Anionic metabolic profiling of urine from antibiotic-treated rats by capillary electrophoresis–mass spectrometry

A recently developed capillary electrophoresis (CE)-negative-ionisation mass spectrometry (MS) method was used to profile anionic metabolites in a microbial-host co-metabolism study. Urine samples from rats receiving antibiotics (penicillin G and streptomycin sulfate) for 0, 4, or 8 days were analysed. A quality control sample was measured repeatedly to monitor the performance of the applied CE-MS method. After peak alignment, relative standard deviations (RSDs) for migration time of five representative compounds were below 0.4 %, whereas RSDs for peak area were 7.9–13.5 %. Using univariate and principal component analysis of obtained urinary metabolic profiles, groups of rats receiving different antibiotic treatment could be distinguished based on 17 discriminatory compounds, of which 15 were downregulated and 2 were upregulated upon treatment. Eleven compounds remained down- or upregulated after discontinuation of the antibiotics administration, whereas a recovery effect was observed for others. Based on accurate mass, nine compounds were putatively identified; these included the microbial-mammalian co-metabolites hippuric acid and indoxyl sulfate. Some discriminatory compounds were also observed by other analytical techniques, but CE-MS uniquely revealed ten metabolites modulated by antibiotic exposure, including aconitic acid and an oxocholic acid. This clearly demonstrates the added value of CE-MS for nontargeted profiling of small anionic metabolites in biological samples.

Ensemble flood forecasting: a review.

Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.

Selection of potential probiotic lactic acid bacteria from fermented olives by in vitro tests

The present study aims to evaluate the probiotic potential of lactic acid bacteria (LAB) isolated from naturally fermented olives and select candidates to be used as probiotic starters for the improvement of the traditional fermentation process and the production of newly added value functional foods. Seventy one (71) lactic acid bacterial strains (17 Leuconostoc mesenteroides, 1 Ln. pseudomesenteroides, 13 Lactobacillus plantarum, 37 Lb. pentosus, 1 Lb. paraplantarum, and 2 Lb. paracasei subsp. paracasei) isolated from table olives were screened for their probiotic potential. Lb. rhamnosus GG and Lb. casei Shirota were used as reference strains. The in vitro tests included survival in simulated gastrointestinal tract conditions, antimicrobial activity (against Listeria monocytogenes, Salmonella Enteritidis, Escherichia coli O157:H7), Caco-2 surface adhesion, resistance to 9 antibiotics and haemolytic activity. Three (3) Lb. pentosus, 4 Lb. plantarum and 2 Lb. paracasei subsp. paracasei strains demonstrated the highest final population (>8 log cfu/ml) after 3 h of exposure at low pH. The majority of the tested strains were resistant to bile salts even after 4 h of exposure, while 5 Lb. plantarum and 7 Lb. pentosus strains exhibited partial bile salt hydrolase activity. None of the strains inhibited the growth of the pathogens tested. Variable efficiency to adhere to Caco-2 cells was observed. This was the same regarding strains' susceptibility towards different antibiotics. None of the strains exhibited β-haemolytic activity. As a whole, 4 strains of Lb. pentosus, 3 strains of Lb. plantarum and 2 strains of Lb. paracasei subsp. paracasei were found to possess desirable in vitro probiotic properties similar to or even better than the reference probiotic strains Lb. casei Shirota and Lb. rhamnosus GG. These strains are good candidates for further investigation both with in vivo studies to elucidate their potential health benefits and in olive fermentation processes to assess their technological performance as novel probiotic starters.

From farmers’ perspective : pesticide use and pest control

Many studies have shown that farmers in developing countries often overuse pesticides and do not adopt safety practices. Policies and interventions to promote a safer use of pesticides are often based on a limited understanding of the farmers’ own perspective of pesticide use. This often results in ineffective policies and the persistence of significant pesticide-related health and environmental problems, especially in developing countries. This chapter explores potentials and limitations of different approaches to study pesticide use in agriculture from the farmers’ perspective. In contrast to the reductionist and mono-disciplinary approaches often adopted, this chapter calls for integrative methodological approaches to provide a realistic and thorough understanding of the farmers’ perspective on pesticide use and illustrates the added value of such an approach with three case studies of pesticide use in Iran, India, and Colombia.

Incorporating toxicokinetics into an individual-based model for more realistic pesticide exposure estimates: A case study of the wood mouse

The potential risk of agricultural pesticides to mammals typically depends on internal concentrations within individuals, and these are determined by the amount ingested and by absorption, distribution, metabolism, and excretion (ADME). Pesticide residues ingested depend, amongst other things, on individual spatial choices which determine how much and when feeding sites and areas of pesticide application overlap, and can be calculated using individual-based models (IBMs). Internal concentrations can be calculated using toxicokinetic (TK) models, which are quantitative representations of ADME processes. Here we provide a population model for the wood mouse (Apodemus sylvaticus) in which TK submodels were incorporated into an IBM representation of individuals making choices about where to feed. This allows us to estimate the contribution of individual spatial choice and TK processes to risk. We compared the risk predicted by four IBMs: (i) “AllExposed-NonTK”: assuming no spatial choice so all mice have 100% exposure, no TK, (ii) “AllExposed-TK”: identical to (i) except that the TK processes are included where individuals vary because they have different temporal patterns of ingestion in the IBM, (iii) “Spatial-NonTK”: individual spatial choice, no TK, and (iv) “Spatial-TK”: individual spatial choice and with TK. The TK parameters for hypothetical pesticides used in this study were selected such that a conventional risk assessment would fail. Exposures were standardised using risk quotients (RQ; exposure divided by LD50 or LC50). We found that for the exposed sub-population including either spatial choice or TK reduced the RQ by 37–85%, and for the total population the reduction was 37–94%. However spatial choice and TK together had little further effect in reducing RQ. The reasons for this are that when the proportion of time spent in treated crop (PT) approaches 1, TK processes dominate and spatial choice has very little effect, and conversely if PT is small spatial choice dominates and TK makes little contribution to exposure reduction. The latter situation means that a short time spent in the pesticide-treated field mimics exposure from a small gavage dose, but TK only makes a substantial difference when the dose was consumed over a longer period. We concluded that a combined TK-IBM is most likely to bring added value to the risk assessment process when the temporal pattern of feeding, time spent in exposed area and TK parameters are at an intermediate level; for instance wood mice in foliar spray scenarios spending more time in crop fields because of better plant cover.

Social entrepreneurship in Sub-Saharan Africa

Responding to calls for a better understanding of the relationship between social enterprises and their environments, this article focuses on contextual influences on social entrepreneurship in sub-Saharan Africa. We identify four predominantly African contextual dimensions, i.e., acute poverty, informality, colonial history, and ethnic group identity, and explore their influence on the way social ventures perceive themselves and on their choice of activities. Our empirical study of 384 social enterprises from 19 sub-Saharan African countries suggests that ethnic group identity and high poverty levels influence both self-perception and activity choices, while the country’s colonial history only influences self-perception and informality has no significant influence on either. These findings point to the need to consider both self-perception and the choice of activities in defining social entrepreneurship. Our study also highlights the importance of African contextual dimensions for understanding social entrepreneurship, and underlines the added value of incorporating insights from African data into management research more broadly.

Probabilistic skill in ensemble seasonal forecasts

Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add, either to purely empirical statistical models or to simpler simulation models. An evaluation of seasonal probability forecasts from the Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction (DEMETER) and ENSEMBLES multi-model ensemble experiments is presented. Two particular regions are considered: Nino3.4 in the Pacific and the Main Development Region in the Atlantic; these regions were chosen before any spatial distribution of skill was examined. The ENSEMBLES models are found to have skill against the climatological distribution on seasonal time-scales. For models in ENSEMBLES that have a clearly defined predecessor model in DEMETER, the improvement from DEMETER to ENSEMBLES is discussed. Due to the long lead times of the forecasts and the evolution of observation technology, the forecast-outcome archive for seasonal forecast evaluation is small; arguably, evaluation data for seasonal forecasting will always be precious. Issues of information contamination from in-sample evaluation are discussed and impacts (both positive and negative) of variations in cross-validation protocol are demonstrated. Other difficulties due to the small forecast-outcome archive are identified. The claim that the multi-model ensemble provides a ‘better’ probability forecast than the best single model is examined and challenged. Significant forecast information beyond the climatological distribution is also demonstrated in a persistence probability forecast. The ENSEMBLES probability forecasts add significantly more information to empirical probability forecasts on seasonal time-scales than on decadal scales. Current operational forecasts might be enhanced by melding information from both simulation models and empirical models. Simulation models based on physical principles are sometimes expected, in principle, to outperform empirical models; direct comparison of their forecast skill provides information on progress toward that goal.

Biorefinery strategies for upgrading Distillers’ Dried Grains with Solubles (DDGS)

Distillers’ Dried Grains with Solubles (DDGS) is the major by-product of bioethanol and distillery plants. Due to its high content of proteins, water-soluble vitamins and minerals, DDGS has been long marketed as animal feed for livestock. EU legislation on liquid biofuels could raise the demand on bioethanol production in Europe, with a resulting increase in DDGS availability. DDGS contains a spectrum of complex organic macromolecules, particularly polysaccharides, in addition to proteins and vitamins, and its use as a starting raw material within a biomass-based biorefining strategy could lead to the development of multi-stream processes for the production of commodities, platform molecules or speciality chemicals, with concomitant economic benefits and waste reduction for bioethanol plants. The present review aims to outline the compositional characteristics of DDGS and evaluate its potential utilisation as a starting material for the production of added-value products. Parameters of influence on the chemical and physical characteristics of DDGS are discussed. Moreover, various pre-treatment strategies are outlined in terms of efficient DDGS fractionation into several added value streams. Additional processing steps for the production of medium and high added value compounds from DDGS are evaluated and their potential applications in the food and chemical industry sector are identified.

On systems and quality in typeface design

This text extends some ideas presented in a keynote lecture of the 5th Encontro de Tipografia conference, in Barcelos, Portugal, in November 2014. The paper discusses problems of identifying the location and encoding of design decisions, the implications of digital workflows for capturing knowledge generating through design practice, and the consequences of the transformation of production tools into commodities. It concludes with a discussion of the perception of added value in typeface design.

Decadal predictability of regional-scale peak winds over Europe using the Earth System Model of the Max-Planck-Institute for Meteorology

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

Effect of impurities in biodiesel-derived waste glycerol on the performance and feasibility of biotechnological processes

The rapid development of biodiesel production technology has led to the generation of tremendous quantities of glycerol wastes, as the main by-product of the process. Stoichiometrically, it has been calculated that for every 100 kg of biodiesel, 10 kg of glycerol are produced. Based on the technology imposed by various biodiesel plants, glycerol wastes may contain numerous kinds of impurities such as methanol, salts, soaps, heavy metals and residual fatty acids. This fact often renders biodiesel-derived glycerol unprofitable for further purification. Therefore, the utilization of crude glycerol though biotechnological means represents a promising alternative for the effective management of this industrial waste. This review summarizes the effect of various impurities-contaminants that are found in biodiesel-derived crude glycerol upon its conversion by microbial strains in biotechnological processes. Insights are given concerning the technologies that are currently applied in biodiesel production, with emphasis to the impurities that are added in the composition of crude glycerol, through each step of the production process. Moreover, extensive discussion is made in relation with the impact of the nature of impurities upon the performances of prokaryotic and eukaryotic microorganisms, during crude glycerol bioconversions into a variety of high added-value metabolic products. Finally, aspects concerning ways of crude glycerol treatment for the removal of inhibitory contaminants as reported in the literature are given and comprehensively discussed

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecastuncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called “How much are you prepared to pay for a forecast?”. The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydrometeorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants’ willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.