Molecular dynamics study of the hydration of lanthanum(III) and europium(III) including many-body effects

Lanthanides complexes are widely used as contrast agents in magnetic resonance imaging (MRI) and are involved in many fields such as organic synthesis, catalysis, and nuclear waste management. The complexation of the ion by the solvent or an organic ligand and the resulting properties (for example the relaxivity in MRI) are mainly governed by the structure and dynamics of the coordination shells. All of the MD approachs already carried out for the lanthanide(III) hydration failed due to the lack of accurate representation of many-body effects. We present the first molecular dynamics simulation including these effects that accounts for the experimental results from a structural and dynamic (water exchange rate) point of view

The failed liberalisation of Algeria and the international context: a legacy of stable authoritarianism

The paper attempts to challenge the somewhat marginal role of international factors in the study of transitions to democracy. Theoretical and practical difficulties in proving causal mechanisms between international variables and domestic outcomes can be overcome by defining the international dimension in terms of Western dominance of world politics and by identifying Western actions towards democratising countries. The paper focuses on the case of Algeria, where international factors are key in explaining the initial process of democratisation and its following demise. In particular, the paper argues that direct Western policies, the pressures of the international system and external shocks influence the internal distribution of power and resources, which underpins the different strategies of all domestic actors. The paper concludes that analysis based purely on domestic factors cannot explain the process of democratisation and that international variables must be taken into more serious account and much more detailed

The GEYSERS optical testbed: a platform for the integration, validation and demonstration of cloud-based infrastructure services

The recent evolution of cloud services is leading to a new service transformation paradigm to accommodate network infrastructures in a cost-scalable way. In this transformation, the network constitutes the key to efficiently connect users to services and applications. In this paper we describe the deployment, validation and demonstration of the optical integrated testbed for the “GEneralized architecture for dYnamic infrastructure SERviceS” (GEYSERS) project to accommodate such cloud based Infrastructure Services. The GEYSERS testbed is composed of a set of local physical testbeds allocated in the facilities of the GEYSERS partners. It is built up based on the requirements specification, architecture definition and per-layer development that constitutes the whole GEYSERS ecosystem, and validates the procedures on the GEYSERS prototypes. The testbed includes optical devices (layer 1), switches (layer 2), and IT resources deployed in different local testbeds provided by the project partners and interconnected among them to compose the whole testbed layout. The main goal of the GEYSERS testbed is twofold. On one hand, it aims at providing a validation ground for the architecture, concepts and business models proposed by GEYSERS, sustained by two main paradigms: Infrastructure as a Service (IaaS) and the coupled provisioning of optical network and IT resources. On the other hand, it is used as a demonstration platform for testing the software prototypes within the project and to demonstrate to the research and business community the project approach and solutions. In this work, we discuss our experience in the deployment of the testbed and share the results and insights learned from our trials in the process. Additionally, the paper highlights the most relevant experiments carried out in the testbed, aimed at the validation of the overall GEYSERS architecture

Application of deep learning models to improve ulcerative colitis endoscopic disease activity scoring under multiple scoring systems

Background and Aims Lack of clinical validation and inter-observer variability are two limitations of endoscopic assessment and scoring of disease severity in patients with ulcerative colitis [UC]. We developed a deep learning [DL] model to improve, accelerate and automate UC detection, and predict the Mayo Endoscopic Subscore [MES] and the Ulcerative Colitis Endoscopic Index of Severity [UCEIS]. Methods A total of 134 prospective videos [1550 030 frames] were collected and those with poor quality were excluded. The frames were labelled by experts based on MES and UCEIS scores. The scored frames were used to create a preprocessing pipeline and train multiple convolutional neural networks [CNNs] with proprietary algorithms in order to filter, detect and assess all frames. These frames served as the input for the DL model, with the output being continuous scores for MES and UCEIS [and its components]. A graphical user interface was developed to support both labelling video sections and displaying the predicted disease severity assessment by the artificial intelligence from endoscopic recordings. Results Mean absolute error [MAE] and mean bias were used to evaluate the distance of the continuous model’s predictions from ground truth, and its possible tendency to over/under-predict were excellent for MES and UCEIS. The quadratic weighted kappa used to compare the inter-rater agreement between experts’ labels and the model’s predictions showed strong agreement [0.87, 0.88 at frame-level, 0.88, 0.90 at section-level and 0.90, 0.78 at video-level, for MES and UCEIS, respectively]. Conclusions We present the first fully automated tool that improves the accuracy of the MES and UCEIS, reduces the time between video collection and review, and improves subsequent quality assurance and scoring

On the search for neutrino oscillations using an artificial neutrino source

In this paper the possibility of searching for neutrino oscillations with an artificial neutrino source is discussed and a comparison with reactor experiments is carried out

Accuracy of citrulline, I-FABP and d-lactate in the diagnosis of acute mesenteric ischemia

Data availability: Research data are not shared.Supplementary Information oi available online at: https://www.nature.com/articles/s41598-021-98012-w#Sec14 .Early diagnosis of acute mesenteric ischemia (AMI) remains a clinical challenge, and no biomarker has been consistently validated. We aimed to assess the accuracy of three promising circulating biomarkers for diagnosing AMI—citrulline, intestinal fatty acid-binding protein (I-FABP), and D-lactate. A cross-sectional diagnostic study enrolled AMI patients admitted to the intestinal stroke center and controls with acute abdominal pain of another origin. We included 129 patients—50 AMI and 79 controls. Plasma citrulline concentrations were significantly lower in AMI patients compared to the controls [15.3 μmol/L (12.0–26.0) vs. 23.3 μmol/L (18.3–29.8), p = 0.001]. However, the area under the receiver operating curves (AUROC) for the diagnosis of AMI by Citrulline was low: 0.68 (95% confidence interval = 0.58–0.78). No statistical difference was found in plasma I-FABP and plasma D-lactate concentrations between the AMI and control groups, with an AUROC of 0.44, and 0.40, respectively. In this large cross-sectional study, citrulline, I-FABP, and D-lactate failed to differentiate patients with AMI from patients with acute abdominal pain of another origin. Further research should focus on the discovery of new biomarkers.Grants from MSD-Avenir and APHP funded the SURVIBIO study; Alexandre Nuzzo received Ph.D. Grants from “Fondation de l'Avenir” and the French Gastroenterology Society (SNFGE)

Modification of the LixV2O5 phase diagram by incorporation of chromium oxide

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A kinetic study of lithium transport in the sol–gel Cr0.11V2O5.16 mixed oxide

International audienc