Validation of a new self-report measure of parental attributions.

Attributional theory and empirical evidence suggest that a tendency to make stable, global self-causal attributions for undesirable events is associated with negative outcomes. However, existing self-report measures of parental attributions do not account for the possibility that dysfunctional parent-causal attributions for child misbehavior might be important predictors of poor family functioning. To address these concerns, the authors developed and tested a new measure of both parent-causal and child-responsible attributions for child misbehavior in a sample of 453 community couples. Structural validity, convergent validity, discriminant validity, internal consistency, and temporal stability of the new measure were examined. As expected, confirmatory factor analysis resulted in 2 factors, Child-Responsible (9 items) and Parent-Causal (7 items); the final model was cross-validated in a holdout sample. The final scale demonstrated adequate internal consistency (αs = .81–.90), test–retest reliability (rs = .55–.76), and convergent and discriminant validity. Dysfunctional parent-causal and child-responsible attributions significantly predicted parental emotional problems, ineffective discipline, parent–child physical aggression, and low parenting satisfaction. Associations with parent–child aggression and parenting satisfaction were generally larger than with partner aggression and relationship satisfaction. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

DIOS-MSEED: A chip-based method for measurement of enantiomeric excess by kinetic resolution/mass spectrometry

The determination of enantiomeric excess by kinetic resolution mass spectrometry has been implemented with the Desorption/Ionization On Silicon (DIOS) MS technique. Measurements can thereby be made much more rapidly than was previously possible, bringing this general methodology for screening asymmetric catalysts closer to true high-throughput status.     

Automatic Summarization of Changes in Biological Image Sequences Using Algorithmic Information Theory

An algorithmic information-theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG). Time courses of object states are compared using an adaptive information distance measure, aided by a closed-form multidimensional quantization. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. The summary is the clustering result and feature subset that maximize the gap statistic. This approach was validated on four bioimaging applications: 1) It was applied to a synthetic data set containing two populations of cells differing in the rate of growth, for which it correctly identified the two populations and the single feature out of 23 that separated them; 2) it was applied to 59 movies of three types of neuroprosthetic devices being inserted in the brain tissue at three speeds each, for which it correctly identified insertion speed as the primary factor affecting tissue strain; 3) when applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain; and 4) when analyzing intracellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification.