Current drive at plasma densities required for thermonuclear reactors

Progress in thermonuclear fusion energy research based on deuterium plasmas magnetically confined in toroidal tokamak devices requires the development of efficient current drive methods. Previous experiments have shown that plasma current can be driven effectively by externally launched radio frequency power coupled to lower hybrid plasma waves. However, at the high plasma densities required for fusion power plants, the coupled radio frequency power does not penetrate into the plasma core, possibly because of strong wave interactions with the plasma edge. Here we show experiments performed on FTU (Frascati Tokamak Upgrade) based on theoretical predictions that nonlinear interactions diminish when the peripheral plasma electron temperature is high, allowing significant wave penetration at high density. The results show that the coupled radio frequency power can penetrate into high-density plasmas due to weaker plasma edge effects, thus extending the effective range of lower hybrid current drive towards the domain relevant for fusion reactors

Corrigendum: Current drive at plasma densities required for thermonuclear reactors

Nature Communications 1: Article number: 55 (2010); Published: 10 August 2010; Updated:19 September 2013. In Fig. 3 of this Article, the colours of the blue and green curves were accidentally interchanged while the manuscript was being revised. In addition, the x axis labels on Fig. 4 should have read 'Frequency (MHz)'

A Cloud-Based Prediction Framework for Analyzing Business Process Performances

Part 1: The International Cross Domain Conference (CD-ARES 2016)International audienceThis paper presents a framework for analyzing and predicting the performances of a business process, based on historical data gathered during its past enactments. The framework hinges on an inductive-learning technique for discovering a special kind of predictive process models, which can support the run-time prediction of some performance measure (e.g., the remaining processing time or a risk indicator) for an ongoing process instance, based on a modular representation of the process, where major performance-relevant variants of it are equipped with different regression models, and discriminated through context variables. The technique is an original combination of different data mining methods (namely, non-parametric regression methods and a probabilistic trace clustering scheme) and ad hoc data transformation mechanisms, meant to bring the log traces to suitable level of abstraction. In order to overcome the severe scalability limitations of current solutions in the literature, and make our approach really suitable for large logs, both the computation of the trace clusters and of the clusters’ predictors are implemented in a parallel and distributed manner, on top of a cloud-based service-oriented infrastructure. Tests on a real-life log confirmed the validity of the proposed approach, in terms of both effectiveness and scalability

Modeling of the nonlinear mode coupling of lower hybrid waves in tokamak plasmas

A nonlinear wave equation is derived to describe the nonlinear mode coupling of lower hybrid (LH) waves launched in tokamak plasmas, driven by low-frequency (LF) (10 MHz) ion-sound evanescent modes (quasi-modes). The spectrum of the LF fluctuations is calculated considering the beating of the LH wave at the radiofrequency (RF) operating line frequency (pump wave) with the noisy background of the RF power generator. This spectrum is calculated in the frame of the kinetic theory, following a perturbative approach. Numerical solutions of the nonlinear LH wave equation show the evolution of the nonlinear mode coupling in condition of a finite depletion of the pump power. Under operating conditions generally met in the experiments, the sidebands, excited by the noise of RF power generators and amplified by the nonlinearity, broaden the launched antenna spectrum, and determine the LH propagation and deposition behavior

Remote sensing in the fight against environmental crimes: The case study of the cattle-breeding facilities in southern Italy

Enforcement of environmental regulation is a persistent challenge and timely detection of the violations is key to holding the violators accountable. The use of remote sensing data is becoming an effective practice in the fight against environmental crimes. In this work, a novel and effective approach for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multispectral data together with geospatial analyses in the geographic information system (GIS) environment, is proposed. Experiments on data available for the area of Caserta (Southern Italy), show that the proposed technique provides very high detection capability, up to 90%, with a acceptable false alarm rate, becoming a useful tool in the hand of agencies engaged in the protection of territory

An improved in silico selection of phenotype affecting polymorphisms in SLC6A4, HTR1A and HTR2A genes

Objective Among the experimentally assessed DNA variations in serotonin related genes, some influence physiological expression of personality and mental disorders, others alter the responses to pharmacological and/or psychotherapeutic treatments. Because of the huge number of polymorphisms lying in genes and of the great length of time necessary to perform association studies, a selection of the variations being studied is a necessary and crucial step. Methods In this work we used the most updated and assessed bioinformatic tools to predict the phenotype affecting polymorphisms of the human HTR1A, HTR2A and SLC6A4 serotonin related genes. Moreover, we carried out a literature search to collect information about the recent association studies to compare it versus our prediction data. Results Gene polymorphism analysis indicated the variations that are worth considering in the association studies in the field of psychiatry, psychology and pharmacogenomics. The literature revision allowed to show both the few well and the most not enough investigated polymorphisms. Conclusions Our data can be useful to select polymorphisms for new association studies, especially those not yet investigated that can be related to behaviour, mental disorders and individual treatment response. Copyright # 2010 John Wiley & Sons, Ltd. key words—computational biology; serotonin; SNP; association; RNA splicin

How much do we know about the coupling of G-proteins to serotonin receptors?

none6Serotonin receptors are G-protein-coupled receptors (GPCRs) involved in a variety of psychiatric disorders. G-proteins, heterotrimeric complexes that couple to multiple receptors, are activated when their receptor is bound by the appropriate ligand. Activation triggers a cascade of further signalling events that ultimately result in cell function changes. Each of the several known G-protein types can activate multiple pathways. Interestingly, since several G-proteins can couple to the same serotonin receptor type, receptor activation can result in induction of different pathways. To reach a better understanding of the role, interactions and expression of G-proteins a literature search was performed in order to list all the known heterotrimeric combinations and serotonin receptor complexes. Public databases were analysed to collect transcript and protein expression data relating to G-proteins in neural tissues. Only a very small number of heterotrimeric combinations and G-protein-receptor complexes out of the possible thousands suggested by expression data analysis have been examined experimentally. In addition this has mostly been obtained using insect, hamster, rat and, to a lesser extent, human cell lines. Besides highlighting which interactions have not been explored, our findings suggest additional possible interactions that should be examined based on our expression data analysis.Article number 49, Issue July 2014.Giulietti M; Vivenzio V; Piva F; Principato G; Bellantuono C; Nardi BGiulietti, Matteo; Vivenzio, Viviana; Piva, Francesco; Principato, Giovanni; Bellantuono, Cesario; Nardi, Bernard

Stem cell microvesicles transfer cystinosin to human cystinotic cells and reduce cystine accumulation in vitro

Contains fulltext : 109597.pdf (publisher's version ) (Open Access)Cystinosis is a rare disease caused by homozygous mutations of the CTNS gene, encoding a cystine efflux channel in the lysosomal membrane. In Ctns knockout mice, the pathologic intralysosomal accumulation of cystine that drives progressive organ damage can be reversed by infusion of wildtype bone marrow-derived stem cells, but the mechanism involved is unclear since the exogeneous stem cells are rarely integrated into renal tubules. Here we show that human mesenchymal stem cells, from amniotic fluid or bone marrow, reduce pathologic cystine accumulation in co-cultured CTNS mutant fibroblasts or proximal tubular cells from cystinosis patients. This paracrine effect is associated with release into the culture medium of stem cell microvesicles (100-400 nm diameter) containing wildtype cystinosin protein and CTNS mRNA. Isolated stem cell microvesicles reduce target cell cystine accumulation in a dose-dependent, Annexin V-sensitive manner. Microvesicles from stem cells expressing CTNS(Red) transfer tagged CTNS protein to the lysosome/endosome compartment of cystinotic fibroblasts. Our observations suggest that exogenous stem cells may reprogram the biology of mutant tissues by direct microvesicle transfer of membrane-associated wildtype molecules

Tapias score for predicting recurrences in resected solitary fibrous tumor of the pleura: Controversial points and future perspectives emerging from an external validation

Solitary fi brous tumor of the pleura (SFTP) is uncommon and has uncertain and unpredictable prognosis. Rarely attempted, the standardization of prognostic criteria has, so far, faile