The role of self-efficacy in predicting rule-following behaviors in shelters for homeless youth: A test of the theory of planned behavior

Through a prospective study of 70 youths staying at homeless-youth shelters, the authors tested the utility of I. Ajzen's (1991) theory of planned behavior (TPB), by comparing the constructs of self-efficacy with perceived behavioral control (PBC), in predicting people's rule-following behavior during shelter stays. They performed the 1st wave of data collection through a questionnaire assessing the standard TPB components of attitudes, subjective norms, PBC, and behavioral intentions in relation to following the set rules at youth shelters. Further, they distinguished between items assessing PBC (or perceived control) and those reflecting self-efficacy (or perceived difficulty). At the completion of each youth's stay at the shelter, shelter staff rated the rule adherence for that participant. Regression analyses revealed some support for the TPB in that subjective norm was a significant predictor of intentions. However, self-efficacy emerged as the strongest predictor of intentions and was the only significant predictor of rule-following behavior. Thus, the results of the present study indicate the possibility that self-efficacy is integral to predicting rule adherence within this context and reaffirm the importance of incorporating notions of people's perceived ease or difficulty in performing actions in models of attitude-behavior prediction.

Utility of signs and symptoms of chronic cough in predicting specific cause in children

Background: Paediatricians rely on cough descriptors to direct them to the level of investigations needed for a child presenting with chronic cough, yet there is a lack of published data to support this approach. A study was undertaken to evaluate ( 1) whether historical cough pointers can predict which children have a specific cause for their cough and ( 2) the usefulness of chest radiography and spirometry as standard investigations in children with chronic cough. Methods: This was a prospective cohort study of children referred to a tertiary hospital with a cough lasting 3 weeks between June 2002 and July 2004. All included children completed a detailed history and examination using a standardised data collection sheet and followed a pathway of investigation until a diagnosis was made. Results: In 100 consecutively recruited children of median age 2.8 years, the best predictor of specific cough observed was a moist cough at the time of consultation with an odds ratio ( OR) of 9.34 (95% CI 3.49 to 25.03). Chest examination or chest radiographic abnormalities were also predictive with OR 3.60 ( 95% CI 1.31 to 9.90) and 3.16 (95% CI 1.32 to 7.62), respectively. The most significant historical pointer for predicting a specific cause of the cough was a parental history of moist cough ( sensitivity 96%, specificity 26%, positive predictive value 74%). Conclusions: The most useful clinical marker in predicting specific cough is the presence of a daily moist cough. Both chest examination and chest radiographic abnormalities are also useful in predicting whether children have a specific cause of their cough.

Measuring and predicting the dynamics of linear monodisperse entangled polymers in rapid flow through an abrupt contraction. A small angle neutron scattering study

Small-angle neutron scattering measurements on a series of monodisperse linear entangled polystyrene melts in nonlinear flow through an abrupt 4:1 contraction have been made. Clear signatures of melt deformation and subsequent relaxation can be observed in the scattering patterns, which were taken along the centerline. These data are compared with the predictions of a recently derived molecular theory. Two levels of molecular theory are used: a detailed equation describing the evolution of molecular structure over all length scales relevant to the scattering data and a simplified version of the model, which is suitable for finite element computations. The velocity field for the complex melt flow is computed using the simplified model and scattering predictions are made by feeding these flow histories into the detailed model. The modeling quantitatively captures the full scattering intensity patterns over a broad range of data with independent variation of position within the contraction geometry, bulk flow rate and melt molecular weight. The study provides a strong, quantitative validation of current theoretical ideas concerning the microscopic dynamics of entangled polymers which builds upon existing comparisons with nonlinear mechanical stress data. Furthermore, we are able to confirm the appreciable length scale dependence of relaxation in polymer melts and highlight some wider implications of this phenomenon.

The relative impact of scar and viable tissue in predicting global left ventricular remodeling in medically treated patients

We sought to determine the relative impact of myocardial scar and viability on post-infarct left ventricular (LV) remodeling in medically-treated patients with LV dysfunction. Forty patients with chronic ischemic heart disease (age 64±9, EF 40±11%) underwent rest-redistribution Tl201 SPECT (scar = 50% transmural extent), A global index of scarring for each patient (CMR scar score) was calculated as the sum of transmural extent scores in all segts. LV end diastolic volumes (LVEDV) and LV end systolic volumes (LVESV) were measured by real-time threedimensional echo at baseline and median of 12 months follow-up. There was a significant positive correlation between change in LVEDV with number of scar segts by all three imaging techniques (LVEDV: SPECT scar, r = 0.62, p < 0.001; DbE scar, r = 0.57, p < 0.001; CMR scar, r = 0.52, p < 0.001) but change in LV volumes did not the correlate with number of viable segments. ROC curve analysis showed that remodeling (LVEDV> 15%) was predicted bySPECTscars(AUC= 0.79),DbEscars(AUC= 0.76),CMR scars (AUC= 0.70), and CMR scar score (AUC 0.72). There were no significant differences between any of the ROC curves (Z score

Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein’s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision. ©2005 IEEE

Predicting peroxisomal proteins

PTS1 proteins are peroxisomal matrix proteins that have a well conserved targeting motif at the C-terminal end. However, this motif is present in many non peroxisomal proteins as well, thus predicting peroxisomal proteins involves differentiating fake PTS1 signals from actual ones. In this paper we report on the development of an SVM classifier with a separately trained logistic output function. The model uses an input window containing 12 consecutive residues at the C-terminus and the amino acid composition of the full sequence. The final model gives a Matthews Correlation Coefficient of 0.77, representing an increase of 54% compared with the well-known PeroxiP predictor. We test the model by applying it to several proteomes of eukaryotes for which there is no evidence of a peroxisome, producing a false positive rate of 0.088%.