323,058 research outputs found
Correction to: Adiponectin in Cerebrospinal Fluid from Patients Affected by Multiple Sclerosis Is Correlated with the Progression and Severity of Disease (Molecular Neurobiology, (2021), 58, 6, (2663-2670), 10.1007/s12035-021-02287-z)
The original version of this article unfortunately contained some mistakes. The surnames and given names of authors were interchanged. It should be: Elisabetta Signoriello, Marta Mallardo, Ersilia Nigro, Rita Polito, Sara Casertano, Andrea Di Pietro, Marcella Coletta, Maria Ludovica Monaco, Fabiana Rossi, Giacomo Lus, and Aurora Daniele The original article has been corrected
Hierarchical Clustering of histogram data using a "model data" based approach
Histogram data are usually used to represent complex phenomena for which is known not only the range of variability but even the inner variability. Several authors have proposed methods to analyze histogram data taking into account frequencies or density probability. In this paper we propose a different way to analyze histogram data. The idea is to take into account histogram shape. In doing that we propose to approximate histogram by a suitable mathematical model and to use model parameters to analyze phenomena described by means of histogram data. In particular, we will show how to transform histogram data in model data and subsequently how to do a cluster analysis on this data
From histogram data to model data analysis
The histogram data are a symbolic representation that is usually affected by error. In fact there are open discussions about the number and the size of the classes. In order to reduce the error we can transform the histogram data by means of an interpolation function or through adaptation to an ex-ante specified model. So far the symbolic data analysis will be based on the parameters of a set of m functions associated to each characteristic describing n objects
From Histogram Data to Model Data Analysis
The aim of this work is to propose a new approach for dealing with histogram data in symbolic data analysis framework. The idea is to approximate histogram data using B-spline functions in order to synthetize the information within data trough some characteristic function parameters. This parameters will be the new data that could be, subsequently, analyzed with methodologies of multidimensional data analysis
Levetiracetam in submaximal subcutaneous pentylentetrazol-induced seizures in rats.
Despite anticonvulsant efficacy in animal models of generalized epilepsy, levetiracetam was not
effective in the maximal subcutaneous PTZ model in mice and rats.
Aim of this study was to assess the efficacy of levetiracetam (LEV) against submaximal, s.c. MET test
(PTZ at the dose of 70 mg/kg) acute seizures in Wistar rats, in comparison to valproic acid (VPA).
Thirty male Wistar rats (P42) were divided in three drug-treatment groups (10 rats in each group) as
follows: valproic acid, levetiracetam, and controls. All animals were tested for seizure threshold at age
P50. VPA (110 mg/kg) and LEV (108 mg/kg) were freshly dissolved in saline and injected i.p. in 2–3 ml/kg,
15 and 30 min, respectively, before pentylenetetrazol (PTZ) injection at the dose of 70 mg/kg.
The average latency of the seizure type 3 (generalized clonic seizure with loss of righting reflexes)
significantly differed between controls and the drug-treated animal groups (p 0.02). The average
duration of the seizure type 2 (threshold seizure) was significantly longer in both groups compared to
controls (<0.02).
In conclusion, LEV plays a role against seizures triggered by subcutaneous PTZ injection given at
submaximal doses in rats, as demonstrated by a significant increase in duration of the seizure type 2
(threshold seizure)
Local energy planning: how to get started with Markal while serving the community and interconnecting University¿s expertise with the territory
Parameters of ventilatory function in chronic bronchitis [Comportement de quelques paramètres fonctionnels ventilatoires dans la bronchite chronique.]
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