The addition of a second pair of electrodes in the GPi in patients with PGD with suboptimal or decaying benefit following the first surgery seems to be a safe procedure and is not followed by an increase in surgery-related complications. This staged procedure may provide further clinical improvement in patients with PGD in whom DBS effect is initially incomplete or when disease progression occurs over time. The position of the additional electrode within the GPi is determined by the available volume within the posteroventral GPi and by the distribution of the dystonic symptoms that need to be controlled.

Semantic portals are characterized for storing and structuring content according to specific domain ontologies. This content is represented through ontological languages, which enable not only adding semantic value to information treatment, but also inferring new knowledge from it. Publication in a semantic portal is typically done by instantiating its ontology, and this is often performed manually or through the use of specific forms. However, in order to keep portals constantly up-to-date, it is necessary to provide means for a more dynamic publication, integrating the portal content with information retrieved from different ontology-based sites on the same or on complementary domains. Reusing information from different ontologies requires specific and efficient mechanisms to align them, taking into account syntactical and semantical conflicts. This paper proposes an extension of the Crosi Mapping System, a matching mechanism which calculates similarities between ontologies. Some of its original algorithms have been enriched with additional functionality. This extension, named e-CMS, has been evaluated using the OAEI ontology alignment benchmark, and results show an increase of 69% in alignment precision when compared to the CMS original version. In order to illustrate its use, the e-CMS strategy was applied to SiGePoS, a System for Generating Semantic Portals. The semantic module, one of the system components, implements the alignment mechanism between ontologies, which is performed by the e-CMS.

Background:Trigeminal neuralgia (TN) is the most common type of facial neuralgia with incidence of 26.8/100,000 person year. In general, this scenario is characterized by a lancinating, unilateral, paroxysmal pain in the area of the fifth cranial nerve. Several treatment methods, including the injection of ethyl alcohol or butyl alcohol into the ganglion, the glycerol injection into the trigeminal cistern, peripheral nerve divisions, the radiofrequency thermocoagulation of the preganglionic fibers, and radiosurgery has been used for TN.Case Description:A case of a 74-year-old woman patient who undergone a treatment of TN through a compression of Meckel cave and developed a transient abducent palsy is presented. Complication regarding to a palsy of abducent nerve is discussed as well as the analysis of presumable evolving physiopathology. A critical review of literature was performed.Conclusions:Among the procedures, we mean that percutaneous microballoon compression (PMC) is the best choice for elderly frail patients, because it had a very low associated mortality-morbidity rate and does not damage permanent the Gasserian ganglion.

Sport science is a research discipline that aims to understand exercise and apply scientific methods in support of increasing an athlete's performance. In this paper, we present initial results on modeling, managing and analyzing an athlete's data gathered by sport scientists. An Olympic data warehouse is designed initially to support the monitoring of an athlete's biochemical data. A trajectory data model is extended to represent the athlete's measurements along his/her training states, referred to here as metaphoric trajectories. Furthermore, a data warehouse for metaphoric trajectories is designed and two analysis approaches -a relational and a multidimensional one -are evaluated. We compare both approaches and discuss their benefits to the athlete's follow-up analyses applied by sport scientists.At this stage of the project, Sportomics scientists are interested in evaluating the variation on amino acid concentration in an athlete's blood during a training cycle. The latter plans the athlete's activity by organizing it according to training states (such as rest, training, recovery, etc.). At each training cycle, a set of variables is observed, including glucose, lactate, arterial pressure, etc., and their measures captured at each planned training state. In fact, the process of monitoring athletes' biophysical conditions during their training cycles is considered the athletes' follow-up activity. This paper focuses on proposing a strategy for managing and analyzing the data collected during this process.The measures obtained during athletes' training cycles are interpolated to produce a curve representative of an athlete's performance. The analyses of curves therein give insights about the athlete's physical condition and support for his/her training plan. The volume of data to be analyzed is, however, increasing and will soon hamper current manual analytical procedures. If we consider that other research areas are expected to join the project, managing and supporting the athlete's biophysical measure analyses will become impractical in a nonautomated scenario.In this context, a data warehouse for storing, managing and analyzing an Olympic athlete's followup data has been designed. The system aims to fulfill the following requirements: (i) to represent the continuous nature of a follow-up procedure; (ii) to express analytical queries over follow-up data; (iii) to permit data from different disciplines to be represented; and (iv) to provide an acceptable response time when running analytical queries.The data warehouse implements a multidimensional model [2] whose dimensions qualify an athlete's biophysical and biochemical measures. These measures collected during a training cycle, are modeled using a trajectory conceptualization, in which each trajectory point, a stop, corresponds to a measurement value, and the trajectory curve describes the behavior of the athlete between two measurements.The trajectory model allows scientists to express queries on the continuous nature of their measures, such as identifying ...

Abstract. Data warehousing is a collection of concepts and tools which aim at providing and maintaining a set of integrated data (the data warehouse -DW ) for business decision support within an organization. They extract data from different operational data sources, and after some cleansing and transformation procedures data are integrated and loaded into a central repository to enable analysis and mining. Data and metadata lineage are important processes for data analysis. The first allows users to trace warehouse data items back to the original source item from which they were derived and the latter shows which operations have been performed to achieve that target data. This work proposes integrating metadata captured during transformation processes using the CWM metadata standard in order to enable data and metadata lineage. Additionally it presents a tool specially developed for performing this task.