Abstract: Typically, nitrification is a two-stage microbial process and is key in wastewater treatment and nutrient recovery from waste streams. Changes in salinity represent a major stress factor that can trigger response mechanisms, impacting the activity and the physiology of bacteria. Despite its pivotal biotechnological role, little information is available on the specific response of nitrifying bacteria to varying levels of salinity. In this study, synthetic communities of ammonia-oxidizing bacteria (AOB Nitrosomonas europaea and/or Nitrosomonas ureae) and nitrite-oxidizing bacteria (NOB Nitrobacter winogradskyi and/or Nitrobacter vulgaris) were tested at 5, 10, and 30 mS cm-1 by adding sodium chloride to the mineral medium (0, 40, and 200 mM NaCl, respectively). Ammonia oxidation activity was less affected by salinity than nitrite oxidation. AOB, on their own or in combination with NOB, showed no significant difference in the ammonia oxidation rate among the three conditions. However, N. winogradskyi improved the absolute ammonia oxidation rate of both N. europaea and N. ureae. N. winogradskyis nitrite oxidation rate decreased to 42% residual activity upon exposure to 30 mS cm-1, also showing a similar behavior when tested with Nitrosomonas spp. The nitrite oxidation rate of N. vulgaris, as a single species, was not affected when adding sodium chloride up to 30 mS cm-1, however, its activity was completely inhibited when combined with Nitrosomonas spp. in the presence of ammonium/ammonia. The proteomic analysis of a co-culture of N. europaea and N. winogradskyi revealed the production of osmolytes, regulation of cell permeability and an oxidative stress response in N. europaea and an oxidative stress response in N. winogradskyi, as a result of increasing the salt concentration from 5 to 30 mS cm-1. A specific metabolic response observed in N. europaea suggests the role of carbon metabolism in the production of reducing power, possibly to meet the energy demands of the stress response mechanisms, induced by high salinity. For the first time, metabolic modifications and response mechanisms caused by the exposure to salinity were described, serving as a tool toward controllability and predictability of nitrifying systems exposed to salt fluctuations.

Abstract: Electroporation is a non-thermal electromagnetic phenomenon widely used in medical diseases treatment. Different mathematical models of electroporation have been proposed in literature to study pore evolution in biological membranes. This paper presents a nonlinear dispersive multiphysic model of electroporation in irregular shaped biological cells in which the spatial and temporal evolution of the pores size is taken into account. The model solves Maxwell and asymptotic Smoluchowski equations and it describes the dielectric dispersion of cell media using a Debye-based relationship. Furthermore, the irregular cell shape has been modeled using the Gielis superformula. Taking into account the cell in mitosis phase, the electroporation process has been studied comparing the numerical results pertaining the model with variable pore radius with those in which the pore radius is supposed constant. The numerical analysis has been performed exposing the biological cell to a rectangular electric pulse having duration of 10 μs. The obtained numerical results highlight considerable differences between the two different models underling the need to include into the numerical algorithm the differential equation modeling the spatial and time evolution of the pores size.

Abstract: This work contributes to new and complementary experimental viscosity data for blended amine mixtures of aqueous N-methyldiethanolamine + 2-amino-2-methyl-1-propanol (MDEA + AMP) solutions with and without CO2 at different temperatures and mass fractions. For the unloaded MDEA + AMP solutions, measurements were conducted with total amine mass fractions ranging from 0.30 to 0.60. In the case of CO2-loaded aqueous MDEA + AMP solutions, experiments were performed at CO2 loadings ranging from 0.11 to 0.80. Proposed correlations were used to represent viscosity at the unloaded and CO2-loaded solutions within experimental uncertainty.

Abstract: Formation and aggregation of metal carboxylates (metal soaps) can degrade the appearance and integrity of oil paints, challenging efforts to conserve painted works of art. Endeavors to understand the root cause of metal soap formation have been hampered by the limited spatial resolution of Fourier transform infrared microscopy (mu-FTIR). We overcome this limitation using optical photothermal infrared spectroscopy (O-PTIR) and photothermal-induced resonance (PTIR), two novel methods that provide IR spectra with approximate to 500 and approximate to 10 nm spatial resolutions, respectively. The distribution of chemical phases in thin sections from the top layer of a 19th-century painting is investigated at multiple scales (mu-FTIR approximate to 10(2) mu m(3), O-PTIR approximate to 10(-1) mu m(3), PTIR approximate to 10(-5) mu m(3)). The paint samples analyzed here are found to be mixtures of pigments (cobalt green, lead white), cured oil, and a rich array of intermixed, small (often << 0.1 mu m(3)) zinc soap domains. We identify Zn stearate and Zn oleate crystalline soaps with characteristic narrow IR peaks (approximate to 1530-1558 cm(-1)) and a heterogeneous, disordered, water-permeable, tetrahedral zinc soap phase, with a characteristic broad peak centered at approximate to 1596 cm(-1). We show that the high signal-to-noise ratio and spatial resolution afforded by O-PTIR are ideal for identifying phase-separated (or locally concentrated) species with low average concentration, while PTIR provides an unprecedented nanoscale view of distributions and associations of species in paint. This newly accessible nanocompositional information will advance our knowledge of chemical processes in oil paint and will stimulate new art conservation practices.

Abstract: Geotourism is a sustainable type of tourism that focuses on the geological and geomorphological heritages of an area, and the associated cultural and biodiversity features. Though the popularity of geotourism is rapidly growing, research on the demand side, particularly on segmenting tourists to geosites and understanding their profiles, is limited. This obviously makes the designing of effective tourism policies that aim at developing geotourism sustainably very difficult. Hence, the main objectives of this study were to segment and profile tourists to geosites based on the benefits sought, and to show its implications for sustainable development of geotourism. With a survey of 415 tourists, this study clustered tourists to geosites in the southern Lake Tana region in Ethiopia based on the benefits sought. A factor-cluster method was applied to segment the tourists. The study identified four distinct segments: Activity-Nature Lovers, Culture Lovers, Nature-Culture Lovers, and Want-It-Alls. These segments differed in their demographic, trip, and behavioral characteristics. The findings implied that for sustainable development, destination managers and marketers need to customize their geotourism product development and marketing strategies based on the needs and characteristics of each market segment.

Abstract: The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

Abstract: A new parameterization for size resolved ultrafine particles (UFP) traffic emissions is proposed based on the results of PARTICULATES project (Samaras et al., 2005). It includes the emission factors from the Emission Inventory Guidebook (2006) (total number of particles, #/km/veh), the shape of the corresponding particle size distribution given in PARTICULATES and data for the traffic activity. The output of the model UFPEM (UltraFine Particle Emission Model) is a sum of continuous distributions of ultrafine particles emissions per vehicle type (passenger cars and heavy duty vehicles), fuel (petrol and diesel) and average speed representative for urban, rural and highway driving. The results from the parameterization are compared with measured total number of ultrafine particles and size distributions in a tunnel in Antwerp (Belgium). The measured UFP concentration over the entire campaign shows a close relation to the traffic activity. The modelled concentration is found to be lower than the measured in the campaign. The average emission factor from the measurement is 4.29E + 14 #/km/veh whereas the calculated is around 30% lower. A comparison of emission factors with literature is done as well and in overall a good agreement is found. For the size distributions it is found that the measured distributions consist of three modes Nucleation, Aitken and accumulation and most of the ultrafine particles belong to the Nucleation and the Aitken modes. The modelled Aitken mode (peak around 0.040.05 μm) is found in a good agreement both as amplitude of the peak and the number of particles whereas the modelled Nucleation mode is shifted to smaller diameters and the peak is much lower that the observed. Time scale analysis shows that at 300 m in the tunnel coagulation and deposition are slow and therefore neglected. The UFPEM emission model can be used as a source term in dispersion models.

Abstract: The use of a photocatalyst (photosensitizer) which produces singlet oxygen instead of enzymes for oxidizing analytes creates opportunities for designing cost-efficient and sensitive photoelectrochemical sensors. We report that perfluoroisopropyl-substituted zinc phthalocyanine (F64PcZn) interacts specifically with a complex phenolic compound, the antibiotic rifampicin (RIF), but not with hydroquinone or another complex phenolic compound, the antibiotic doxycycline. The specificity is imparted by the selective preconcentration of RIF in the photocatalytic layer, as revealed by electrochemical and optical measurements, complemented by molecular modeling that confirms the important role of a hydrophobic cavity formed by the iso-perfluoropropyl groups of the photocatalyst. The preconcentration effect favorably enhances the RIF photoelectrochemical detection limit as well as sensitivity to nanomolar (ppb) concentrations, LOD = 7 nM (6 ppb) and 2.8 A.M-1.cm(-2), respectively. The selectivity to RIF, retained in the photosensitizer layer, is further enhanced by the selective removal of all unretained phenols via simple washing of the electrodes with pure buffer. The utility of the sensor for analyzing municipal wastewater was demonstrated. This first demonstration of enhanced selectivity and sensitivity due to intrinsic interactions of a molecular photocatalyst (photosensitizer) with an analyte, without use of a biorecognition element, may allow the design of related, robust, simple, and viable sensors.