Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

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

Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE - Part 1: Simulating historical global burned area and fire regimes

Journal Article© 2014 Author(s). Fire is an important global ecological process that influences the distribution of biomes, with consequences for carbon, water, and energy budgets. Therefore it is impossible to appropriately model the history and future of the terrestrial ecosystems and the climate system without including fire. This study incorporates the process-based prognostic fire module SPITFIRE into the global vegetation model ORCHIDEE, which was then used to simulate burned area over the 20th century. Special attention was paid to the evaluation of other fire regime indicators such as seasonality, fire size and fire length, next to burned area. For 2001-2006, the simulated global spatial extent of fire agrees well with that given by satellite-derived burned area data sets (L3JRC, GLOBCARBON, GFED3.1), and 76-92% of the global burned area is simulated as collocated between the model and observation, depending on which data set is used for comparison. The simulated global mean annual burned area is 346 Mha yrg'1, which falls within the range of 287-384 Mha yrg'1 as given by the three observation data sets; and is close to the 344 Mha yrg'1 by the GFED3.1 data when crop fires are excluded. The simulated long-term trend and variation of burned area agree best with the observation data in regions where fire is mainly driven by climate variation, such as boreal Russia (1930-2009), along with Canada and US Alaska (1950-2009). At the global scale, the simulated decadal fire variation over the 20th century is only in moderate agreement with the historical reconstruction, possibly because of the uncertainties of past estimates, and because land-use change fires and fire suppression are not explicitly included in the model. Over the globe, the size of large fires (the 95th quantile fire size) is underestimated by the model for the regions of high fire frequency, compared with fire patch data as reconstructed from MODIS 500 m burned area data. Two case studies of fire size distribution in Canada and US Alaska, and southern Africa indicate that both number and size of large fires are underestimated, which could be related with short fire patch length and low daily fire size. Future efforts should be directed towards building consistent spatial observation data sets for key parameters of the model in order to constrain the model error at each key step of the fire modelling

Multiscale Topological Properties Of Functional Brain Networks During Motor Imagery After Stroke

In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise in regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions that increased in connection during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results increase our understanding of stroke-induced alterations in functional brain networks.Comment: Neuroimage, accepted manuscript (unedited version) available online 19-June-201

Scaling, Modeling, and Resilience of the Arctic Boreal Ecosystem

Arctic tundra landscapes are characterized by underlying permafrost sustained by extremely low average temperatures. These permafrost soils have been sequestering carbon for millennia, effectively locking it into the frozen ground. Currently, anthropogenic climate change, exacerbated by Arctic amplification, is driving rapid and unprecedented warming in the Arctic region putting the permafrost at risk of thaw. Thawing permafrost could release vast amounts of previously stored carbon as greenhouse gasses, driving the permafrost carbon feedback to accelerate warming. Unfortunately, the high spatial variability and complex feedback mechanisms limit our understanding of the connections and dynamics between above- and below-ground processes, and current models often fail to adequately capture permafrost C dynamics, a much-needed representation in climate predictions. First, we conducted a scaling exercise to evaluate the potential of novel remote sensing technologies to capture key tundra processes and reduce observational mismatches. Unoccupied aerial systems, airborne imaging spectroscopy, and satellite imagery were used to model the active layer and characterize key permafrost features. Medium spatial resolution image bands proved to be good predictors of average thaw depth, whereas high resolution imagery showed more contrast beneficial in complex landscapes like polygon tundra. And while average thaw depth predictions have proved valuable, when studying the resilience of the Arctic Boreal Region (ABR) it is important to observe local features at the matching scale. Second, airborne imaging spectroscopy allows for a region-wide mapping of spectral vegetation traits reflecting the variability in hydrology or nutrient availability. Key traits indicative of tundra functioning were selected and clustered to create a high-resolution spatial dataset reflecting above-ground tundra characteristics reflecting the below-ground permafrost conditions. Further analysis of the spectral traits revealed the local adaptation strategies to environmental conditions and disturbances. Lastly, based on the Landsat archive, yearly disturbances were mapped and disturbance trends by thermokarst zone were created. This study highlights the importance of landscape characteristics in analyzing and modeling disturbance trends. By leveraging each remote sensing data product, we enhanced the characterization of tundra landscapes. The scaling approach identified the benefits and pitfalls of each product for modeling, which is crucial for region-wide application. Remote sensing proved extremely valuable and provided insights into the historical and current state of the permafrost and allows for an improved prediction of future shifts in vegetation and ecosystem trajectories by improving the modeling of key vegetation parameters and understanding permafrost-vegetation interactions

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