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Trichotillomania is a clinical psychiatric manifestation involving significant hair loss in association with recurrent hair-pulling behavior, the etiology of which is still unknown. The aim of this study was to investigate the genetic and environmental and behavioral contribution to trichotillomania (hair-pulling disorder) and to relate the findings to contemporary theories about the etiology. This was a descriptive correlational and twins study design. 672 twins (MZ=474; DZ=202) were selected from Iranian twins associations in 2018. The Massachusetts General Hospital Hair-pulling Scale(1995) and the Self-Report of Zygosity were used as research instrument. Twin modeling methods were employed to decompose the variance in the liability trichotillomania (hair-pulling disorder) into additive genetic and shared and non-shared environmental factors. The data was analyzed by SPSS, STATA and M-plus. Univariate model-fitting analyses showed that genetic factors accounted for approximately 64% of the variance in TTM. The best extracted model for TTM was the DE model. The correlation coefficient results indicated that the correlation rate was 0/61 and 0/31 in MZ and DZ group respectively. It can be concluded that TTM phenotype is the results of a polygenetic. There is no intermediate despite of the dominance effect and it results from a gene-gene interaction or a dominant gene.

Introduction: The new DSM-5 “Obsessive-Compulsive and Related Disorders” chapter contains a series of conditions supposed to be etiologically related to obsessive-compulsive disorder (OCD). This study aimed to investigate the genetic and environmental contribution to Skin Picking Disorder and relate the findings to contemporary theories about the etiology of obsessive-compulsive disorder.Methods: This research was a twin study design. 672 twins (Mz=474; DZ=202) were selected from twins associations in some cities in Iran in 2019. The Skin Picking Scale and the Self-Report of Zygosity were used as the research instruments. Twin modeling methods were employed to decompose the variance in the liability to excoriation (skin-picking) disorder (SPD) into additive genetic and shared and non-shared environmental factors. SPSS-26, Stata-17 and M-plus version 8.3.2 analyzed the data.Results: Univariate model-fitting analyses showed that genetic factors accounted for approximately 80% of the variance in SPD, whereas non-shared environmental factors and measurement error were responsible for the remaining variance. Shared environmental factors were negligible. The best-fitted model for SPD was DE.Conclusion: The results indicate that SPD has a strong heritability. Therefore, family and molecular genetic studies are required to delineate the role of candidate genes and non-shared environmental factors.

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.