Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Survival of Lactobacillus plantarum in model solutions and fruit juices

The aim of the work was to study the survival of Lactobacillus plantarum NCIMB 8826 in model solutions and develop a mathematical model describing its dependence on pH, citric acid and ascorbic acid. A Central Composite Design (CCD) was developed studying each of the three factors at five levels within the following ranges, i.e., pH (3.0-4.2), citric acid (6-40 g/L), and ascorbic acid (100-1000 mg/L). In total, 17 experimental runs were carried out. The initial cell concentration in the model solutions was approximately 1 × 10(8)CFU/mL; the solutions were stored at 4°C for 6 weeks. Analysis of variance (ANOVA) of the stepwise regression demonstrated that a second order polynomial model fits well the data. The results demonstrated that high pH and citric acid concentration enhanced cell survival; one the other hand, ascorbic acid did not have an effect. Cell survival during storage was also investigated in various types of juices, including orange, grapefruit, blackcurrant, pineapple, pomegranate, cranberry and lemon juice. The model predicted well the cell survival in orange, blackcurrant and pineapple, however it failed to predict cell survival in grapefruit and pomegranate, indicating the influence of additional factors, besides pH and citric acid, on cell survival. Very good cell survival (less than 0.4 log decrease) was observed after 6 weeks of storage in orange, blackcurrant and pineapple juice, all of which had a pH of about 3.8. Cell survival in cranberry and pomegranate decreased very quickly, whereas in the case of lemon juice, the cell concentration decreased approximately 1.1 logs after 6 weeks of storage, albeit the fact that lemon juice had the lowest pH (pH~2.5) among all the juices tested. Taking into account the results from the compositional analysis of the juices and the model, it was deduced that in certain juices, other compounds seemed to protect the cells during storage; these were likely to be proteins and dietary fibre In contrast, in certain juices, such as pomegranate, cell survival was much lower than expected; this could be due to the presence of antimicrobial compounds, such as phenolic compounds.

Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water

We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KM-GAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KM-GAP is based on the PRA model framework (Pöschl-Rudich-Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modelled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmo- spheric aerosols and clouds. In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270 K is close to unity. Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for eðcient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone.

Style analysis in real estate markets: beyond the sectors and regions dichotomy

While style analysis has been studied extensively in equity markets, applications of this valuable tool for measuring and benchmarking performance and risk in a real estate context are still relatively new. Most previous real estate studies on this topic have identified three investment categories (rather than styles): sectors, administrative regions and economic regions. However, the low explanatory power reveals the need to extend this analysis to other investment styles. We identify four main real estate investment styles and apply a multivariate model to randomly generated portfolios to test the significance of each style in explaining portfolio returns. Results show that significant alpha performance is significantly reduced when we account for the new investment styles, with small vs. big properties being the dominant one. Secondly, we find that the probability of obtaining alpha performance is dependent upon the actual exposure of funds to style factors. Finally we obtain that both alpha and systematic risk levels are linked to the actual characteristics of portfolios. Our overall results suggest that it would be beneficial for real estate fund managers to use these style factors to set benchmarks and to analyze portfolio returns.

Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water

We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KMGAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KMGAP is based on the PRA model framework (P¨oschl-Rudich- Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modeled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system and the computational constraints, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmospheric aerosols and clouds. In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270K is close to unity (Winkler et al., 2006). Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for efficient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone.

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

A model of the hemodynamic response and oxygen delivery to brain

A recent nonlinear system by Friston et al. (2000. NeuroImage 12: 466–477) links the changes in BOLD response to changes in neural activity. The system consists of five subsystems, linking: (1) neural activity to flow changes; (2) flow changes to oxygen delivery to tissue; (3) flow changes to changes in blood volume and venous outflow; (4) changes in flow, volume, and oxygen extraction fraction to deoxyhemoglobin changes; and finally (5) volume and deoxyhemoglobin changes to the BOLD response. Friston et al. exploit, in subsystem 2, a model by Buxton and Frank coupling flow changes to changes in oxygen metabolism which assumes tissue oxygen concentration to be close to zero. We describe below a model of the coupling between flow and oxygen delivery which takes into account the modulatory effect of changes in tissue oxygen concentration. The major development has been to extend the original Buxton and Frank model for oxygen transport to a full dynamic capillary model making the model applicable to both transient and steady state conditions. Furthermore our modification enables us to determine the time series of CMRO2 changes under different conditions, including CO2 challenges. We compare the differences in the performance of the “Friston system” using the original model of Buxton and Frank and that of our model. We also compare the data predicted by our model (with appropriate parameters) to data from a series of OIS studies. The qualitative differences in the behaviour of the models are exposed by different experimental simulations and by comparison with the results of OIS data from brief and extended stimulation protocols and from experiments using hypercapnia.

Impact of vegetation and preferential source areas on global dust aerosol: results from a model study

[1] We present a model of the dust cycle that successfully predicts dust emissions as determined by land surface properties, monthly vegetation and snow cover, and 6-hourly surface wind speeds for the years 1982–1993. The model takes account of the role of dry lake beds as preferential source areas for dust emission. The occurrence of these preferential sources is determined by a water routing and storage model. The dust source scheme also explicitly takes into account the role of vegetation type as well as monthly vegetation cover. Dust transport is computed using assimilated winds for the years 1987–1990. Deposition of dust occurs through dry and wet deposition, where subcloud scavenging is calculated using assimilated precipitation fields. Comparison of simulated patterns of atmospheric dust loading with the Total Ozone Mapping Spectrometer satellite absorbing aerosol index shows that the model produces realistic results from daily to interannual timescales. The magnitude of dust deposition agrees well with sediment flux data from marine sites. Emission of submicron dust from preferential source areas are required for the computation of a realistic dust optical thickness. Sensitivity studies show that Asian dust source strengths are particularly sensitive to the seasonality of vegetation cover.

Output sustainability to exogenous and endogenous shocks: evidence from emerging economies

This paper studies the impact of exogenous and endogenous shocks (exogenous shock is used interchangeably with external shock; endogenous shock is used interchangeably with domestic shock) on output fluctuations in post-communist countries during the 2000s. The first part presents the analytical framework and formulates a research hypothesis. The second part presents vector autoregressive estimation and analysis model proposed by Pesaran (2004) and Pesaran and Smith (2006) that relates bank real lending, the cyclical component of output and spreads and accounts for cross-sectional dependence (CD) across the countries. Impulse response functions show that exogenous positive shock lead to a drop in output sustainability for 9 over 12 Central Eastern European countries and Russia, when the endogenous shock is mild and ambiguous. Moreover, the effect of exogenous shock is more significant during the crises. Variance decompositions show that exogenous shock in the aftermath of crisis had a substantial impact on economic activity of emerging economies.

SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

Fast and slow precipitation responses to individual climate forcers: a PDRMIP multi-model study

Precipitation is expected to respond differently to various drivers of anthropogenic climate change. We present the first results from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where nine global climate models have perturbed CO2, CH4, black carbon, sulfate, and solar insolation. We divide the resulting changes to global mean and regional precipitation into fast responses that scale with changes in atmospheric absorption and slow responses scaling with surface temperature change. While the overall features are broadly similar between models, we find significant regional intermodel variability, especially over land. Black carbon stands out as a component that may cause significant model diversity in predicted precipitation change. Processes linked to atmospheric absorption are less consistently modeled than those linked to top-of-atmosphere radiative forcing. We identify a number of land regions where the model ensemble consistently predicts that fast precipitation responses to climate perturbations dominate over the slow, temperature-driven responses.

Stromal-derived factor-1 alpha (CXCL12) levels increase in periodontal disease

Background: The CXC chemokine receptor 4 (CXCR4) and its ligand, stromal cell-derived factor-1 (SDF-1 alpha or CXC chemokine ligand 12) are involved in the trafficking of leukocytes into and out of extravascular tissues. The purpose of this study was to determine whether SDF-1 alpha secreted by host cells plays a role in recruiting inflammatory cells into the periodontia during local inflammation. Methods: SDF-1 alpha levels were determined by enzyme-linked immunosorbent assay in gingival crevicular fluid (GCF) of 24 individuals with periodontitis versus healthy individuals in tissue biopsies and in a preclinical rat model of Porphyromonas gingivalis lipopolysaccharide-induced experimental bone loss. Neutrophil chemotaxis assays were also used to evaluate whether SDF-1 alpha plays a role in the recruitment of host cells at periodontal lesions. Results: Subjects with periodontal disease had higher levels of SDF-1 alpha in their GCF compared to healthy subjects. Subjects with periodontal disease who underwent mechanical therapy demonstrated decreased levels of SDF-1 alpha. Immunohistologic staining showed that SDF-1 alpha and CXCR4 levels were elevated in samples obtained from periodontally compromised individuals. Similar results were observed in the rodent model. Neutrophil migration was enhanced in the presence of SDF-1 alpha, mimicking immune cell migration in periodontal lesions. Conclusions: SDF-1 alpha may be involved in the immune defense pathway activated during periodontal disease. Upon the development of diseased tissues, SDF-1 alpha levels increase and may recruit host defensive cells into sites of inflammation. These studies suggest that SDF-1 alpha may be a useful biomarker for the identification of periodontal disease progression.

Role of SOCS-1 Gene on Melanoma Cell Growth and Tumor Development

Melanoma is the most aggressive form of skin cancer, and its incidence has increased dramatically over the years. The murine B16F10 melanoma in syngeneic C57Bl/6 mice has been used as a highly aggressive model to investigate tumor development. Presently, we demonstrate in the B16F10-Nex2 subclone that silencing of SOCS-1, a negative regulator of Jak/Stat pathway, leads to reversal of the tumorigenic phenotype and inhibition of melanoma cell metastasis. SOCS-1 silencing with short hairpin RNA affected tumor growth and cell cycle regulation with arrest at the S phase with large-sized nuclei, reduced cell motility, and decreased melanoma cell invasion through Matrigel. A clonogenic assay showed that SOCS-1 acted as a modulator of resistance to anoikis. In addition, down-regulation of SOCS-1 decreased the expression of epidermal growth factor receptor ( mainly the phosphorylated-R), Ins-R alpha, and fibroblast growth factor receptor. In vivo, silencing of SOCS-1 inhibited subcutaneous tumor growth and metastatic development in the lungs. Because SOCS-1 is expressed in most melanoma cell lines and bears a relation with tumor invasion, thickness, and stage of disease, the present results on the effects of SOCS-1 silencing in melanoma suggest that this regulating protein can be a target of cancer therapy.

Structural modeling and mutational analysis of yeast eukaryotic translation initiation factor 5A reveal new critical residues and reinforce its involvement in protein synthesis

Eukaryotic translation initiation factor 5A (eIF5A) is a protein that is highly conserved and essential for cell viability. This factor is the only protein known to contain the unique and essential amino acid residue hypusine. This work focused on the structural and functional characterization of Saccharomyces cerevisiae eIF5A. The tertiary structure of yeast eIF5A was modeled based on the structure of its Leishmania mexicana homologue and this model was used to predict the structural localization of new site-directed and randomly generated mutations. Most of the 40 new mutants exhibited phenotypes that resulted from eIF-5A protein-folding defects. Our data provided evidence that the C-terminal alpha-helix present in yeast eIF5A is an essential structural element, whereas the eIF5A N-terminal 10 amino acid extension not present in archaeal eIF5A homologs, is not. Moreover, the mutants containing substitutions at or in the vicinity of the hypusine modification site displayed nonviable or temperature-sensitive phenotypes and were defective in hypusine modification. Interestingly, two of the temperature-sensitive strains produced stable mutant eIF5A proteins - eIF5A(K56A) and eIF5A(Q22H,L93F)- and showed defects in protein synthesis at the restrictive temperature. Our data revealed important structural features of eIF5A that are required for its vital role in cell viability and underscored an essential function of eIF5A in the translation step of gene expression.

Swedish translation and psychometric testing of the safety attitudes questionnaire (operating room version)

Background: Tens of millions of patients worldwide suffer from avoidable disabling injuries and death every year. Measuring the safety climate in health care is an important step in improving patient safety. The most commonly used instrument to measure safety climate is the Safety Attitudes Questionnaire (SAQ). The aim of the present study was to establish the validity and reliability of the translated version of the SAQ. Methods: The SAQ was translated and adapted to the Swedish context. The survey was then carried out with 374 respondents in the operating room (OR) setting. Data was received from three hospitals, a total of 237 responses. Cronbach's alpha and confirmatory factor analysis (CFA) was used to evaluate the reliability and validity of the instrument. Results: The Cronbach's alpha values for each of the factors of the SAQ ranged between 0.59 and 0.83. The CFA and its goodness-of-fit indices (SRMR 0.055, RMSEA 0.043, CFI 0.98) showed good model fit. Intercorrelations between the factors safety climate, teamwork climate, job satisfaction, perceptions of management, and working conditions showed moderate to high correlation with each other. The factor stress recognition had no significant correlation with teamwork climate, perception of management, or job satisfaction. Conclusions: Therefore, the Swedish translation and psychometric testing of the SAQ (OR version) has good construct validity. However, the reliability analysis suggested that some of the items need further refinement to establish sound internal consistency. As suggested by previous research, the SAQ is potentially a useful tool for evaluating safety climate. However, further psychometric testing is required with larger samples to establish the psychometric properties of the instrument for use in Sweden.