Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome-phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.

Significant research has examined social media users' self-presentation strategies, both within a single account and across multiple platforms. Few studies, however, have considered how users' self-presentation varies across multiple accounts on a single platform. In this survey study, we examine how Instagram users manage content sharing decisions and the audience for their content across two types of Instagram accounts: Rinsta ("real" Instagram) and Finsta ("fake" Instagram). Data analyzed includes 499 Finsta and Rinsta posts rated across different dimensions of self-presentation (e.g., self-presentation intention, picture editing effort) and 453 open-ended responses about participants' rationales for using the two accounts. Through this mixed-method approach, we find that Rinsta accounts are more likely to be public, focus on positive and uplifting content, and involve more careful editing and selection, while Finsta accounts are used for more off-the-cuff, emotional, and inappropriate content, as well as those photos that do not make the Rinsta cut. Participants also rated how they perceived responses to their Finsta and Rinsta posts. Mediation analysis suggests that participants perceived responses to Finsta posts as less satisfying, useful, and supportive - partly because Finsta posts contained more negative emotions and received fewer comments than Rinsta posts. We discuss these findings in relation to prior work on social media, self-presentation, and platform affordances, and consider the benefits of this more time-intensive approach to managing two distinct personas on one platform.

Most social media platforms record, display, and archive users' personal histories. This persistence of posts over time can be problematic, as users' self-presentation goals and network composition change, but old content remains. In this paper, we explore an alternative feature that provides control over content persistence. We present findings from interviews with 16 users of the popular Chinese social media platform WeChat Moments. We focused on Moments' Time Limit setting, which makes social media data ephemeral to audiences, but persistent to posters. Interviewees described changes in their self-presentation goals and social network composition over time and reported the Time Limit feature helped them effortlessly manage their desired self-presentation as they matured. Drawing on these findings, we discuss design implications for social media to facilitate greater control over content visibility and persistence, which may have significant benefits for social media users with large and diverse networks.

This letter proposes a data arrangement for fully polarimetric synthetic aperture radar (PolSAR). It is an essential novel method in the use of the rotation transformation in data interpretation. The key point of the proposal is employing a single pixel-based and selective rotation transformation for each pixel before the speckle filtering. The experimental results with ALOS2-PALSAR2 data show that the proposed data arrangement has much higher performance in recognizing double-bounce scattering in the man-made target area. At the same time, it is effective in avoiding the overestimation of double-bounce and/or surface scattering in natural target areas.

Ground penetrating radar (GPR) has been widely applied to detection of subsurface linear targets, such as underground pipes and reinforced rebars in concrete structures. The azimuth direction of a subsurface linear target can be hardly delineated by a commercial single-polarization GPR system. In this paper, a hybrid dual-polarimetric GPR system is employed to detect buried linear objects. A full-polarimetric scattering matrix is extracted from the double-channel GPR reflection signal. A rotation transformation is applied to the scattering matrix to estimate the target azimuth angle. A laboratory experiment was conducted to detect four metal rebars buried in dry sand at different azimuth angle relative to the GPR scan direction. The maximum error of the estimated azimuth direction is less than 15%. It is concluded that radar polarimetry can provide richer information than single-polarization GPR.