Humans routinely deal with both traditional and novel risks. Different kinds of risks have been a driving force for both evolutionary adaptations and personal development. This study explored the genetic and environmental influences on human risk taking in different task domains. Our approach was threefold. First, we integrated several scales of domain-specific risk-taking propensity and developed a synthetic scale, including both evolutionarily typical and modern risks in the following 7 domains: cooperation/competition, safety, reproduction, natural/physical risk, moral risk, financial risk, and gambling. Second, we conducted a twin study using the scale to estimate the contributions of genes and environment to risk taking in each of these 7 domains. Third, we conducted a series of meta-analyses of extant twin studies across the 7 risk domains. The results showed that individual differences in risk-taking propensity and its consistency across domains were mainly regulated by additive genetic influences and individually unique environmental experiences. The heritability estimates from the meta-analyses ranged from 29% in financial risk taking to 55% in safety. Supporting the notion of risk-domain specificity, both the behavioral and genetic correlations among the 7 domains were generally low. Among the relatively few correlations between pairs of risk domains, our analysis revealed a common genetic factor that regulates moral, financial, and natural/physical risk taking. This is the first effort to separate genetic and environmental influences on risk taking across multiple domains in a single study and integrate the findings of extant twin studies via a series of meta-analyses conducted in different task domains. (PsycINFO Database Record
Nonphotorealistic rendering (NPR) techniques are used to transform real-world images into high-quality aesthetic styles automatically. NPR mainly focuses on transfer hand-painted styles to other content images, and simulates pencil drawing, watercolor painting, sketch painting, Chinese monochromes, calligraphy and, so on. However, digital simulation of Chinese embroidery style has not attracted researcher’s much attention. This study proposes an embroidery style transfer method from a 2D image on the basis of a convolutional neural network (CNN) and evaluates the relevant rendering features. The primary novelty of the rendering technique is that the strokes and needle textures are produced by the CNN and the results can display embroidery styles. The proposed method can not only embody delicate strokes and needle textures but also realize stereoscopic effects to achieve real embroidery features. First, using conditional random fields (CRF), the algorithm segments the target content and the embroidery style images through a semantic segmentation network. Then, the binary mask image is generated to guide the embroidery style transfer for different regions. Next, CNN is used to extract the strokes and texture features from the real embroidery images, and transfer these features to the content images. Finally, the simulating image is generated to show the features of the real embroidery styles. To demonstrate the performance of the proposed method, the simulations are compared with real embroidery artwork and other methods. In addition, the quality evaluation method is used to evaluate the quality of the results. In all the cases, the proposed method is found to achieve needle visual quality of the embroidery styles, thereby laying a foundation for the research and preservation of embroidery works.
Disentangling the genetic and environmental influences of gambling is important for explaining the roots of individual differences in gambling behavior and providing guidance for precaution and intervention, but we are unaware of any comprehensive and systematic quantitative meta-analysis. We systematically identified 18 twin studies on gambling in the meta-analysis. The correlation coefficients within monozygotic (MZ) and dizygotic (DZ) twins, along with the corresponding sample size, were used to calculate the proportion of the total variance accounted for by additive genes (A), dominant genes (D), the shared environment (C), and the non-shared environment plus measurement error (E). We further assessed the moderating effects of gambling assessment (symptom oriented assessment vs. behavior oriented assessment), age, and sex. The whole sample analyses showed moderate additive genetic (a2 = 0.50) and non-shared environmental influences (e2 = 0.50) on gambling. The magnitude of the genetic influence (a2) was higher for disordered gambling assessed with symptom oriented assessment (53%) than for general gambling assessed with behavior oriented assessment (41%). Additionally, the magnitude of the genetic influence (a2) was higher for adults (53%) than adolescents (42%). Genetic influence (a2) was greater for male (47%) gambling than female (28%) gambling. Shared environment had noticeable effects on female gambling (c2 = 14%) but zero effect on male gambling. In conclusion, gambling behavior was moderately heritable and moderately influenced by non-shared environmental factors. Gambling assessment, age, and sex significantly moderated the magnitude of genetic and environmental influences on gambling. Note that the number of studies might serve as a limitation.
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