Parameter identification of the fermentative production of fructo-oligosaccharides by Aureobasidium pullulans

In this study, a mathematical model for the production of Fructo-oligosaccharides (FOS) by Aureobasidium pullulans is developed. This model contains a relatively large set of unknown parameters, and the identification problem is analyzed using simulation data, as well as experimental data. Batch experiments were not sufficiently informative to uniquely estimate all the unknown parameters, thus, additional experiments have to be achieved in fed-batch mode to supplement the missing information. © 2015 IEEE.funded by the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy Office. The authors thank the financial support from the F.R.S.-FNRS, the Belgium National Fund for the Scientific Research (Research Project 24643.08). C. Nobre thanks the Fundação para a Ciência e Tecnologia for the strategic funding of UID/BIO/04469/2013 uni

Fructo-oligosaccharides separation and purification by simulated moving bed chromatography

The interest on oligosaccharides such as fructo-oligosaccharides (FOS) has strongly increased in recent years for food and pharmaceutical applications, mainly due to their improved technological and functional properties. FOS can be produced by fermentative processes from sucrose, and can be found in mixture with other mono- and di-saccharides and salts, at the end of the process [1]. Unlike FOS, the small saccharides (SGF), namely fructose, glucose and sucrose in the mixture, are known to be cariogenic, caloric and do not present prebiotic activity. The purification of FOS from the other sugars can represent and important increment on the economic value of the final product, which can be further used in diabetic and dietetic food [2]. Different strategies have been developed for this purpose, including microbial treatment [3], ultra and nano-filtration, activated charcoal systems [4], or ion-exchange chromatography [5]. Ion exchange resins may be then used in batch or continuous chromatographic processes, as Simulated Moving Bed (SMB) chromatography, to purify sugars. A screening of different commercial resins was previously done in order to select the most suitable to separate the oligosaccharides [5]. The resin Diaion 535Ca showed an increased recovery yield and purity of FOS (92 and 90%, respectively). In the present work, the separation process was implemented in the SMB, using the selected resin, namely. Equilibrium adsorption isotherms were determined by the Retention Time Method (RTM), for each single sugar. The resin was afterwards packed in eight SMB columns, and tested in the pilot plant. Different operation parameters, including switching time, extra time, internal flow-rates and operating pump flow-rates for feed, raffinate, desorbent, eluent and recycling streams, were tested in the plant. The separation of fructose from glucose and FOS from the SGF was evaluated. Firstly, the separation of a binary sugar mixture of fructose/sucrose (~ 50/50%) was performed followed by the separation of FOS from a fermentative broth. Fructose was purified from 53 to 76% and sucrose from 47 to 77%. FOS and SGF were purified from 50 to 67%. The implementation of UV detectors between the SMB columns allowed following the sugar concentration profile online during the separation process. The accurate selection of the operating parameters was made using the concentration signal obtained and showed to be a crucial step for an improved separation

Microbial treatment approaches for high-purity fructo-oligosaccharides production

The production of high-purity fructooligosaccharides (FOS), known as prebiotics, by sucrose fermentation using whole microbial cells has been recently explored. At the end of the fermentation process, FOS are present in mixture with small saccharides that are known to have an inhibitory effect of transfructosylating enzymes and to decrease the prebiotic activity of the mixture. This issue can be overcome by reducing the small saccharides present in FOS broth, which can be done using a combined microbial treatment, among others, improving as well the further purification of FOS by Simulated Moving Bed (SMB) chromatography. The main goal of this work was the use of combined microbial treatment approaches to improve FOS production and enhance a high purity FOS content. Aureobasidium pullulans and Saccharomyces cerevisiae were used combined to produce FOS and reduce the small sugars in the culture, respectively. FOS-producing microorganism was used free, immobilized to a non-conventional carrier or encapsulated in Ca-alginate beads, in mixture with the non-oligosaccharides consuming microorganism, free or encapsulated in Ca-alginate beads. A factorial design, considering three different variables, was performed, to select the microbial treatment approach through which increased FOS levels and yields can be obtained. The 38 assays were performed in shaken-flasks and the most suitable one was scaled-up to a 3L bioreactor. The inoculation time of S. cerevisiae showed to be the most relevant variable for FOS production, and the use of immobilized A. pullulans, mixed with encapsulated S. cerevisiae inoculated after 20h of fermentation, was the best combination, with statistical relevance (p<0.01), to obtain enhanced FOS concentration, percentage in the medium, yield and productivity. Results in bioreactor showed a higher fermentation time (20 to 25h) needed to obtain an increased maximal production of FOS (around 132 g.L-1) and yielded 0.70 ± 0.05 g of FOS per gram of initial sucrose. Also, the approach selected improved the percentage of FOS in the medium throughout the fermentation time, providing a pre-purified broth, with lower levels of mono-saccharides for further purification by SMB

Recent global and regional trends in burned area and their compensating environmental controls

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite- observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales

An overview of ADSL Homed Nepenthes Honeypots In Western Australia

This paper outlines initial analysis from research in progress into ADSL homed Nepenthes honeypots. One of the Nepenthes honeypots prime objective in this research was the collection of malware for analysis and dissection. A further objective is the analysis of risks that are circulating within ISP networks in Western Australian. What differentiates Nepenthes from many traditional honeypot designs it that is has been engineered from a distributed network philosophy. The program allows distribution of results across a network of sensors and subsequent aggregation of malware statistics readily within a large network environment

Air quality impacts of European wildfire emissions in a changing climate

Wildfires are not only a threat to human property and a vital element of many ecosystems, but also an important source of air pollution. In this study, we first review the available evidence for a past or possible future climate-driven increase in wildfire emissions in Europe. We then introduce an ensemble of model simulations with a coupled wildfire–dynamic-ecosystem model, which we combine with published spatial maps of both wildfire and anthropogenic emissions of several major air pollutants to arrive at air pollutant emission projections for several time slices during the 21st century. The results indicate moderate wildfire-driven emission increases until 2050, but there is a possibility of large increases until the last decades of this century at high levels of climate change. We identify southern and north-eastern Europe as potential areas where wildfires may surpass anthropogenic pollution sources during the summer months. Under a scenario of high levels of climate change (Representative Concentration Pathway, RCP, 8.5), emissions from wildfires in central and northern Portugal and possibly southern Italy and along the west coast of the Balkan peninsula are projected to reach levels that could affect annual mean particulate matter concentrations enough to be relevant for meeting WHO air quality targets

TEM analysis of apatite surface layers observed on zinc based glass polyalkenoate cements

peer-reviewedGlass polyalkenoate cements (GPCs) are acid base cements formed by the reaction of an aqueous solution of polyalkenoic acid, usually polyacrylic acid (PAA) with an acid degradable aluminosilicate glass. The result of the reaction is cement consisting of reacted and unreacted glass particles embedded in a polysalt matrix. In addition to these conventional GPCs, aluminium free glass polyalkenoate cements based on zinc silicate glasses (Zn-GPCs) exhibit significant potential as bone cements for several reasons. Primarily, they are formulated without the inclusion of aluminium (Al) [1] in the glass phase and thus eliminate clinical complications arising from the release of the Al3+ ion from the cement in vivo. Such complications have, in the past, included aluminium induced encephalopathy [2-5] and defective mineralisation of cancellous bone [6]. Secondly, Zn-GPCs set without a significant evolution of heat, when compared with commercial bone cements such as Spineplex ® (Stryker, Limerick, Ireland). Finally, these materials can be tailored to release clinically beneficial ions into surrounding tissues [7]. In addition to Zn, these cements have been synthesized to contain strontium (Sr) [8, 9]. Both Sr and Zn inhibit osteoclastic turnover and promote osteoblastic turnover, resulting in increased bone strength and decreased fracture risk [10-14].Acceptedpeer-reviewe

The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols

The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models

The status and challenge of global fire modelling

This is the final version of the article. Available from European Geosciences Union / Copernicus Publications via the DOI in this record.The discussion paper version of this article was published in Biogeosciences Discussions on 25 January 2016 and is in ORE at http://hdl.handle.net/10871/34451Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.Stijn Hantson and Almut Arneth acknowledge support by the EU FP7 projects BACCHUS (grant agreement no. 603445) and LUC4C (grant agreement no. 603542). This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF), through the Helmholtz Association and its research programme ATMO, and the HGF Impulse and Networking fund. The MC-FIRE model development was supported by the global change research programmes of the Biological Resources Division of the US Geological Survey (CA 12681901,112-), the US Department of Energy (LWT-6212306509), the US Forest Service (PNW96–5I0 9 -2-CA), and funds from the Joint Fire Science Program. I. Colin Prentice is supported by the AXA Research Fund under the Chair Programme in Biosphere and Climate Impacts, part of the Imperial College initiative Grand Challenges in Ecosystems and the Environment. Fang Li was funded by the National Natural Science Foundation (grant agreement no. 41475099 and no. 2010CB951801). Jed O. Kaplan was supported by the European Research Council (COEVOLVE 313797). Sam S. Rabin was funded by the National Science Foundation Graduate Research Fellowship, as well as by the Carbon Mitigation Initiative. Allan Spessa acknowledges funding support provided by the Open University Research Investment Fellowship scheme. FireMIP is a non-funded community initiative and participation is open to all. For more information, contact Stijn Hantson ([email protected])