The ACTH response to dexamethasone in PTSD

Objective: Enhanced negative feedback and reduced adrenal output are two different models that have been put forth to explain the paradoxical observations of increased release of corticotropin-releasing factor in the face of low cortisol levels in posttraumatic stress disorder (PTSID). To discriminate between these models, the authors measured levels of adrenocorticopic hormone (ACTH) and cortisol at baseline and in response to dexamethasone in medically healthy subjects with and without PTSID. Under conditions of enhanced negative feedback inhibition, ACTH levels would not be altered relative to cortisol levels, but the ACTH response to dexamethasone would be augmented, in concert with the enhanced cortisol response to dexamethasone. In contrast, under conditions of reduced adrenal output, ACTH levels would be expected to be higher at baseline relative to cortisol levels, but the ACTH response to dexamethasone would be unchanged in PTSID relative to healthy comparison subjects. Method: The ACTH and cortisol responses to 0.50 mg of dexamethasone were assessed in 19 subjects (15 men and four women) with PTSID and 19 subjects (14 men and five women) without psychiatric disorder. Results: The ACTH-to-cortisol ratio did not differ between groups before or after dexamethasone, but the subjects with PTSD showed greater suppression of ACTH (as well as cortisol) in response to dexamethasone. Conclusions: The data support the hypothesis of enhanced cortisol negative feedback inhibition of ACTH secretion at the level of the pituitary in PTSD. Pituitary glucocorticoid receptor binding, rather than low adrenal output, is implicated as a likely mechanism for this effect.

Experimental separation of the coherent component of X-ray scattering prior to RDF analysis of non-crystalline polymers

An experimental method is described which enables the inelastically scattered X-ray component to be removed from diffractometer data prior to radial density function analysis. At each scattering angle an energy spectrum is generated from a Si(Li) detector combined with a multi-channel analyser from which the coherently scattered component is separated. The data obtained from organic polymers has an improved signal/noise ratio at high values of scattering angle, and a commensurate enhancement of resolution of the RDF at low r is demonstrated for the case of PMMA (ICI `Perspex'). The method obviates the need for the complicated correction for multiple scattering.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.