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Modelling solute mixing and transport processes is one of the key steps to effectively managing karstic groundwater resources, particularly under the threat of climate change and risk of contamination. For that reason, a considerable body of literature has been devoted to understanding and describing solute mixing and transport processes in karst aquifers. However, due to the strong multiscale heterogeneity (from microscale to aquifer scale), modelling solute mixing and transport processes in karst aquifers remains a challenging task. This presentation critically reviews the current state of knowledge and fundamental challenges in the modelling of solute mixing and transport processes in karst aquifers, thereby collocating and synthesizing the existing body of knowledge in the literature. To provide a holistic and objective picture of the state-of-the-art of the solute mixing and transport modelling, we performed a bibliometric analysis on the relevant literature for karst groundwater studies (over 2800 scientific papers). Further, with a meta-analysis of scientific papers focusing on the quantitative tracer tests, we evaluated the field-based transport parameters that are typically served for the solute mixing and transport models.The review unveils the fundamental modelling hinges underlying a successful modelling practice for the solute mixing and transport processes in karst, thereby discussing to what extent and in what ways we are dealing with these challenges. The major modelling challenges are defined as follows: (i) Model conceptualization based on data collection and system understanding (e.g., How well is the problem of interest defined? To what extent is the domain of interest described?), ii) Model selection considering the choice of a dominant physicochemical process (e.g., How well is the process of interest represented by a set of governing equations over the problem domain?), iii) Time-variability of solute mixing and transport processes (e.g., To what extent do the parameters represent the process of interest under the different time-scales?), iv) Model parametrization considering the parameter non-uniqueness and transferability (e.g., How realistic are the model parameters? To which extent are they transferable over the same aquifer?), v) Uncertainty quantification in model results (e.g., How robust are the model results? How much are we (un)certain about our model?). Finally, we address potential research directions and knowledge gaps by encouraging the community for building a protocol for solute transport modelling in karst aquifers, as well as providing more transparent and reproducible results.

The recent droughts and unprecedented floods in Central Europe have revealed our vulnerability to extreme weather events. Besides climate change as a driver of more frequent and intensifying extreme events, demographic change and socio-economic development exacerbate severe impacts. International frameworks for disaster risk reduction (DRR) and climate change adaptation (e.g. Sendai framework for DRR, EU Strategy on adaptation to climate change) acknowledge the critical need for integrating risk governance, communication and operational mechanisms for coping with extreme climate events throughout the entire Disaster Risk Management cycle.DIRECTED aspires to foster disaster-resilient European societies by expanding our capabilities to communicate, utilise and exchange state-of-the-art data, information and knowledge between different actors. The project strives to boost the integration, accessibility and interoperability of models, facilitating knowledge sharing and improving dialogue and cooperation on all levels of Disaster Risk Management cycle. Four regional and municipal Real World Labs in the Capital Region of Denmark, the Danube Region, Emilia Romagna Region, Italy and the Rhine-Erft District, Germany, are at the centre of the bottom-up, value-driven co-development approach. The Real World Labs ensure the project continuously and actively involves key stakeholders in the development process and address topical problems of multi-hazard risk management and climate change adaptation to maximise the impacts of the DIRECTED project. Key to supporting interoperability will be the establishment of the DATA-FABRIC, an innovative, federated cloud platform that enables secure, flexible, discovery and sharing of all structured and unstructured data. DIRECTED is committed to promote the power of open data and open science in all of its research efforts.Through an interdisciplinary approach that brings together natural and social scientists, with data experts, local stakeholders as well as first and second responders DIRECTED builds lasting real world partnerships and leverages synergies for Disaster Risk Reduction and Climate Change Adaptation efforts in Europe.

Autoimmune encephalitis (AE) associated with autoantibodies against leucine-rich glioma-inactivated protein-1 (LGI1) can present with faciobrachial dystonic seizures (FBDS) and/or limbic encephalitis (LE). The reasons for this heterogeneity in phenotypes are unclear. We performed autoantibody (abs) characterization per patient, two patients suffering from LE and two from FBDS, using isolated antibodies specified with single amino acid epitope mapping. Electrophysiological slice recordings were conducted alongside spine density measurements, postsynaptic Alpha-amino-3-hydoxy-5-methyl-4-isoaxole-proprionate-receptors (AMPA-R) and N-methyl-D-aspartate-receptors receptor (NMDA-R) cluster counting. These results were correlated with the symptoms of each patient. While LGI1 abs from LE patients mainly interacted with the Leucine-rich repeat section of LGI1, abs from both FBDS patients also recognized the Epitempin section as well. Six-hour incubation of mouse hippocampal slices with LE patients autoantibodies but not from the FBDS patients resulted in a significant decline in long-term potentiation (p = 0.0015) or short-term plasticity at CA3-CA1 neurons and in decreased hippocampal synaptic density. Cluster differentiation showed no decrease in postsynaptic AMPA-R and NMDA-R. LGI1 autoantibodies selected by phenotype show an almost distinct epitope pattern, elicit disparate functional effects on hippocampal neurons, and cause divergent effects on spine density. This data illuminates potential pathomechanisms for disease heterogeneity in LGI1 AE.

Introducing additional information sources, such as hydrochemical signatures and water isotopes, into the model calibration has shown to be useful to enhance model robustness by increasing parameter identifiability and maintaining simulation reliability. Our study explores the added value of discharge young water fractions (Fyw, derived from the volume-weighted δ18O concentrations) on the model reliability as a calibration constraint. For this, we coupled a karst hydrological model (VarKarst) with a catchment-scale transport model (StorAge Selection (SAS) function approach) to simulate discharge δ18O concentration (δ18OQ) and corresponding Fyw. We performed a multi-variable calibration scheme by simultaneously constraining a large model ensemble (1x106 realizations) with respect to the model performance on discharge and Fyw. By searching a model output space in which the model performance on discharge and model performance on Fyw provides an optimal trade-off, we extracted hydrologically more informative model realizations. We tested our calibration approach at the Wasseralm spring, which supplies drinking water to the city of Vienna, Austria. The contribution of the information content of Fyw to the model robustness was assessed by the degree of reduction in the parameter and simulation uncertainties. Our results indicate that the inclusion of Fyw notably reduced the uncertainty in model parameters (6 parameters out of 8), simulations (14 % vs. 10 % by δ18OQ), and water balance components for the model internal states (around 40 %). Our findings reveal that Fyw confirms the model reliability (KGE: 0.71 ± 0.01 in validation) in that it mainly reduces the parameter equifinality by providing physically more plausible and identifiable parameter sets. Therefore, Fyw is a potentially useful metric to better constrain the model outputs, thereby limiting the model uncertainty.

Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

The lumped parameter modeling approach has been widely applied in karst hydrology for, among other applications, the understanding of their functioning of the assessment or groundwater availability in a context of global change. Nonetheless, such an approach generally does not account for land-cover land-use (LCLU) changes and their potential impacts on recharge processes. The study focuses on three forests dominated karst catchments: Kerschbaum (Lower Austria), Baget (French Pyrenees) and Oeillal (southern France), and investigates how LCLU changes in a lumped parameter modeling approach can affect both the internal fluxes and the model performance. The active subspace method is used to perform sensitivity analysis of model parameters, and to quantify parameter uncertainty. We show that the consideration of a semi-distributed recharge constitutes a relevant approach to capture the impact of LCLU changes on flow dynamics, but also introduces more uncertainty in the modeling approach. This approach may thus allow identifying the trade-off between modeling approach complexity and its performance. Finally, it gives new insight for the assessment of LCLU changes impacts on karst groundwater resource.

INTRODUCTIONImpaired long-term memory is a defining feature of Mild Cognitive Impairment (MCI). We tested whether this impairment is item-specific, limited to some memoranda whereas some remain consistently memorable.METHODSWe conducted item-based analyses of long-term visual recognition memory. 394 participants (healthy controls (HC), Subjective Cognitive Decline (SCD), and MCI) in the multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) were tested with images from a pool of 835 photographs.RESULTSWe observed consistent memorability for images in HCs, SCDs, and MCI, predictable by a neural network trained on another healthy sample. Looking at memorability differences between groups, we identified images that could successfully categorize group membership with higher success and a substantial image reduction than the original image set.DISCUSSIONIndividuals with SCD and MCI show consistent memorability for specific items, while other items show significant diagnosticity. Certain stimulus features could optimize diagnostic assessment, while others could support memory.

Introduction: Hepatic steatosis (HS) is associated with coronary artery disease (CAD) and cardiovascular (CV) events. Previously, we have demonstrated that granular measures of lipids (lipoprotein particle number/size) are associated with CAD and CV events and incremental to traditional lipid measures. Hypothesis: Granular measures of lipids are associated with HS detected by cardiac computed tomography (CT) and with HS detected by histopathology. Methods: We included 1524 subjects from the PROMISE trial. HS was defined as CT attenuation of the liver Results: Subjects with HS (n=413) were slightly younger (59±8 vs 61±8 yrs) and more likely men (53 vs 44%) as compared to controls (n=1111). Three lipoprotein factors were associated with HS: LDL/LDL particle size (OR 1.36, 95%CI 1.21-1.53, p Conclusions: We found association of small LDL, large HDL and large TG particles, previously associated with CV event risk, with HS phenotyped by CT and histopathology. These results suggest that use of lipoprotein subclasses may improve CV risk assessment in patients with HS.

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

The Mediterranean region is identified as a climate change hotspot, where future scenarios indicate an increase of temperature associated with a decrease of precipitation. Providing future scenarios of water resource availability considering both climate and anthropogenic changes on karst catchments remains a major challenge for hydrological sciences. The study concerns the Oeillal spring, which is one outlet of the karst catchment associated with the Fonfroide-Monredon massif (southern France), mainly composed by Jurassic limestones. We assess the relative effects of climate changes and anthropogenic forcing on the karst spring discharge by coupling 12 climate model simulations (GCM/RCM) under two emission scenarios (RCP 4.5 and RCP 8.5) with 3 hydrological models and considering 4 scenarios of groundwater abstraction for drinking water supply (no abstraction, present-day abstraction, +50 % abstraction and +100 % abstraction at horizon 2100). We find that climate change has a major effect on the future evolution of the Oeillal spring’s discharge and that groundwater abstraction constitutes a secondary but non-negligible factor, which increases the occurrence of dry up of the Oeillal spring.

International audience; Global sensitivity analysis is an important step in the process of developing and analyzing hydrological models. Measured data of different variables are used to identify the number of sensitive model parameters and to better constrain the model output. However, data scarcity is a common issue in hydrology. Since in hydrology we are dealing with multi-scale time dependent problems, we want to overcome that issue by exploiting the potential of using the decomposed wavelet temporal scales of the discharge signal for the identification of sensitive model parameters. In the proposed methodology, we coupled the discrete wavelet transform with a technique for model parameter dimension reduction, that is, the active subspace method. We apply the proposed methodology to the LuKARS model, a lumped karst aquifer model for the Kerschbaum spring in Waidhofen/Ybbs (Austria). Our results demonstrate that the temporal scale dependency of hydrological processes affects the structure and dimension of the active subspaces. The results reveal that the dimensionality of an active subspace increases with the increasing number of hydrologic processes affecting a temporal scale. As a consequence, different parameters are sensitive on different temporal scales. Finally, we show that the total number of sensitive parameters identified at different temporal scales is larger than the number of sensitive parameters obtained using the complete spring discharge signal. Hence, instead of using multiple time series to determine the number of sensitive parameters, we can also obtain more information about parameter sensitivities from one single, decomposed time series.

(IF 6.44; Q1); International audience; Karst aquifers provide drinking water for 10% of the world’s population, support agriculture, groundwater-dependent activities, and ecosystems. These aquifers are characterised by complex groundwater-flow systems, hence, they are extremely vulnerable and protecting them requires an in-depth understanding of the systems. Poor data accessibility has limited advances in karst research and realistic representation of karst processes in large-scale hydrological studies. In this study, we present World Karst Spring hydrograph (WoKaS) database, a community-wide effort to improve data accessibility. WoKaS is the first global karst springs discharge database with over 400 spring observations collected from articles, hydrological databases and researchers. The dataset’s coverage compares to the global distribution of carbonate rocks with some bias towards the latitudes of more developed countries. WoKaS database will ensure easy access to a large-sample of good quality datasets suitable for a wide range of applications: comparative studies, trend analysis and model evaluation. This database will largely contribute to research advancement in karst hydrology, supports karst groundwater management, and promotes international and interdisciplinary collaborations.

Karst aquifers provide drinking water for 10% of the world’s population, support agriculture, groundwater-dependent activities, and ecosystems. These aquifers are characterised by complex groundwater-flow systems, hence, they are extremely vulnerable and protecting them requires an in-depth understanding of the systems. Poor data accessibility has limited advances in karst research and realistic representation of karst processes in large-scale hydrological studies. In this study, we present World Karst Spring hydrograph (WoKaS) database, a community-wide effort to improve data accessibility. WoKaS is the first global karst springs discharge database with over 400 spring observations collected from articles, hydrological databases and researchers. The dataset’s coverage compares to the global distribution of carbonate rocks with some bias towards the latitudes of more developed countries. WoKaS database will ensure easy access to a large-sample of good quality datasets suitable for a wide range of applications: comparative studies, trend analysis and model evaluation. This database will largely contribute to research advancement in karst hydrology, supports karst groundwater management, and promotes international and interdisciplinary collaborations., Measurement(s)hydrographic feature • fluid flow rateTechnology Type(s)digital curationFactor Type(s)geographic location • yearSample Characteristic - Environmentkarst • spring • groundwater Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11336507

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems‐wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)‐based proteomics workflow for the in‐depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV, A robust proteomic workflow quantifies more than 1,000 proteins in cerebrospinal fluid and reveals an Alzheimer's Disease‐associated signature of more than 20 proteins across three independent cohorts. These include tau, superoxide dismutase 1, PARK7, YKL‐40 and novel biomarker candidates.

Modeling karst spring discharge while considering potential impacts of land use changes in a recharge area is a crucial task for water resource managers worldwide. Generally, such models are based on sophisticated mathematical functions developed and applied by researchers and their complex nature does not allow an intuitive applicability. To overcome this limitation and to make these models applicable for stakeholders, they need to be integrated in an applicable and open source framework that can be used by water managers without losing the original and full modeling functionalities.In our work, we introduce a user-friendly modeling environment by integrating the recently proposed LuKARS (Land use change modeling in KARSt systems) model into FREEWAT (FREE and Open Source Software Tools for WATer Resource Management). LuKARS is a lumped, rainfall-discharge model for karst systems that considers impacts of land use changes by changing the area of a so-called hydrotope, representing a landscape unit with homogeneous soil and land use properties. FREEWAT provides an open source toolkit for water resource management that is implemented as a plugin in QGIS. The integration of LuKARS benefits from QGIS’ mapping, visualization and geospatial manipulation capabilities. The plugin of FREEWAT provides a modular concept of pre- and post-processing tools that facilitate the setup, calibration, analysis, storage and sharing of a LuKARS model.

Introduction We examined the association between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease, neural novelty responses, and brain volume in predementia old age. Methods We conducted a cross-sectional analysis of the observational, multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Seventy-six participants completed task functional magnetic resonance imaging and provided CSF (40 cognitively unimpaired, 21 experiencing subjective cognitive decline, and 15 with mild cognitive impairment). We assessed the correlation between CSF biomarkers and whole-brain functional magnetic resonance imaging novelty responses to scene images. Results Total tau levels were specifically and negatively associated with novelty responses in the right amygdala and right hippocampus. Mediation analyses showed no evidence that these associations were dependent on the volume of hippocampus/amygdala. No relationship was found between phosphorylated-tau or Aβ42 levels and novelty responses. Discussion Our data show that CSF levels of total tau are associated with anatomically specific reductions in novelty processing, which cannot be fully explained by atrophy.

International audience; The Mediterranean region is a climate change hotspot where the rate of climate change exceeds the global mean. The rapidly changing climate in combination with an increase in anthropogenic pressures cause water resources in the Mediterranean basin to become increasingly scarce. Modelling future water resource availability considering both climate and anthropogenic changes on karst catchments remains a major challenge in the field of hydrology. The purpose of this study is to assess the relative effects of climate change and anthropogenic forcing on the spring discharge of a Mediterranean karst system by coupling 12 climate model simulations under two emission scenarios (RCP 4.5 and RCP 8.5) with three hydrological models and four scenarios of future groundwater extraction for drinking water supply (no abstraction, present-day abstraction, +50% abstraction and +100% abstraction at horizon 2100). The study area is the Oeillal spring’s karst catchment which is located at the Fonfroide-Monredon massif located in Southern France. The periods of spring drying-up can increase up to 30% according the RCP 4.5 and up to 70% according the RCP 8.5. This may be aggravated by groundwater abstraction in the area which, combined with climate change, could contribute to double the length of spring drying-up period in the worst scenario. The main results of the study suggest that climate change has a major effect on the future evolution of the Oeillal spring’s discharge and that groundwater abstraction constitutes a secondary but non-negligible factor which increases the occurrence of drying-up of this Mediterranean spring.

Retention and detention basins are engineering constructions with multiple objectives, e.g., flood protection and irrigation. Their performance is highly location-dependent, and thus, optimization strategies are needed. LOCASIN (Location detection of retention and detention basins) is an open-source MATLAB tool that enables automated and rapid detection, characterization and evaluation of basin locations. The site detection is based on a numerical raster analysis to determine the optimal dam axis orientation, the dam geometry and the basin area and volume. After selecting a reasonable basin combination, the results are summarized and visualized. LOCASIN represents a user-friendly and flexible tool for policy makers, engineers and scientists to determine dam and basin properties of optimized positions for planning and research purposes. It can be applied in an automated way to solve small and large scale engineering problems. The software is available on GitHub.

Dolomite karst systems are less common and less exploited than limestone systems, but still they can significantly contribute to drinking water supply in many countries. The knowledge about spatial and temporal variations of hydrochemical parameters of such systems is much more limited than for limestone karst aquifers. In the present research, 40-year-long observations of hydrochemical parameters gathered from five springs and a pumping well in Waidhofen an der Ybbs (Austria) were used to show the variability of chemical water composition in a small-scale ( ~ 10 km2) dolomite karst system. Integration of classic geochemical methods and multivariate statistical analysis revealed that the hydrofacies correlate directly with the lithofacies (dolomite) in the study area. At the same time, measured concentrations of Ca2+, Mg2+, Na+, Cl−, and HCO3− allow for a classification of the springs in three groups based on their ionic ratios. This classification highlights the spatial and temporal variability that can be encountered in dolomite karst systems, even on small spatial scales, that are relevant for water suppliers. Moreover, temporal observations of hydrochemical parameters show increasing trends of nitrate concentrations in all sampling points, though with different rates. The analysis of the stable isotopes δ15N–NO3 and δ18O–NO3 revealed that nitrification processes in forest floors and mineral soils are the main source of nitrate in most locations investigated. The findings of the present study highlight the necessity of a detailed temporal and spatial distributed monitoring to support water resources management in dolomitic karst aquifers.

In Alzheimer's disease (AD), a single-nucleotide polymorphism in the gene encoding brain-derived neurotrophic factor (BDNFVal66Met) is associated with worse impact of primary AD pathology (beta-amyloid, Aβ) on neurodegeneration and cognitive decline, rendering BDNFVal66Met an important modulating factor of cognitive impairment in AD. However, the effect of BDNFVal66Met on functional networks that may underlie cognitive impairment in AD is poorly understood. Using a cross-validation approach, we first explored in subjects with autosomal dominant AD (ADAD) from the Dominantly Inherited Alzheimer Network (DIAN) the effect of BDNFVal66Met on resting-state fMRI assessed functional networks. In seed-based connectivity analysis of six major large-scale networks, we found a stronger decrease of hippocampus (seed) to medial-frontal connectivity in the BDNFVal66Met carriers compared to BDNFVal homozogytes. BDNFVal66Met was not associated with connectivity in any other networks. Next, we tested whether the finding of more pronounced decrease in hippocampal-medial-frontal connectivity in BDNFVal66Met could be also found in elderly subjects with sporadically occurring Aβ, including a group with subjective cognitive decline (N = 149, FACEHBI study) and a group ranging from preclinical to AD dementia (N = 114, DELCODE study). In both of these independently recruited groups, BDNFVal66Met was associated with a stronger effect of more abnormal Aβ-levels (assessed by biofluid-assay or amyloid-PET) on hippocampal-medial-frontal connectivity decreases, controlled for hippocampus volume and other confounds. Lower hippocampal-medial-frontal connectivity was associated with lower global cognitive performance in the DIAN and DELCODE studies. Together these results suggest that BDNFVal66Met is selectively associated with a higher vulnerability of hippocampus-frontal connectivity to primary AD pathology, resulting in greater AD-related cognitive impairment.

Mnemonic discrimination, the ability to distinguish similar events in memory, relies on subregions in the human medial temporal lobes (MTLs). Tau pathology is frequently found within the MTL of older adults and therefore likely to affect mnemonic discrimination, even in healthy older individuals. The MTL subregions that are known to be affected early by tau pathology, the perirhinal-transentorhinal region (area 35) and the anterior-lateral entorhinal cortex (alEC), have recently been implicated in the mnemonic discrimination of objects rather than scenes. Here we used an object-scene mnemonic discrimination task in combination with fMRI recordings and analyzed the relationship between subregional MTL activity, memory performance, and levels of total and phosphorylated tau as well as Aβ42/40 ratio in CSF. We show that activity in alEC was associated with mnemonic discrimination of similar objects but not scenes in male and female cognitively unimpaired older adults. Importantly, CSF tau levels were associated with increased fMRI activity in the hippocampus, and both increased hippocampal activity as well as tau levels were associated with mnemonic discrimination of objects, but again not scenes. This suggests that dysfunction of the alEC-hippocampus object mnemonic discrimination network might be a marker for tau-related cognitive decline.SIGNIFICANCE STATEMENTSubregions in the human medial temporal lobe are critically involved in episodic memory and, at the same time, affected by tau pathology. Impaired object mnemonic discrimination performance as well as aberrant activity within the entorhinal-hippocampal circuitry have been reported in earlier studies involving older individuals, but it has thus far remained elusive whether and how tau pathology is implicated in this specific impairment. Using task-related fMRI in combination with measures of tau pathology in CSF, we show that measures of tau pathology are associated with increased hippocampal activity and reduced mnemonic discrimination of similar objects but not scenes. This suggests that object mnemonic discrimination tasks could be promising markers for tau-related cognitive decline.

Background The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease. Objective To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients. Methods Eighty-one inpatients (55 females, 81.6±5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimer's disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis. Results Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p≤0.001). Integration of all modalities resulted in 76% accuracy (p≤0.001). Conclusion These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.

Simulating karst spring discharge and land use change impacts in a recharge area is strategic for water resource management in many countries worldwide. We introduce a user-friendly modeling environment by integrating the recently proposed LuKARS (Land use change modeling in KARSt systems) model into the FREEWAT (FREE and Open Source Software Tools for WATer Resource Management) framework. LuKARS is a rainfall-discharge model for karst systems that simulates the impact of land use changes by changing the area of dominant hydrotopes, i.e. landscape units with homogeneous soil and land use properties. FREEWAT is a free and open source toolkit for water resource management implemented in QGIS desktop application. The integration of LuKARS into FREEWAT takes advantage of the GIS capabilities to map, visualize and change the relevant hydrotope shapefiles. FREEWAT provides a modular framework for pre- and post-processing tools that facilitate the setup, calibration, analysis, storage and sharing of LuKARS models.

The use of the active subspace method was recently proposed to reduce the dimension of complex hydrological models, perform sensitivity analysis of model parameters and quantify the uncertainty affecting model parameters. Although this inversion method is highly promising in terms of computational performance, a clear hydrological interpretation of the meaning of the active subspace that it identifies is missing. In this work, we infer how the active subspace changes in dimension and feature depending on geometrical and hydrological properties of the karst aquifer model LuKARS. We find that both the hydrotope area coverage and model parameters describing the catchment characteristics (here: water storage and discharge properties of the hydrotopes) have major impacts on the active subspace. Our results show that the active subspace method can be used to investigate the relation between the model structure, the area of a hydrotope and the simulated spring discharge.

In this article, we perform a parameter study for a recently developed karst hydrological model. The study consists of a high-dimensional Bayesian inverse problem and a global sensitivity analysis. For the first time in karst hydrology, we use the active subspace method to find directions in the parameter space that dominate the Bayesian update from the prior to the posterior distribution in order to effectively reduce the dimension of the problem and for computational efficiency. Additionally, the calculated active subspace can be exploited to construct sensitivity metrics on each of the individual parameters and be used to construct a natural model surrogate. The model consists of 21 parameters to reproduce the hydrological behavior of spring discharge in a karst aquifer located in the Kerschbaum spring recharge area at Waidhofen a.d. Ybbs in Austria. The experimental spatial and time series data for the inference process were collected by the water works in Waidhofen. We show that this case study has implicit low-dimensionality, and we run an adjusted Markov chain Monte Carlo algorithm in a low-dimensional subspace to construct samples of the posterior distribution. The results are visualized and verified by plots of the posterior's push-forward distribution displaying the uncertainty in predicting discharge values due to the experimental noise in the data. Finally, a discussion provides hydrological interpretation of these results for the Kerschbaum area., 27 pages, 5 figures, 2 tables; 5 pages supplementary information

Background Prepulse inhibition (PPI) of the acoustic startle response, a measurement of sensorimotor gaiting, is modulated by monoaminergic, presumably dopaminergic neurotransmission. Disturbances of the dopaminergic system can cause deficient PPI as found in neuropsychiatric diseases. A target specific influence of deep brain stimulation (DBS) on PPI has been shown in animal models of neuropsychiatric disorders. In the present study, three patients with early dementia of Alzheimer type underwent DBS of the median forebrain bundle (MFB) in a compassionate use program to maintain cognitive abilities. This provided us the unique possibility to investigate the effects of different stimulation conditions of DBS of the MFB on PPI in humans. Results Separate analysis of each patient consistently showed a frequency dependent pattern with a DBS-induced increase of PPI at 60 Hz and unchanged PPI at 20 or 130 Hz, as compared to sham stimulation. Conclusions Our data demonstrate that electrical stimulation of the MFB modulates PPI in a frequency-dependent manner. PPI measurement could serve as a potential marker for optimization of DBS settings independent of the patient or the examiner.

Natalizumab (NTZ) was the first approved humanized monoclonal antibody in highly active relapsing remitting MS (RRMS). Because of the mechanism of inhibiting the migration of immune cells through the blood-brain barrier into the CNS, NTZ is associated with an increased risk of progressive multifocal leukoencephalopathy (PML) by the John Cunningham virus (JCV). Infections with other neurotropic viruses are rarely reported.1,2 We present a case of rapid retinal necrosis induced by varicella zoster virus (VZV) in a patient with RRMS under long-term NTZ treatment.