| 1. |
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| 2. |
- Braniste, Viorica, et al.
(författare)
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The gut microbiota influences blood-brain barrier permeability in mice
- 2014
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Ingår i: Science Translational Medicine. - : American Association for the Advancement of Science. - 1946-6234 .- 1946-6242. ; 6:263, s. 263ra158-
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Tidskriftsartikel (refereegranskat)abstract
- Pivotal to brain development and function is an intact blood-brain barrier (BBB), which acts as a gatekeeper to control the passage and exchange of molecules and nutrients between the circulatory system and the brain parenchyma. The BBB also ensures homeostasis of the central nervous system (CNS). We report that germ-free mice, beginning with intrauterine life, displayed increased BBB permeability compared to pathogen-free mice with a normal gut flora. The increased BBB permeability was maintained in germ-free mice after birth and during adulthood and was associated with reduced expression of the tight junction proteins occludin and claudin-5, which are known to regulate barrier function in endothelial tissues. Exposure of germ-free adult mice to a pathogen-free gut microbiota decreased BBB permeability and up-regulated the expression of tight junction proteins. Our results suggest that gut microbiota-BBB communication is initiated during gestation and propagated throughout life.
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| 3. |
- Comley, Robert A., et al.
(författare)
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A Comparison of Gray Matter Density in Restless Legs Syndrome Patients and Matched Controls Using Voxel-Based Morphometry
- 2012
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Ingår i: Journal of Neuroimaging. - : WILEY-BLACKWELL. - 1051-2284 .- 1552-6569. ; 22:1, s. 28-32
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND Restless legs syndrome (RLS) is a common neurological disorder the pathophysiology of which is incompletely understood. Four studies have examined structural differences between the brains of RLS patients and healthy controls, using voxel-based morphometry (VBM). All 4 studies have provided different results. METHODS Optimized VBM was used to search for structural differences in gray matter density. Sixteen RLS patients naive to dopaminergic drugs and 16 age-and sex-matched controls received structural T1-weighted MR scans. Structural data were analyzed using FSL-VBM. RESULTS No difference in gray matter density was detected between the two groups (voxel-wise significance: no significant voxels at P = .89 (whole brain Family Wise Error (FWE) corrected); no significant voxels at P < .05 (whole brain False Discovery Rate (FDR) corrected; smallest achievable FDR threshold .99). CONCLUSION/DISCUSSION The present study did not replicate (confirm) previous findings of structural brain changes in RLS, but instead supported the findings of a recent study showing a lack of gray matter alteration in an elderly RLS population. More specifically, the results do not support neuronal loss as an underlying disease mechanism in RLS. Potential limitations in the application of VBM are also discussed.
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| 4. |
- Gong, Lebing, et al.
(författare)
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Global-scale river routing-an efficient time-delay algorithm based on HydroSHEDS high-resolution hydrography
- 2011
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Ingår i: Hydrological Processes. - : Wiley. - 0885-6087 .- 1099-1085. ; 25:7, s. 1114-1128
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Tidskriftsartikel (refereegranskat)abstract
- Coupling of global hydrologic and atmospheric models is difficult because of the highly nonlinear hydrological processes to be integrated at large scales. Aggregation of high-resolution data into low-resolution spatial distribution functions is one way to preserve information and account for the nonlinearity. We used HydroSHEDS, presently the most highly resolved (3 '') global hydrography available, to provide accurate control on global river routing through a computationally efficient algorithm. The high resolution of HydroSHEDS allowed discrimination of river-channel pixels, and time-delay distributions were calculated for all such pixels. The distributions were aggregated into network-response functions (NRFs) for each low-resolution cell using an algorithm originally developed for the 1-km-resolution HYDRO1k hydrography. The large size of HydroSHEDS required a modification in algorithm to maintain computational efficiency. The new algorithm was tested with a high-quality local and a more uncertain global weather dataset to identify whether improved routing would provide a gain when weather data quality was limiting. The routing was coupled to the WASMOD-M runoff-generation model to evaluate discharge from the Dongjiang River and the Willamette River basins. The HydroSHEDS-based routing, compared with the HYDRO1k-based routing, provided a small gain in model efficiency, for local and global weather data and for both test basins. The HydroSHEDS-based routing, contrary to the HYDRO1k-based routing, provided physically realistic wave velocities. The most stable runoff-generation parameter values were achieved when HydroSHEDS was used to derive the NRFs. Routing was computed in two steps: first, a preparatory calculation which was a one-time effort and second, the routing during each simulation. The computational efficiency was four to five orders of magnitude better for the simulation step than that for the preparatory step.
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| 5. |
- Gong, Lebing
(författare)
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Large-scale Runoff Generation and Routing : Efficient Parameterisation using High-resolution Topography and Hydrography
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Water has always had a controlling influence on the earth’s evolution. Understanding and modelling the large-scale hydrological cycle is important for climate prediction and water-resources studies. In recent years large-scale hydrological models, including the WASMOD-M evaluated in the thesis, have increasingly become a main assessment tool for global water resources.The monthly version of WASMOD-M, the starting point of the thesis, revealed restraints imposed by limited hydrological and climate data quality and the need to reduce model-structure uncertainties. The model simulated the global water balance with a small volume error but was less successful in capturing the dynamics. In the last years, global high-quality, high-resolution topographies and hydrographies have become available. The main thrust of the thesis was the development of a daily WASMOD-M making use of these data to better capture the global water dynamics and to parameterise local non-linear processes into the large-scale model. Scale independency, parsimonious model structure, and computational efficiency were main concerns throughout the model development.A new scale-independent routing algorithm, named NRF for network-response function, using two aggregated high-resolution hydrographies, HYDRO1k and HydroSHEDS, was developed and tested in three river basins with different climates in China and North America. The algorithm preserves the spatially distributed time-delay information in the form of simple network-response functions for any low-resolution grid cell in a large-scale hydrological model.A distributed runoff-generation algorithm, named TRG for topography-derived runoff generation, was developed to represent the highly non-linear process at large scales. The algorithm, when inserted into the daily WASMOD-M and tested in same three basins, led to the same or a slightly improved performance compared to a one-layer VIC model, with one parameter less to be calibrated. The TRG algorithm also offered a more realistic spatial pattern for runoff generation.The thesis identified significant improvements in model performance when 1) local instead of global climate data were used, and 2) when the scale-independent NRF routing algorithm was used instead of a traditional storage-based routing algorithm. In the same time, spatial resolution of climate input and choice of high-resolution hydrography have secondary effects on model performance.Two high-resolution topographies and hydrographies were used and compared, and new techniques were developed to aggregate their information for use at large scales. The advantages and numerical efficiency of feeding high-resolution information into low-resolution global models were highlighted.
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| 6. |
- Guerrero, José-Luis, 1977-, et al.
(författare)
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Exploring the hydrological robustness of model-parameter values with alpha shapes
- 2013
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Ingår i: Water resources research. - : American Geophysical Union (AGU). - 0043-1397 .- 1944-7973. ; 49:10, s. 6700-6715
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of parameter values in hydrological models has gradually moved from subjective, trial-and-error methods into objective estimation methods. Translation of nature's complexity to bit operations is an uncertain process as a result of data errors, epistemic gaps, computational deficiencies, and other limitations, and relies on calibration to fit model output to observed data. The robustness of the calibrated parameter values to these types of uncertainties is therefore an important concern. In this study, we investigated how the hydrological robustness of the model-parameter values varied within the geometric structure of the behavioral (well-performing) parameter space with a depth function based on α shapes and an in-depth posterior performance analysis of the simulations in relation to the observed discharge uncertainty. The α shape depth is a nonconvex measure that may provide an accurate and tight delimitation of the geometric structure of the behavioral space for both unimodal and multimodal parameter-value distributions. WASMOD, a parsimonious rainfall-runoff model, was applied to six Honduran and one UK catchment, with differing data quality and hydrological characteristics. Model evaluation was done with two performance measures, the Nash-Sutcliffe efficiency and one based on flow-duration curves. Deep parameter vectors were in general found to be more hydrologically robust than shallow ones in the analyses we performed; model-performance values increased with depth, deviations to the observed data for the high-flow aspects of the hydrograph generally decreased with increasing depth, deep parameter vectors generally transferred in time with maintained high performance values, and the model had a low sensitivity to small changes in the parameter values. The tight delimitation of the behavioral space provided by the α shapes depth function showed a potential to improve the efficiency of calibration techniques that require further exploration. For computational reasons only a three-parameter model could be used, which limited the applicability of this depth measure and the conclusions drawn in this paper, especially concerning hydrological robustness at low flows.
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| 7. |
- Guerrero, José-Luis
(författare)
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Robust Water Balance Modeling with Uncertain Discharge and Precipitation Data : Computational Geometry as a New Tool
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Models are important tools for understanding the hydrological processes that govern water transport in the landscape and for prediction at times and places where no observations are available. The degree of trust placed on models, however, should not exceed the quality of the data they are fed with. The overall aim of this thesis was to tune the modeling process to account for the uncertainty in the data, by identifying robust parameter values using methods from computational geometry. The methods were developed and tested on data from the Choluteca River basin in Honduras.Quality control of precipitation and discharge data resulted in a rejection of 22% percent of daily raingage data and the complete removal of one out of the seven discharge stations analyzed. The raingage network was not found sufficient to capture the spatial and temporal variability of precipitation in the Choluteca River basin. The temporal variability of discharge was evaluated through a Monte Carlo assessment of the rating-equation parameter values over a moving time window of stage-discharge measurements. Al hydrometric stations showed considerable temporal variability in the stage-discharge relationship, which was largest for low flows, albeit with no common trend. The problem with limited data quality was addressed by identifying robust model parameter values within the set of well-performing (behavioral) parameter-value vectors with computational-geometry methods. The hypothesis that geometrically deep parameter-value vectors within the behavioral set were hydrologically robust was tested, and verified, using two depth functions. Deep parameter-value vectors tended to perform better than shallow ones, were less sensitive to small changes in their values, and were better suited to temporal transfer. Depth functions rank multidimensional data. Methods to visualize the multivariate distribution of behavioral parameters based on the ranked values were developed. It was shown that, by projecting along a common dimension, the multivariate distribution of behavioral parameters for models of varying complexity could be compared using the proposed visualization tools. This has a potential to aid in the selection of an adequate model structure considering the uncertainty in the data.These methods allowed to quantify observational uncertainties. Geometric methods have only recently begun to be used in hydrology. It was shown that they can be used to identify robust parameter values, and some of their potential uses were highlighted.
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| 8. |
- Guerrero, Jose-Luis, et al.
(författare)
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Temporal variability in stage-discharge relationships
- 2012
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 446, s. 90-102
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Tidskriftsartikel (refereegranskat)abstract
- Although discharge estimations are central for water management and hydropower, there are few studies on the variability and uncertainty of their basis; deriving discharge from stage heights through the use of a rating curve that depends on riverbed geometry. A large fraction of the world's river-discharge stations are presumably located in alluvial channels where riverbed characteristics may change over time because of erosion and sedimentation. This study was conducted to analyse and quantify the dynamic relationship between stage and discharge and to determine to what degree currently used methods are able to account for such variability. The study was carried out for six hydrometric stations in the upper Choluteca River basin, Honduras, where a set of unusually frequent stage-discharge data are available. The temporal variability and the uncertainty of the rating curve and its parameters were analysed through a Monte Carlo (MC) analysis on a moving window of data using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. Acceptable ranges for the values of the rating-curve parameters were determined from riverbed surveys at the six stations, and the sampling space was constrained according to those ranges, using three-dimensional alpha shapes. Temporal variability was analysed in three ways: (i) with annually updated rating curves (simulating Honduran practices), (ii) a rating curve for each time window, and (iii) a smoothed, continuous dynamic rating curve derived from the MC analysis. The temporal variability of the rating parameters translated into a high rating-curve variability. The variability could turn out as increasing or decreasing trends and/or cyclic behaviour. There was a tendency at all stations to a seasonal variability. The discharge at a given stage could vary by a factor of two or more. The quotient in discharge volumes estimated from dynamic and static rating curves varied between 0.5 and 1.5. The difference between discharge volumes derived from static and dynamic curves was largest for sub-daily ratings but stayed large also for monthly and yearly totals. The relative uncertainty was largest for low flows but it was considerable also for intermediate and large flows. The standard procedure of adjusting rating curves when calculated and observed discharge differ by more than 5% would have required continuously updated rating curves at the studied locations. We believe that these findings can be applicable to many other discharge stations around the globe.
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| 9. |
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| 10. |
- Kauffeldt, Anna, 1981-
(författare)
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Disinformative and Uncertain Data in Global Hydrology : Challenges for Modelling and Regionalisation
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Water is essential for human well-being and healthy ecosystems, but population growth and changes in climate and land-use are putting increased stress on water resources in many regions. To ensure water security, knowledge about the spatiotemporal distribution of these resources is of great importance. However, estimates of global water resources are constrained by limitations in availability and quality of data. This thesis explores the quality of both observational and modelled data, gives an overview of models used for large-scale hydrological modelling, and explores the possibilities to deal with the scarcity of data by prediction of flow-duration curves.The evaluation of the quality of observational data for large-scale hydrological modelling was based on both hydrographic data, and model forcing and evaluation data for basins worldwide. The results showed that a GIS polygon dataset outperformed all gridded hydrographic products analysed in terms of representation of basin areas. Through a screening methodology based on the long-term water-balance equation it was shown that as many as 8–43% of the basins analysed displayed inconsistencies between forcing (precipitation and potential evaporation) and evaluation (discharge) data depending on how datasets were combined. These data could prove disinformative in hydrological model inference and analysis.The quality of key hydrological variables from a numerical weather prediction model was assessed by benchmarking against observational datasets and by analysis of the internal land-surface water budgets of several different model setups. Long-term imbalances were found between precipitation and evaporation on the global scale and between precipitation, evaporation and runoff on both cell and basin scales. These imbalances were mainly attributed to the data assimilation system in which soil moisture is used as a nudge factor to improve weather forecasts.Regionalisation, i.e. transfer of information from data-rich areas to data-sparse areas, is a necessity in hydrology because of a lack of observed data in many areas. In this thesis, the possibility to predict flow-duration curves in ungauged basins was explored by testing several different methodologies including machine learning. The results were mixed, with some well predicted curves, but many predicted curves exhibited large biases and several methods resulted in unrealistic curves.
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