Assessment of social cognition in bipolar disorder

Background: Impairment in social cognition may contribute to deficits in social functioning in patients with bipolar disorder (BD). In this study, a complex social cognition task was administered during a neuroimaging session. The behavioral and neural correlates of social cogniton in patients with BD were compared to healthy comparison (HC) subjects. Methods: The task was administered to 25 HC and 25 patients with depression scores ranging from euthymic to depressed at the time of assessment. The task required participants to evaluate situations that were “enhancing” or “threatening” to self-esteem, directed at both oneself, and at other people. For instance, self-esteem enhancing scenarios involved vignettes of activities such as receiving praise during a sports game, while a threatening scenario involved, for example, receiving criticism at a party. Participants were then required to evaluate characters in the scenarios on the basis of positive (“kind”) or negative (“mean”) descriptors. Evaluations were classified from extremely negative to extremely positive. The frequencies of behavioral responses were analyzed using chi-square tests and fMRI data were analyzed using Statistical Parametric Mapping software. Results: Patients differed significantly from HCs in their evaluation of threatening scenarios, directed at both oneself and at other people (p<0.001). Patients had a lower proportion of responses in the neutral category, and more responses in the positive and negative categories, relative to HCs. Neuroimaging results reveal differential patterns of prefrontal-cortical and limbic-subcortical activation in BDs throughout the task [p<0.05 (unc.)]. Conclusions: Findings will contribute to understanding difficulty in interpersonal functioning in patients with BD.

Quantitative analysis of solid-state processes studied with isothermal microcalorimetry

Quantitative analysis of solid-state processes from isothermal microcalorimetric data is straightforward if data for the total process have been recorded and problematic (in the more likely case) when they have not. Data are usually plotted as a function of fraction reacted (α); for calorimetric data, this requires knowledge of the total heat change (Q) upon completion of the process. Determination of Q is difficult in cases where the process is fast (initial data missing) or slow (final data missing). Here we introduce several mathematical methods that allow the direct calculation of Q by selection of data points when only partial data are present, based on analysis with the Pérez-Maqueda model. All methods in addition allow direct determination of the reaction mechanism descriptors m and n and from this the rate constant, k. The validity of the methods is tested with the use of simulated calorimetric data, and we introduce a graphical method for generating solid-state power-time data. The methods are then applied to the crystallization of indomethacin from a glass. All methods correctly recovered the total reaction enthalpy (16.6 J) and suggested that the crystallization followed an Avrami model. The rate constants for crystallization were determined to be 3.98 × 10-6, 4.13 × 10-6, and 3.98 × 10 -6 s-1 with methods 1, 2, and 3, respectively. © 2010 American Chemical Society.

Representing clinical documents to support automatic retrieval of evidence from the Cochrane Library

The overall aim of our research is to develop a clinical information retrieval system that retrieves systematic reviews and underlying clinical studies from the Cochrane Library to support physician decision making. We believe that in order to accomplish this goal we need to develop a mechanism for effectively representing documents that will be retrieved by the application. Therefore, as a first step in developing the retrieval application we have developed a methodology that semi-automatically generates high quality indices and applies them as descriptors to documents from The Cochrane Library. In this paper we present a description and implementation of the automatic indexing methodology and an evaluation that demonstrates that enhanced document representation results in the retrieval of relevant documents for clinical queries. We argue that the evaluation of information retrieval applications should also include an evaluation of the quality of the representation of documents that may be retrieved. ©2010 IEEE.

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.