Deep brain stimulation for essential tremor in patients with ventriculomegaly.

Background: Deep brain stimulation of the nucleus ventralis intermedius (VIM-DBS) is considered a safe and effective treatment for medically intractable essential tremor (ET). However, ventriculomegaly can provide a surgical challenge, as there is an increased risk of breaching the ventricle during the procedure, with potential risk of intraventricular hemorrhage and target displacement. Case Description: In this case series, we report successful bilateral VIM-DBS in a 72-year-old and 69-year-old female ET patient with significant ventriculomegaly. VIM-DBS therapy provided an excellent tremor response. After 5 years, a ventriculoperitoneal shunt was implanted in the first patient due to an incomplete Hakim-Adams triad, with significant improvement in gait and cognition. Conclusion: To the best of our knowledge, we present the first report on VIM-DBS in ET patients with ventriculomegaly and illustrate that VIM-DBS can provide an excellent tremor response in patients with medically intractable ET, even in the context of marked ventriculomegaly.

Algorithms for recollection of search terms based on the Wikipedia category structure.

The common user interface for a search engine consists of a text field where the user can enter queries consisting of one or more keywords. Keyword query based search engines work well when the users have a clear vision what they are looking for and are capable of articulating their query using the same terms as indexed. For our multimedia database containing 202,868 items with text descriptions, we supplement such a search engine with a category-based interface whose category structure is tailored to the content of the database. This facilitates browsing and offers the users the possibility to look for named entities, even if they forgot their names. We demonstrate that this approach allows users who fail to recollect the name of named entities to retrieve data with little effort. In all our experiments, it takes 1 query on a category and on average 2.49 clicks, compared to 5.68 queries on the database's traditional text search engine for a 68.3% success probability or 6.01 queries when the user also turns to Google, for a 97.1% success probability.