Psychological factors that promote behavior modification by obese patients

<p>Abstract</p> <p>Background</p> <p>The weight-loss effect of team medical care in which counseling is provided by clinical psychologists was investigated in an university hospital obesity (OB) clinic. Nutritional and exercise therapy were also studied. In our previous study, we conducted a randomized, controlled trial with obese patients and confirmed that subjects who received counseling lost significantly more weight than those in a non-counseling group. The purpose of this study was to identify the psychological characteristics assessed by ego states that promote behavior modification by obese patients.</p> <p>Methods</p> <p>147 obese patients (116 females, 31 males; mean age: 45.9 ± 15.4 years) participated in a 6-month weight-loss program in our OB clinic. Their psychosocial characteristics were assessed using the Tokyo University Egogram (TEG) before and after intervention. The Wilcoxon signed rank test was used to compare weight and psychological factors before and after intervention. Multiple regression analysis was used to identify factors affecting weight loss.</p> <p>Results</p> <p>Overall, 101 subjects (68.7%) completed the program, and their data was analyzed. The subjects mean weight loss was 6.2 ± 7.3 kg (<it>Z </it>= 7.72, <it>p </it>< 0.01), and their mean BMI decreased by 2.4 ± 2.7 kg/m²(<it>Z </it>= 7.65, <it>p </it>< 0.01). Significant differences were observed for the Adult (A) ego state (0.68 ± 3.56, <it>Z </it>= 1.95, <it>p </it>< 0.05) and the Free Child (FC) ego state (0.59 ± 2.74, <it>Z </it>= 2.46, <it>p </it>< 0.01). The pre-FC ego state had a significant effect on weight loss (β = 0.33, <it>p </it>< 0.01), and a tendency for changes in the A ego state scores to affect weight loss (β = - 0.20, <it>p </it>= 0.06) was observed.</p> <p>Conclusion</p> <p>This study of a 6-month weight-loss program that included counseling by clinical psychologists confirmed that the A ego state of obese patients, which is related to their self-monitoring skill, and the FC ego state of them, which is related to their autonomy, were increased. Furthermore, the negative aspects of the FC ego state related to optimistic and instinctive characteristics inhibited the behavior modification, while the A ego state represented objective self-monitoring skills that may have contributed to weight loss.</p

Psychiatric-disorder-related behavioral phenotypes and cortical hyperactivity in a mouse model of 3q29 deletion syndrome

3q29 microdeletion, a rare recurrent copy number variant (CNV), greatly confers an increased risk of psychiatric disorders, such as schizophrenia and autism spectrum disorder (ASD), as well as intellectual disability. However, disease-relevant cellular phenotypes of 3q29 deletion syndrome remain to be identified. To reveal the molecular and cellular etiology of 3q29 deletion syndrome, we generated a mouse model of human 3q29 deletion syndrome by chromosome engineering, which achieved construct validity. 3q29 deletion (Df/+) mice showed reduced body weight and brain volume and, more importantly, impaired social interaction and prepulse inhibition. Importantly, the schizophrenia-related impaired prepulse inhibition was reversed by administration of antipsychotics. These findings are reminiscent of the growth defects and neuropsychiatric behavioral phenotypes in patients with 3q29 deletion syndrome and exemplify that the mouse model achieves some part of face validity and predictive validity. Unbiased whole-brain imaging revealed that neuronal hyperactivation after a behavioral task was strikingly exaggerated in a restricted region of the cortex of Df/+ mice. We further elucidated the cellular phenotypes of neuronal hyperactivation and the reduction of parvalbumin expression in the cortex of Df/+ mice. Thus, the 3q29 mouse model provides invaluable insight into the disease-causative molecular and cellular pathology of psychiatric disorders

Potentials of Branch Predictors — from Entropy Viewpoints —

Abstract. Predictors essentially predicts the most recent events based on the record of past events, history. It is obvious that prediction performance largely relies on regularity–randomness level of the history. This paper concentrates on extracting effective information out from branch history, and discuss expected performance of branch predictors. For this purpose, this paper introduces entropy point-of-views to quantitative characterization of both program behavior and prediction mechanisms. This paper defines two new entropies independent of prediction methods and other two entropies dependent on predictor organization. These new entropies are useful tools for analyzing upper-bound of prediction performance. This paper shows some evaluation results for typical predictors.