An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.

When surfacers do not dive : multiple significance of extended surface times in marine turtles

Marine turtles spend more than 90% of their life underwater and have been termed surfacers as opposed to divers. Nonetheless turtles have been reported occasionally to float motionless at the surface but the reasons for this behaviour are not clear. We investigated the location, timing and duration of extended surface times (ESTs) in 10 free-ranging loggerhead turtles (Caretta caretta) and the possible relationship to water temperature and diving activity recorded via satellite relay data loggers for 101–450 days. For one turtle that dived only in offshore areas, ESTs contributed 12% of the time whereas for the other turtles ESTs contributed 0.4–1.8% of the time. ESTs lasted on average 90 min but were mostly infrequent and irregular, excluding the involvement of a fundamental regulatory function. However, 82% of the ESTs occurred during daylight, mostly around noon, suggesting a dependence on solar radiation. For three turtles, there was an appreciable (7°C to 10.5°C) temperature decrease with depth for dives during periods when ESTs occurred frequently, suggesting a re-warming function of EST to compensate for decreased body temperatures, possibly to enhance digestive efficiency. A positive correlation between body mass and EST duration supported this explanation. By contrast, night-active turtles that exceeded their calculated aerobic dive limits in 7.6–16% of the dives engaged in nocturnal ESTs, probably for lactate clearance. This is the first evidence that loggerhead turtles may refrain from diving for at least two reasons, either to absorb solar radiation or to recover from anaerobic activity.

Enhancing medical named entity recognition with an extended segment representation technique

Objective : The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications.

Methods : An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy.

Results : The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.