Experimental prolonged febrile seizures (FS) lead to structural and molecular changes that promote hippocampal hyperexcitability and reduce seizure threshold to further convulsants. However, whether these seizures provoke later-onset epilepsy, as has been suspected in humans, has remained unclear. Previously, intermittent EEGs with behavioural observations for motor seizures failed to demonstrate spontaneous seizures in adult rats subjected to experimental prolonged FS during infancy. Because limbic seizures may be behaviourally subtle, here we determined the presence of spontaneous limbic seizures using chronic video monitoring with concurrent hippocampal and cortical EEGs, in adult rats (starting around 3 months of age) that had sustained experimental FS on postnatal day 10. These subjects were compared with groups that had undergone hyperthermia but in whom seizures had been prevented (hyperthermic controls), as well as with normothermic controls. Only events that fulfilled both EEG and behavioural criteria, i.e. electro-clinical events, were considered spontaneous seizures. EEGs (over 400 recorded hours) were normal in all normothermic and hyperthermic control rats, and none of these animals developed spontaneous seizures. In contrast, prolonged early-life FS evoked spontaneous electro-clinical seizures in 6 out of 17 experimental rats (35.2%). These seizures consisted of sudden freezing (altered consciousness) and typical limbic automatisms that were coupled with polyspike/sharp-wave trains with increasing amplitude and slowing frequency on EEG. In addition, interictal epileptiform discharges were recorded in 15 (88.2%) of the experimental FS group and in none of the controls. The large majority of hippocampally-recorded seizures were heralded by diminished amplitude of cortical EEG, that commenced half a minute prior to the hippocampal ictus and persisted after seizure termination. This suggests a substantial perturbation of normal cortical neuronal activity by these limbic spontaneous seizures. In summary, prolonged experimental FS lead to later-onset limbic (temporal lobe) epilepsy in a significant proportion of rats, and to interictal epileptifom EEG abnormalities in most others, and thus represent a model that may be useful to study the relationship between FS and human temporal lobe epilepsy.

We have designed, implemented and evaluated an end-to-end system spellchecking and autocorrection system that does not require any manually annotated training data. The World Wide Web is used as a large noisy corpus from which we infer knowledge about misspellings and word usage. This is used to build an error model and an n-gram language model. A small secondary set of news texts with artificially inserted misspellings are used to tune confidence classifiers. Because no manual annotation is required, our system can easily be instantiated for new languages. When evaluated on human typed data with real misspellings in English and German, our web-based systems outperform baselines which use candidate corrections based on hand-curated dictionaries. Our system achieves 3.8% total error rate in English. We show similar improvements in preliminary results on artificial data for Russian and Arabic.

BackgroundThe practice of evidence-based medicine (EBM) requires clinicians to integrate their expertise with the latest scientific research. But this is becoming increasingly difficult with the growing numbers of published articles. There is a clear need for better tools to improve clinician's ability to search the primary literature. Randomized clinical trials (RCTs) are the most reliable source of evidence documenting the efficacy of treatment options. This paper describes the retrieval of key sentences from abstracts of RCTs as a step towards helping users find relevant facts about the experimental design of clinical studies.MethodUsing Conditional Random Fields (CRFs), a popular and successful method for natural language processing problems, sentences referring to Intervention, Participants and Outcome Measures are automatically categorized. This is done by extending a previous approach for labeling sentences in an abstract for general categories associated with scientific argumentation or rhetorical roles: Aim, Method, Results and Conclusion. Methods are tested on several corpora of RCT abstracts. First structured abstracts with headings specifically indicating Intervention, Participant and Outcome Measures are used. Also a manually annotated corpus of structured and unstructured abstracts is prepared for testing a classifier that identifies sentences belonging to each category.ResultsUsing CRFs, sentences can be labeled for the four rhetorical roles with F-scores from 0.93–0.98. This outperforms the use of Support Vector Machines. Furthermore, sentences can be automatically labeled for Intervention, Participant and Outcome Measures, in unstructured and structured abstracts where the section headings do not specifically indicate these three topics. F-scores of up to 0.83 and 0.84 are obtained for Intervention and Outcome Measure sentences.ConclusionResults indicate that some of the methodological elements of RCTs are identifiable at the sentence level in both structured and unstructured abstract reports. This is promising in that sentences labeled automatically could potentially form concise summaries, assist in information retrieval and finer-grained extraction.

In this paper, we describe a new methodology to develop mixed-initiative spoken dialog systems, which is based on the extensive use of simulations to accelerate the development process. With the help of simulations, a system providing information about a database of nearly 1000 restaurants in the Boston area has been developed. The simulator can produce thousands of unique dialogs which benefit not only dialog development but also provide data to train the speech recognizer and understanding components, in preparation for real user interactions. Also described is a strategy for creating cooperative responses to user queries, incorporating an intelligent language generation capability that produces content-dependent verbal descriptions of listed items.

The intervention arms of a randomized controlled trials can be identified by machine extraction incorporating syntactic features derived from full sentence parsing.