3,015 research outputs found

    Low plasma neurofilament light levels associated with raised cortical microglial activation suggest inflammation acts to protect prodromal Alzheimer's disease

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    BACKGROUND: Plasma and cerebrospinal fluid levels of neurofilament light (NfL), a marker of axonal degeneration, have previously been reported to be raised in patients with clinically diagnosed Alzheimer's disease (AD). Activated microglia, an intrinsic inflammatory response to brain lesions, are also known to be present in a majority of Alzheimer or mild cognitive impaired (MCI) subjects with raised β-amyloid load on their positron emission tomography (PET) imaging. It is now considered that the earliest phase of inflammation may be protective to the brain, removing amyloid plaques and remodelling synapses. Our aim was to determine whether the cortical inflammation/microglial activation load, measured with the translocator protein marker 11C-PK11195 PET, was correlated with plasma NfL levels in prodromal and early Alzheimer subjects. METHODS: Twenty-seven MCI or early AD cases with raised cortical β-amyloid load had 11C-(R)-PK11195 PET, structural and diffusion magnetic resonance imaging, and levels of their plasma NfL measured. Correlation analyses were performed using surface-based cortical statistics. RESULTS: Statistical maps localised areas in MCI cases where levels of brain inflammation correlated inversely with plasma NfL levels. These areas were localised in the frontal, parietal, precuneus, occipital, and sensorimotor cortices. Brain inflammation correlated negatively with mean diffusivity (MD) of water with regions overlapping. CONCLUSION: We conclude that an inverse correlation between levels of inflammation in cortical areas and plasma NfL levels indicates that microglial activation may initially be protective to axons in AD. This is supported by the finding of an inverse association between cortical water diffusivity and microglial activation in the same regions. Our findings suggest a rationale for stimulating microglial activity in early and prodromal Alzheimer cases-possibly using immunotherapy. Plasma NfL levels could be used as a measure of the protective efficacy of immune stimulation and for monitoring efficacy of putative neuroprotective agents

    Nitrogen transfer from forage legumes to nine neighbouring plants in a multi-species grassland

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    Legumes play a crucial role in nitrogen supply to grass-legume mixtures for ruminant fodder. To quantify N transfer from legumes to neighbouring plants in multi-species grasslands we established a grass-legume-herb mixture on a loamy-sandy site in Denmark. White clover (Trifolium repens L.), red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) were leaf-labelled with 15N enriched urea during one growing season. N transfer to grasses (Lolium perenne L. and xfestulolium), white clover, red clover, lucerne, birdsfoot trefoil (Lotus corniculatus L.), chicory (Cichorium intybus L.), plantain (Plantago lanceolata L.), salad burnet (Sanguisorba minor L.)and caraway (Carum carvi L.) was assessed. Neighbouring plants contained greater amounts of N derived from white clover (4.8 gm-2) compared with red clover (2.2 gm-2) and lucerne (1.1 gm-2). Grasses having fibrous roots received greater amounts of N from legumes than dicotyledonous plants which generally have taproots. Slurry application mainly increased N transfer from legumes to grasses. During the growing season the three legumes transferred approximately 40 kg N ha-1 to neighbouring plants. Below-ground N transfer from legumes to neighbouring plants differed among nitrogen donors and nitrogen receivers and may depend on root characteristics and regrowth strategies of plant species in the multi-species grassland

    Nesiritide: Harmful or Harmless?

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90328/1/phco.26.10.1465.pd

    Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets

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    We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters

    “So, I told him to look for friends!” Barriers and protecting factors that may facilitate inclusion for children with Language Disorder in everyday social settings:cross-cultural qualitative interviews with parents

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    Purpose: Although researchers have explored parental perspectives on childhood speech and language disorders, this work has mostly been conducted in English-speaking countries. Little is known about parental experiences across countries. Participation in the COST Action IS1406 ‘Enhancing children’s oral language skills across Europe and beyond’ provided an opportunity to conduct cross-cultural qualitative interviews. The aims were to explore how parents construe inclusion and/or exclusion of their child and how parents involve themselves in order to facilitate inclusion. Method: Parents from nine countries and with a child who had received services for speechlanguage disorder participated in semi-structured qualitative interviews. We used thematic analysis to analyze the data. Results: Two overarching themes were identified: ‘Language disabilities led to social exclusion’ and ‘Promoting pathways to social inclusion’. Two subthemes were identified Interpersonal relationships are important and Deliberate proactiveness as stepping stones for social inclusion. Conclusions: Across countries, parents report that their children’s hidden disability causes misunderstandings that can lead to social exclusion and that they are important advocates for their children. It is important that the voices and experiences of parents of children with developmental disabilities are understood and acknowledged. Parents’ recommendations about how to support social inclusion need to be addressed at all levels of society

    Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

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    Simultaneous analysis of economic and environmental performance of horticultural crop production requires qualified assumptions on the effect of management options, and particularly of nitrogen (N) fertilisation, on the net returns of the farm. Dynamic soil-plant-environment simulation models for agro-ecosystems are frequently applied to predict crop yield, generally as dry matter per area, and the environmental impact of production. Economic analysis requires conversion of yields to fresh marketable weight, which is not easy to calculate for vegetables, since different species have different properties and special market requirements. Furthermore, the marketable part of many vegetables is dependent on N availability during growth, which may lead to complete crop failure under sub-optimal N supply in tightly calculated N fertiliser regimes or low-input systems. In this paper we present two methods for converting simulated total dry matter to marketable fresh matter yield for various vegetables and European growth conditions, taking into consideration the effect of N supply: (i) a regression based function for vegetables sold as bulk or bunching ware and (ii) a population approach for piecewise sold row crops. For both methods, to be used in the context of a dynamic simulation model, parameter values were compiled from a literature survey. Implemented in such a model, both algorithms were tested against experimental field data, yielding an Index of Agreement of 0.80 for the regression strategy and 0.90 for the population strategy. Furthermore, the population strategy was capable of reflecting rather well the effect of crop spacing on yield and the effect of N supply on product grading

    Towards Translational ImmunoPET/MR Imaging of Invasive Pulmonary Aspergillosis: The Humanised Monoclonal Antibody JF5 Detects Aspergillus Lung Infections In Vivo

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    This is the final published versionAvailable from Ivyspring International Publisher via the DOI in this recordInvasive pulmonary aspergillosis (IPA) is a life-threatening lung disease of hematological malignancy and bone marrow transplant patients caused by the ubiquitous environmental fungus Aspergillus fumigatus. Current diagnostic tests for the disease lack sensitivity as well as specificity, and culture of the fungus from invasive lung biopsy, considered the gold standard for IPA detection, is slow and often not possible in critically ill patients. In a previous study, we reported the development of a novel non-invasive procedure for IPA diagnosis based on antibody-guided positron emission tomography and magnetic resonance imaging (immunoPET/MRI) using a [64Cu]DOTA-labeled mouse monoclonal antibody (mAb), mJF5, specific to Aspergillus. To enable translation of the tracer to the clinical setting, we report here the development of a humanised version of the antibody (hJF5), and pre-clinical imaging of lung infection using a [64Cu]NODAGA-hJF5 tracer. The humanised antibody tracer shows a significant increase in in vivo biodistribution in A. fumigatus infected lungs compared to its radiolabeled murine counterpart [64Cu]NODAGA-mJF5. Using reverse genetics of the pathogen, we show that the antibody binds to the antigenic determinant 1,5-galactofuranose (Galf) present in a diagnostic mannoprotein antigen released by the pathogen during invasive growth in the lung. The absence of the epitope Galf in mammalian carbohydrates, coupled with the enhanced imaging capabilities of the hJF5 antibody, means that the [64Cu]NODAGA-hJF5 tracer developed here represents an ideal candidate for the diagnosis of IPA and translation to the clinical setting.This work was supported by the European Union Seventh Framework Programme FP7/2007-2013 under Grant 602820, the Deutsche Forschungsgemeinschaft (Grant WI3777/1-2 to SW), and the Werner Siemens Foundation. We thank Sven Krappman for use of the A. fumigatustdTomato strain, and acknowledge the Imaging Centre Essen (IMCES) for assistance with optical imaging of lungs

    Haematogenous Staphylococcus aureus meningitis. A 10-year nationwide study of 96 consecutive cases

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    BACKGROUND: Haematogenous Staphylococcus aureus meningitis is rare but associated with high mortality. Knowledge about the disease is still limited. The objective of this study was to evaluate demographic and clinical prognostic features of bacteraemic S. aureus meningitis. METHODS: Nationwide surveillance in Denmark from 1991 to 2000 with clinical and bacteriological data. Risks of death were estimated by Cox proportional hazards regression analysis. RESULTS: Among 12480 cases of S. aureus bacteraemia/sepsis, we identified 96 cases of non-surgical bacteraemic S. aureus meningitis (0.8%). Incidence rates were 0.24 (95% confidence interval [CI], 0.18 to 0.30)/100 000 population between 1991–1995 and 0.13 (CI, 0.08 to 0.17)/100 000 population between 1996–2000. Mortality was 56%. After adjustment, only co morbidity (hazard ratio [HR], 3.45; CI, 1.15 to 10.30) and critical illness (Pitt score ≥ 4) (HR, 2.14; CI, 1.09 to 4.19) remained independent predictors of mortality. CONCLUSION: The incidence, but not mortality of bacteraemic S. aureus meningitis decreased during the study period. Co morbidity and critical illness were independent predictors of a poor outcome
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