobenthos communities.

This study describes the influence of urban area on plant communities and benthic invertebrates inhabiting the Slupia River (northern Poland). Ten plant communities and 37 macrozoobenthos taxa were determined during four seasonal samplings at 25 sampling sites (October 2005 and January, April, and August 2006). The obtained data set was statistically evaluated in order to reveal the influence of anthropogenic transformations on the investigated communities against the background of other abiotic factors. Multivariate regression tree (MRT) method was used for vegetation, while for benthic fauna, both MRT and artificial neural network (ANN) methods were applied. The following explanatory variables were used: season, water temperature, and salinity; location of a sampling site; degree of human impact on the riverbed; microhabitat; and substrate type. MRT analyses showed significant differences in plant community structure depending on the location of a sampling site, indicating the influence of anthropogenic pressure, while macrozoobenthos composition differed significantly only between seasons. The overall ANN model proved the importance of type and location of a sampling site for the approximation of benthic fauna density. Additionally, influence of the explanatory variables on the consecutive macrozoobenthos taxa was analyzed on the basis of separate ANN.

Effect of Protein Denaturation Temperature on Rheological Properties of Baltic Herring (Clupea harengus membras) Muscle Tissue.

The technological properties of raw fish are influenced by the changes in protein structure under heating, which determines the texture and quality of the product. The aim of the study was to examine the protein denaturation temperature and the rheological properties of Baltic herring muscle tissue. The thermal properties were determined by the differential scanning calorimetry (DSC) method and the rheological properties were determined using dynamic oscillatory tests. DSC showed four peaks associated with denaturing transformations of myosin (39.59 °C), sarcoplasm (51.67 °C), connective tissue (63.16 °C), and actin (74.40 °C). Analysis showed that not all transformations occurred according to the same kinetic model. The first two and the last peak are described by 1st order kinetics, while peak 3 is described by 2nd order kinetics. Correlating the changes in fish tissue structure during heating with the rheological characteristics provides more information. The obtained kinetics models correlated very strongly with the results of model testing. Rheological changes of the G' and G" values had two inflexion points and demonstrate a high degree of convergence with the DSC changes of herring muscle tissue from 20 to 85 °C.

Hourly predictive artificial neural network and multivariate regression tree models of Alternaria and Cladosporium spore concentrations in Szczecin (Poland).

A study was made of the link between time of day, weather variables and the hourly content of certain fungal spores in the atmosphere of the city of Szczecin, Poland, in 2004-2007. Sampling was carried out with a Lanzoni 7-day-recording spore trap. The spores analysed belonged to the taxa Alternaria and Cladosporium. These spores were selected both for their allergenic capacity and for their high level presence in the atmosphere, particularly during summer. Spearman correlation coefficients between spore concentrations, meteorological parameters and time of day showed different indices depending on the taxon being analysed. Relative humidity (RH), air temperature, air pressure and clouds most strongly and significantly influenced the concentration of Alternaria spores. Cladosporium spores correlated less strongly and significantly than Alternaria. Multivariate regression tree analysis revealed that, at air pressures lower than 1,011 hPa the concentration of Alternaria spores was low. Under higher air pressure spore concentrations were higher, particularly when RH was lower than 36.5%. In the case of Cladosporium, under higher air pressure (>1,008 hPa), the spores analysed were more abundant, particularly after 0330 hours. In artificial neural networks, RH, air pressure and air temperature were the most important variables in the model for Alternaria spore concentration. For Cladosporium, clouds, time of day, air pressure, wind speed and dew point temperature were highly significant factors influencing spore concentration. The maximum abundance of Cladosporium spores in air fell between 1200 and 1700 hours.

The application of artificial neural networks (ANN) for the denaturation of meat proteins – the kinetic analysis method.

BACKGROUND: Artificial neural networks (ANN) are a common mathematical tool widely used in many research fields. Since they are applicable to non-linear relationships and do not require preliminary assumptions, they are a particularly promising tool in relation to meat processing. Thermal denaturation contains    a lot of information concerning the quality of meats. The aim was to create a methodology of kinetic analysis to obtain a quick and accurate tool for meat protein denaturation in non-isothermal conditions based on   The Coats-Redfern equation with the use of ANN. METHODS: The analyses were carried out on samples of minced samples of Longissimus dorsi (pork). Thermal properties were determined using the differential scanning calorimetry (DSC) method with a Q100 TA Instruments apparatus. The data obtained was processed using the artificial neural network module in Statistica 13.0 software. RESULTS: The following models fit well with experimental data: F1 and F2 (r = 0.99, F Snedecor’s F statistics 836943.20 and 971947.41 respectively). Deviations from experimental conversion degrees were higher for model F2, while for F1, good conformity was obtained across the whole range of α(T). CONCLUSIONS: This preliminary study confirmed that methods of traditional kinetics of processes in non-isothermal conditions based on the Coats-Redfern equation can be successfully applied to meat protein denaturation. The method of kinetic analysis allows a high level of accuracy to be achieved and meets the requirements of an efficient engineering tool.  .

The use of neural networks in evaluation of the direction and dynamics of changes in lipid parameters in kidney transplant patients on the Mediterranean diet.

OBJECTIVE: The objective of the study was to assess whether neural networks can be a tool useful in the evaluation of the effect of the Mediterranean diet (MD) on the direction and dynamics of selected parameters. DESIGN: Randomized, prospective study. SETTING: Outpatient Clinic of the Department of Nephrology, Transplantology, and Internal Medicine. PATIENTS AND INTERVENTION: The study group consisted of 21 patients after kidney transplantation whose diet complied with the MD; the control group included 16 patients (also after transplantation) on a low-fat diet, isocaloric with the study diet. MAIN OUTCOME MEASURES: Anthropometry, plasma lipids, chromatography of triacylglycerols and fatty acids, and activity of superoxide dismutase and catalase were measured in both groups. Statistical analysis was done with the SNN (Statistica Neural Networks) StatSoft software package. RESULTS: The advantage of neural networks is the possibility of the dynamic presentation of a process taking place in a biological system. In the MD group in the first months of use of the diet, the cholesterol level was reduced only in the group of young and middle-aged patients. This tendency was not observed among elderly patients, among whom a small reduction of the total cholesterol level was noted only at the end of the observation period. In control group at the beginning of the observation, the plasma total cholesterol level was proportional to the patient's age. After 6 months, the total cholesterol increased in young patients and redacted in the group of elderly patients. CONCLUSIONS: We concluded that the MD diet would be ideal for posttransplantation patients without serious pathologic dyslipidemia. In the case of patients with substantial dyslipidemia, appropriate pharmacologic treatment lowering proatherosclerotic lipid levels should be used in combination with the MD. Artificial neural networks (ANNs) were a useful tool in modeling biological parameters, showing dynamics of the studied interactions in a very detailed way. ANN is the most suitable method for investigations with many variables, interconnected nonlinearly; therefore, this method allows for a more general approach to biological problems. However, it should be noted that considerable data sets are required to obtain a satisfactory fit to the data. Moreover, to ensure the predictive power of this method for new cases, the representative database is indispensable. In spite of these demands, ANN is a prospective tool for reliable, quick assessments and predictions.

Seasonal variation of Ganoderma spore concentrations in urban and suburban districts of the city of Szczecin, Poland.

According to recent studies, Ganoderma may be the third genus, after Alternaria and Cladosporium, the spores of which cause symptoms of allergy, and concentration is related to meteorological factors. The aerobiology of Ganoderma spores in Szczecin in urban and suburban districts was examined using Lanzoni Volumetric Spore Traps in 2008-2010. Ganoderma spores were present in the atmosphere on more than 90% of the days from June through September with peak concentrations in June, July and September. The number of days with spores was lower in the suburban district, while the total number of spores collected was higher there than in the urban district. Correlation and multiple regression analyses revealed weak relationships between Ganoderma and meteorological conditions, while testing the significance of differences between the districts showed that urban development did not have a clear impact on the values of meteorological parameters. A significantly higher abundance of spores in the suburbs of Szczecin seemed to be conditioned by the closeness of potential area sources. This study indicates that a single measuring site in the city centre insufficiently reflected the dynamics and level of Ganoderma spore concentration in peripheral districts.

The relationships between air pollutants, meteorological parameters and concentration of airborne fungal spores.

Fungal spores are an important component of bioaerosol and also considered to act as indicator of the level of atmospheric bio-pollution. Therefore, better understanding of these phenomena demands a detailed survey of airborne particles. The objective of this study was to examine the dependence of two the most important allergenic taxa of airborne fungi--Alternaria and Cladosporium--on meteorological parameters and air pollutant concentrations during three consecutive years (2006-2008). This study is also an attempt to create artificial neural network (ANN) forecasting models useful in the prediction of aeroallergen abundance. There were statistically significant relationships between spore concentration and environmental parameters as well as pollutants, confirmed by the Spearman's correlation rank analysis and high performance of the ANN models obtained. The concentrations of Cladosporium and Alternaria spores can be predicted with quite good accuracy from meteorological conditions and air pollution recorded three days earlier.

Hourly predictive artificial neural network and multivariate regression trees models of Ganoderma spore concentrations in Rzeszów and Szczecin (Poland).

Ganoderma spores are one of the most airspora abundant taxa in many regions of the world, and are considered to be important allergens. The aerobiology of Ganoderma basidiospores in two cities in Poland was examined using the volumetric method, (Burkard and Lanzonii Spore Traps), from selected days in 2004, 2005 and 2006. Spores of Ganoderma were present in the atmosphere from June to November, with peak concentrations generally occurring from late July to mid-October. ANN (artificial neural network) and MRT (multivariate regression trees), models indicated that atmospheric phenomenon, hour and relative humidity were the most important variables influencing spore content. The remaining variables (air temperature, dew point, air pressure, wind speed and wind direction), also contributed to the high network performance, (ratio above 1), but their impact was less distinct. Those results are consistent with the Spearman's rank correlation analysis.

Artificial neural network models of relationships between Alternaria spores and meteorological factors in Szczecin (Poland).

Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004--2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and logx+1 transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.