319 research outputs found

    EnKF assimilation of simulated spaceborne Doppler observations of vertical velocity: impact on the simulation of a supercell thunderstorm and implications for model-based retrievals

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    International audienceRecently, a number of investigations have been made that point to the robust effectiveness of the Ensemble Kalman Filter (EnKF) in convective-scale data assimilation. These studies have focused on the assimilation of ground-based Doppler radar observations (i.e. radial velocity and reflectivity). The present study differs from these investigations in two important ways. First, in anticipation of future satellite technology, the impact of assimilating spaceborne Doppler-retrieved vertical velocity is examined; second, the potential for the EnKF to provide an alternative to instrument-based microphysical retrievals is investigated. It is shown that the RMS errors of the analyzed fields produced by assimilation of vertical velocity alone are in general better than those obtained in previous studies: in most cases assimilation of vertical velocity alone leads to analyses with small errors (e.g. -1 for velocity components) after only 3 or 4 assimilation cycles. The microphysical fields are notable exceptions, exhibiting lower errors when observations of reflectivity are assimilated together with observations of vertical velocity, likely a result of the closer relationship between reflectivity and the microphysical fields themselves. It is also shown that the spatial distribution of the error estimates improves (i.e. approaches the true errors) as more assimilation cycles are carried out, which could be a significant advantage of EnKF model-based retrievals

    Redundant Gs-coupled serotonin receptors regulate amyloid-β metabolism in vivo

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    BACKGROUND: The aggregation of amyloid-β (Aβ) into insoluble plaques is a hallmark pathology of Alzheimer’s disease (AD). Previous work has shown increasing serotonin levels with selective serotonin re-uptake inhibitor (SSRI) compounds reduces Aβ in the brain interstitial fluid (ISF) in a mouse model of AD and in the cerebrospinal fluid of humans. We investigated which serotonin receptor (5-HTR) subtypes and downstream effectors were responsible for this reduction. RESULTS: Agonists of 5-HT(4)R, 5-HT(6)R, and 5-HT(7)R significantly reduced ISF Aβ, but agonists of other receptor subtypes did not. Additionally, inhibition of Protein Kinase A (PKA) blocked the effects of citalopram, an SSRI, on ISF Aβ levels. Serotonin signaling does not appear to change gene expression to reduce Aβ levels in acute timeframes, but likely acts within the cytoplasm to increase α-secretase enzymatic activity. Broad pharmacological inhibition of putative α-secretases increased ISF Aβ and blocked the effects of citalopram. CONCLUSIONS: In total, these studies map the major signaling components linking serotonin receptors to suppression of brain ISF Aβ. These results suggest the reduction in ISF Aβ is mediated by a select group of 5-HTRs and open future avenues for targeted therapy of AD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13024-016-0112-5) contains supplementary material, which is available to authorized users

    Working Memory, Jumping to Conclusions and Emotion Recognition: a Possible Link in First Episode Psychosis (Fep)

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    Introduction A large body of literature has demonstrated that people affected by psychotic disorders show deficits in working memory, in Emotion Recognition (ER) and in data-gathering to reach a decision (Jumping To Conclusions - JTC). Aims To investigate a possible correlation between working memory, JTC and ER in FEP. Methods 41 patients and 89 healthy controls completed assessments of working memory using WAIS shortened version, JTC using the 60:40 Beads Task and ER using Degraded Facial Affect Recognition Task. Results According to the literature, cases had poorer performance in working memory tasks (Digit Span: \u3bc7,72 [ds=2,98] vs \u3bc10,14 [ds=3,10], U=865,00, p=0,00; Digit Symbol: \u3bc5,36 [ds=2,43] vs \u3bc10,05 [ds=3,10], U=455,50, p=0,00; Arithmetic: \u3bc5,46 [ds=2,76] vs \u3bc8,74 [ds=3,24], U=865,50, p=0,00; Block Design: \u3bc4,82 [ds=2,72] vs \u3bc7,60 [ds=3,18], U=912,00, p=0,00), in Beads Task (81,6% vs 51,1%, \u3c72=10,27, p=0,001, \u3bc2,53 [ds=3,57] vs \u3bc4,23 [ds=4,77], U=1171,00, p=0,006) and in DFAR (total errors: \u3bc21,62 [ds=7,43] vs \u3bc16,58 [ds=8,69], U=554,50, p=0,002). Furthermore working memory tasks in cases group correlated significantly with JTC (Digit Span: rrho=0,276, p=0,003; Digit Symbol: rrho=0,275, p=0,002; Arithmetic: rrho=0,265, p=0,003; Block Design: rrho=0,292, p=0,001), but only Digit Span with ER (rrho=-0,239; p=0,021). In addition, we found that JTC and ER were significantly associated (rrho=-0,281; p=0,004). Conclusions Data show that working memory impairments, JTC style and dysfunctions in the facial emotions recognition are phenomena strongly correlated in the group of patients. Preliminary results suggest the importance of early rehabilitation as the impairments detected may lead to difficulties in social and relational adaptation in psychotic patients

    A method to define the priority for maintenance and repair works of Italian motorway tunnels

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    The construction of motorways in Italy dates back to 1921 and still lasts today. Along them there is a large number of tunnels, many of which have been in service for more than 50 years and have experienced various levels of decay due to aging. An extensive assessment and inspection plan is taking place finalized to highlight situations where maintenance and repair works are needed to guarantee the continuation of service in safe conditions and functionality. Due to the number of tunnels, the need arises to classify them and define priorities for intervention on the basis of a first assessment and of a robust and scientific-based tool to orientate the investments. This paper describes the methodology that was developed by the Authors for this purpose, assessing the attention level of every tunnel. The method relies on a quantitative approach that allows quantifying the risk based on five risk factors composed of a number of relevant parameters. Their relative interaction, which guided the scores assigned to each parameter, was assessed by applying the Rock Engineering System [2]. A number of examples of existing tunnels are shown to illustrate the application of the method and to draw conclusions about its validity and reliability

    The use of rituximab in idiopathic inflammatory myopathies: description of a monocentric cohort and review of the literature

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    Rituximab (RTX), a chimeric monoclonal antibody targeted against CD20, has been used to treat refractory inflammatory myopathies (IIM). The primary objective of this study was to retrospectively assess the efficacy of RTX in reducing disease activity in patients with IIM refractory to conventional therapy. Secondary aim was the evaluation of adverse events (AE) during the treatment period. We examined 26 patients with a diagnosis of IIM, referred to our Rheumatology Unit and treated with RTX for active refractory disease. Patients were treated with RTX 1000 mg i.v., twice, with a 2-week interval. RTX treatment was associated with a significant reduction of creatine kinase (p=0.001) after six months compared to the baseline, an improved muscular strength measured with MMT8 (p<0.001) and a reduction of the extramuscular activity of the disease measured with MYOACT (p<0.001). In particular, RTX improved DM skin rash, arthritis and pulmonary manifestations. Autoantibody positivity (in particular antisynthetase, anti- SRP and antiRo/SSA), and a disease duration <36 months at the moment of the treatment are associated with a better response rate. Treatment with RTX was also associated with a reduction of the mean daily dose of steroids needed to control disease activity (p=0.002). Our results have confirmed that RTX is efficacious in the treatment of refractory IIM. Ad hoc controlled trials are needed to better clarify the specific subset of patients who may better respond to the treatment and the optimal therapeutic schedule

    Impact of a 6-wk olive oil supplementation in healthy adults on urinary proteomic biomarkers of coronary artery disease, chronic kidney disease, and diabetes (types 1 and 2): a randomized, parallel, controlled, double-blind study

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    Background: Olive oil (OO) consumption is associated with cardiovascular disease prevention because of both its oleic acid and phenolic contents. The capacity of OO phenolics to protect against low-density lipoprotein (LDL) oxidation is the basis for a health claim by the European Food Safety Authority. Proteomic biomarkers enable an early, presymptomatic diagnosis of disease, which makes them important and effective, but understudied, tools for primary prevention. Objective: We evaluated the impact of supplementation with OO, either low or high in phenolics, on urinary proteomic biomarkers of coronary artery disease (CAD), chronic kidney disease (CKD), and diabetes. Design: Self-reported healthy participants (n = 69) were randomly allocated (stratified block random assignment) according to age and body mass index to supplementation with a daily 20-mL dose of OO either low or high in phenolics (18 compared with 286 mg caffeic acid equivalents per kg, respectively) for 6 wk. Urinary proteomic biomarkers were measured at baseline and 3 and 6 wk alongside blood lipids, the antioxidant capacity, and glycation markers. Results: The consumption of both OOs improved the proteomic CAD score at endpoint compared with baseline (mean improvement: –0.3 for low-phenolic OO and −0.2 for high-phenolic OO; P &#60; 0.01) but not CKD or diabetes proteomic biomarkers. However, there was no difference between groups for changes in proteomic biomarkers or any secondary outcomes including plasma triacylglycerols, oxidized LDL, and LDL cholesterol. Conclusion: In comparison with low-phenolic OO, supplementation for 6 wk with high-phenolic OO does not lead to an improvement in cardiovascular health markers in a healthy cohort. This trial was registered at www.controlled-trials.com as ISRCTN93136746

    Comparing microphysical/dynamical outputs by different cloud resolving models: impact on passive microwave precipitation retrieval from satellite

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    International audienceMesoscale cloud resolving models (CRM's) are often utilized to generate consistent descriptions of the microphysical structure of precipitating clouds, which are then used by physically-based algorithms for retrieving precipitation from satellite-borne microwave radiometers. However, in principle, the simulated upwelling brightness temperatures (TB's) and derived precipitation retrievals generated by means of different CRM's with different microphysical assumptions, may be significantly different even when the models simulate well the storm dynamical and rainfall characteristics. In this paper, we investigate this issue for two well-known models having different treatment of the bulk microphysics, i.e. the UW-NMS and the MM5. To this end, the models are used to simulate the same 24-26 November 2002 flood-producing storm over northern Italy. The model outputs that best reproduce the structure of the storm, as it was observed by the Advanced Microwave Scanning Radiometer (AMSR) onboard the EOS-Aqua satellite, have been used in order to compute the upwelling TB's. Then, these TB's have been utilized for retrieving the precipitation fields from the AMSR observations. Finally, these results are compared in order to provide an indication of the CRM-effect on precipitation retrieval

    Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

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    Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML
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