Tail regeneration affects the digestive performance of a Mediterranean lizard

In caudal autotomy, lizards shed their tail to escape from an attacking predator. Since the tail serves multiple functions, caudal regeneration is of pivotal importance. However, it is a demanding procedure that requires substantial energy and nutrients. Therefore, lizards have to increase energy income to fuel the extraordinary requirements of the regenerating tail. We presumed that autotomized lizards would adjust their digestion to acquire this additional energy. To clarify the effects of tail regeneration on digestion, we compared the digestive performance before autotomy, during regeneration, and after its completion. Tail regeneration indeed increased gut passage time but did not affect digestive performance in a uniform pattern: though protein income was maximized, lipid and sugar acquisition remained stable. This divergence in proteins may be attributed to their particular role in tail reconstruction, as they are the main building blocks for tissue formation.     

Chemical composition and mass closure of ambient PM10 at urban sites

The chemical composition of PM10 was studied during summer and winter sampling campaigns conducted at two different urban sites in the city of Thessaloniki, Greece (urban-traffic, UT and urban-industrial, UI). PM10 samples were chemically analysed for minerals (Si, Al, Ca, Mg, Fe, Ti, K), trace elements (Cd, Cr, Cu, Mn, Pb, V, Zn, Te, Co, Ni, Se, Sr, As, and Sb), water-soluble ions (Cl^?, NO₃^?, SO₄^2?, Na⁺, K⁺, NH₄⁺, Ca²⁺, Mg²⁺) and carbonaceous compounds (OC, EC). Spatial variations of atmospheric concentrations showed significantly higher levels of minerals, some trace metals and TC at the UI site, while at the UT site significantly higher levels of elements like Cd, Ba, Sn, Sb and Te were observed. Crustal elements, excepting Ca at the UI site, did not exhibit significant seasonal variations at any site pointing to constant emissions throughout the year. In order to reconstruct the particle mass, the determined components were classified into six classes as follows: mineral matter (MIN), trace elements (TE), organic matter (OM), elemental carbon (EC), sea salt (SS) and secondary inorganic aerosol (SIA). Good correlations with slopes close to 1 were found between chemically determined and gravimetrically measured PM10 masses for both sites. According to the chemical mass closure obtained, the major components of PM10 at both sites were MIN (soil-derived compounds), followed by OM and SIA. The fraction unaccounted for by chemical analysis comprised on average 8% during winter and 15% during summer at the urban-industrial site, while at the urban-traffic site the percentages were 21.5% in winter and 4.8% in summer.     

An application of parametric and nonparametric models to the assessment of fluoride levels in vegetation exposed to stack emissions of an aluminum reduction plant in Greece

Various statistical models were developed for assessing airborne fluoride (F) levels in natural vegetation near an aluminum reduction plant using as predictor variables the distance from the emission source, the predominating wind, and characteristic topography-geomorphology parameters. Results revealed that F concentrations in vegetation showed a predictable response to both wind conditions and landscape features. The linear model was found to give good estimations, taking advantage of the relatively strong linear correlation between concentration and distance. A nonlinear relationship between the F concentration in vegetation and the other variables was also found, while interactions between the variables were found to be non-first-order. The nonlinear relationship hypothesis was supported by the improved results of various nonlinear models that also indicated the importance of the area's topography-geomorphology and meteorology in model predictions. The application of an artificial neural network (ANN) model showed the closest agreement between predicted and observed values with a correlation coefficient of 0.92. The improved reliability of the ANN and a regression tree model (RTM) also were indicated by the normal distribution of their residuals. The RTM and the ANN were further investigated and found to be capable of identifying the importance of the variables in vegetation exposure to air emissions.     

Optimization of thermo-chemical pretreatment and enzymatic hydrolysis of kitchen wastes

The use of abundant waste materials with high carbohydrate content may contribute substantially to reduction of biofuels production cost. The present study aimed at optimizing the combined effect of thermo-chemical pretreatment and enzymatic hydrolysis of kitchen wastes (KW) for maximizing the production of fermentable soluble sugars. To this end, acid pretreatment of KW samples was performed with hydrochloric acid (0–3% HCl) at 30–100 °C for 0–120 min treatment time. Alternatively, alkaline pretreatment of KW samples was performed with potassium hydroxide solution (0–11%) at constant temperature and time (0 °C and 20 min, respectively). KOH pretreatment at such conditions targets to degrade the resistant starch of KW samples. Both acid and alkaline pretreatments were followed by addition of variable levels of enzyme dosage (0–3.6% v/v α-amylase and 0–3.2% v/v amyloglucosidase-AMG) at constant pH, temperature and time (pH = 5, T = 50 °C and t = 30 min, respectively). Based on our results, glucose concentration increased by ～300% after pretreatment with either acid or KOH in combination with enzymatic hydrolysis (2% HCl, 85 °C, 80 min, 0.1% α-amylase, AMG, and 1% KOH, 0 °C, 20 min, 1.1% α-amylase, 0.4% AMG) compared to raw (untreated) KW. Estimating the different Y_G yields at KW loading of 5%, an increase of 192% and 121% for total soluble monosugars and total soluble sugars, respectively, was succeeded compared to untreated KW. The effect of solids loading on the obtained sugar yields using the optimum conditions for thermo-chemical pretreatment followed by enzymatic hydrolysis was also tested resulting to 27.5% increase of the soluble glucose yield when half of the solids loading (2.5%) was used. A decrease of total soluble sugars yield by 32.2% was observed when solely acid hydrolysis at optimum conditions from our previous study was applied at 30% solids loading.     

Composition and production rate of pharmaceutical and chemical waste from Xanthi General Hospital in Greece

The objective of this work was to determine the composition and production rates of pharmaceutical and chemical waste produced by Xanthi General Hospital in Greece (XGH). This information is important to design and cost management systems for pharmaceutical and chemical waste, for safety and health considerations and for assessing environmental impact. A total of 233 kg pharmaceutical and 110 kg chemical waste was collected, manually separated and weighed over a period of five working weeks. The total production of pharmaceutical waste comprised 3.9% w/w of the total hazardous medical waste produced by the hospital. Total pharmaceutical waste was classified in three categories, vial waste comprising 51.1%, syringe waste with 11.4% and intravenous therapy (IV) waste with 37.5% w/w of the total. Vial pharmaceutical waste only was further classified in six major categories: antibiotics, digestive system drugs, analgesics, hormones, circulatory system drugs and "other". Production data below are presented as average (standard deviation in parenthesis). The unit production rates for total pharmaceutical waste for the hospital were 12.4 (3.90) g/patient/d and 24.6 (7.48) g/bed/d. The respective unit production rates were: (1) for vial waste 6.4 (1.6) g/patient/d and 13 (2.6) g/bed/d, (2) for syringe waste 1.4 (0.4) g/patient/d and 2.8 (0.8) g/bed/d and (3) for IV waste 4.6 (3.0) g/patient/d and 9.2 (5.9) g/bed/d. Total chemical waste was classified in four categories, chemical reagents comprising 18.2%, solvents with 52.3%, dyes and tracers with 18.2% and solid waste with 11.4% w/w of the total. The total production of chemical waste comprised 1.8% w/w of the total hazardous medical waste produced by the hospital. Thus, the sum of pharmaceutical and chemical waste was 5.7% w/w of the total hazardous medical waste produced by the hospital. The unit production rates for total chemical waste for the hospital were 5.8 (2.2) g/patient/d and 1.1 (0.4) g/exam/d. The respective unit production rates were: (1) for reagents 1.7 (2.4) g/patient/d and 0.3 (0.4) g/examination/d, (2) for solvents 248 (127) g/patient/d and 192 (101) g/examination/d, (3) for dyes and tracers 4.7 (1.4) g/patient/d and 2.5 (0.9) g/examination/d and (4) for solid waste 54 (28) g/patient/d and 42 (22) g/examination/d.     

Fumaric acid production using renewable resources from biodiesel and cane sugar production processes

The microbial production of fumaric acid by Rhizopus arrhizus NRRL 2582 has been evaluated using soybean cake from biodiesel production processes and very high polarity (VHP) sugar from sugarcane mills. Soybean cake was converted into a nutrient-rich hydrolysate via a two-stage bioprocess involving crude enzyme production via solid state fermentations (SSF) of either Aspergillus oryzae or R. arrhizus cultivated on soybean cake followed by enzymatic hydrolysis of soybean cake. The soybean cake hydrolysate produced using crude enzymes derived via SSF of R. arrhizus was supplemented with VHP sugar and evaluated using different initial free amino nitrogen (FAN) concentrations (100, 200, and 400 mg/L) in fed-batch cultures for fumaric acid production. The highest fumaric acid concentration (27.3 g/L) and yield (0.7 g/g of total consumed sugars) were achieved when the initial FAN concentration was 200 mg/L. The combination of VHP sugar with soybean cake hydrolysate derived from crude enzymes produced by SSF of A. oryzae at 200 mg/L initial FAN concentration led to the production of 40 g/L fumaric acid with a yield of 0.86 g/g of total consumed sugars. The utilization of sugarcane molasses led to low fumaric acid production by R. arrhizus, probably due to the presence of various minerals and phenolic compounds. The promising results achieved through the valorization of VHP sugar and soybean cake suggest that a focused study on molasses pretreatment could lead to enhanced fumaric acid production.     

Assessment of vulnerability of the eastern Cretan beaches (Greece) to sea level rise

Beaches are both sensitive and critical components of the coastal systems, as they are particularly vulnerable to environmental change (e.g., the sea level rise) and form valuable coastal ecosystems and economic resources. The objective of the present study has been to record the spatial characteristics and other attributes (e.g., topography, sediments and accessibility) of the 71 beaches of the E. Crete (Eastern Mediterranean) that are either already developed or have a reasonable development potential and assess their erosion risk under sea level rise. Beach retreats are predicted by ensembles of six cross-shore (1D) analytical and numerical morphodynamic models, set up/forced on the basis of collected/collated information and three sea level rise scenarios (0.26, 0.82 and 1.86 m); these retreats are then compared with the recorded maximum (dry) beach widths. Projections by the unified ensemble suggest that, in the case of a 0.26 m rise, 80 % of the examined beaches are to retreat by more than 20 and 16 % by more than 50 % of their maximum dry width. In the case of a 0.82 m rise, 72 % of the tested beaches are predicted to retreat by more than 50 % of their dry width and 21 % by a distance at least equal to their observed maximum dry widths. A sea level rise of 1.86 m represents a ‘doom’ scenario, as 75 % of the beaches are predicted to retreat by more than their maximum width. These results may be conservative, as other significant beach erosion factors (e.g., decreasing beach sediment supply) have not been considered.     

Combining Neural Network Models to Predict Spatial Patterns of Airborne Pollutant Accumulation in Soils around an Industrial Point Emission Source

Abstract

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R² values were found to be higher than 0.9 while, for the contributing individual networks, values for R² ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.     

Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.