Detection of imprinting and heterogeneous maternal effects on high blood pressure using Framingham Heart Study data

Both imprinting and maternal effects could lead to parent-of-origin patterns in complex traits of human disorders. Statistical methods that differentiate these two effects and identify them simultaneously by using family-based data from retrospective studies are available. The usual data structures include case-parents triads and nuclear families with multiple affected siblings. We develop a likelihood-based method to detect imprinting and maternal effects simultaneously using data from prospective studies. The proposed method utilizes both affected and unaffected siblings in nuclear families by modeling familial genotypes and offspring's disease status jointly. Maternal effect is usually modeled as a fixed effect under the assumption that maternal variant allele(s) has (have) identical effect on any offspring. However, recent studies report that different people may carry different amounts of substances encoded by the mother's variant allele(s) (called maternal microchimerism), which could result in heterogeneity of maternal effects. The proposed method incorporates the heterogeneity of maternal effects by adding a random component to the logit of the penetrance. Our method was applied to the Framingham Heart Study data in two steps to detect single-nucleotide polymorphisms (SNPs) that may be associated with high blood pressure. In the first step, SNPs that affect susceptibility of high blood pressure through minor allele, genomic imprinting, or maternal effects were identified by using the proposed model without the random effect component. In the second step, we fitted the mixed effect model to the identified SNPs that have significant maternal effect to detect heterogeneity of the maternal effects

Research on the Application Strategy of Virtual Reality Technology under the Background of Media Integration -- Take Chinese Minnan Culture Short Video as an Example

In this study, an experiment was designed to verify the communication effects and impacts of virtual reality technology on the viewers of a short video on Minnan cultural content. A control experiment and a questionnaire survey were chosen as the main research methods. According to the requirements of the experiment, the research subjects meeting the requirements were selected and divided into the control group and the experimental group. At the end of the experiment, a questionnaire survey was conducted on all the participants. The results and data of the questionnaire were analyzed at the end.By analyzing the data from the results of the questionnaire and with almost similar other variables in the control group, the minor effects of some subjective and objective factors on the experiment were excluded. The samples of the two groups showed significant differences in all eight items of the questionnaire, including the degree of interest in the content of the short videos, the richness of the content of the short videos, the rating of the degree of integration of the traditional culture and the short videos, the rating of the sense of visual experience and satisfaction with the content of this part of the short videos, the willingness to create this kind of short videos, the likelihood of recommending this kind of short videos to your family members or friends, the complete explanation of the connotation of traditional culture by using the short videos, and whether or not it will attract you to watch the similar content the next time, and the question of whether the traditional culture is fully explained through the short videos.It was found by analyzing the result data of this experiment and the questionnaire survey conducted after the experiment. Compared with Group B who watched the short video of Minnan culture supported by virtual reality technology, the audience of Group A had a better sense of video viewing and content experience, and gained a better viewing and using experience. This method makes traditional culture better disseminated and presented with the help of virtual reality technology. Therefore, we believe that combining virtual reality technology with short videos of Minnan culture can better spread the traditional culture of Minnan. And virtual reality technology has the characteristics of low cost and easy to operate. It is convenient for short video creators to choose and create

Research on the External Communication of Chinese Culture Empowered by Short Videos in Rural China -- Take the Short Video Content of YouTube Platform as an Example

Using YouTube as the platform and rural short videos as the entry point, this study investigates how Chinese rural short videos empower the external dissemination of Chinese culture on the YouTube platform. By using comparative analysis and content analysis research methods to select samples and construct categories, the aim is to compare rural and non rural themed Chinese short videos, as well as the similarities and differences in text structure and symbol types between Chinese rural short videos and foreign rural short videos, to analyze the empowerment of rural elements combined with different themes, and to summarize the dissemination content characteristics of Chinese rural short videos that have successfully gained popularity on the YouTube platform

A stabilized multiple time step method for coupled Stokes-Darcy flows and transport model

A stabilized finite element algorithm with different time steps on different physical variables for the coupled Stokes-Darcy flows system with the solution transport is studied. The viscosity in the model is assumed to depend on the concentration. The nonconforming piecewise linear Crouzeix-Raviart element and piecewise constant are used to approximate velocity and pressure in the coupled Stokes-Darcy flows system, and conforming piecewise linear finite element is used to approximate concentration in the transport system. The time derivatives are discretized with different step sizes for the partial differential equations in these two systems. The existence and uniqueness of the approximate solution are unconditionally satisfied. A priori error estimates are established, which also provides a guidance on the ratio of time step sizes with respect to the ratio of the physical parameters. Numerical examples are presented to verify the theoretical results

Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset

Nowadays, billions of videos are online ready to be viewed and shared. Among an enormous volume of videos, some popular ones are widely viewed by online users while the majority attract little attention. Furthermore, within each video, different segments may attract significantly different numbers of views. This phenomenon leads to a challenging yet important problem, namely fine-grained video attractiveness prediction. However, one major obstacle for such a challenging problem is that no suitable benchmark dataset currently exists. To this end, we construct the first fine-grained video attractiveness dataset, which is collected from one of the most popular video websites in the world. In total, the constructed FVAD consists of 1,019 drama episodes with 780.6 hours covering different categories and a wide variety of video contents. Apart from the large amount of videos, hundreds of millions of user behaviors during watching videos are also included, such as "view counts", "fast-forward", "fast-rewind", and so on, where "view counts" reflects the video attractiveness while other engagements capture the interactions between the viewers and videos. First, we demonstrate that video attractiveness and different engagements present different relationships. Second, FVAD provides us an opportunity to study the fine-grained video attractiveness prediction problem. We design different sequential models to perform video attractiveness prediction by relying solely on video contents. The sequential models exploit the multimodal relationships between visual and audio components of the video contents at different levels. Experimental results demonstrate the effectiveness of our proposed sequential models with different visual and audio representations, the necessity of incorporating the two modalities, and the complementary behaviors of the sequential prediction models at different levels.Comment: Accepted by WWW 2018 The Big Web Trac