BACKGROUND: Microbial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes. RESULTS: To avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well. CONCLUSIONS: The proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization.

Security threats increase as connectivity among vehicles increases. In particular, a lack of authentication, integrity, and confidentiality makes the controller area network (CAN) protocol, which is used in critical domains such as vehicle body and powertrain, vulnerable to threats. In this paper, we propose methods for CAN security enhancement that use a support vector machine (SVM) and the autocorrelation of the received signal to detect a malicious node. Robustness to temperature variation is also considered because autocorrelation is affected by temperature variation. There are two methods based on the degree of uniformity of the temperature distribution. If the temperature is uniformly distributed over the vehicle and the temperature sensor is embedded in the secure node, the first scheme (temperature measurement system) trains data in each segmented temperature range more precisely using multiple classifiers. If not (i.e., a nonuniform temperature distribution or an absence of a temperature sensor), the alternative scheme (all-temperature training system) trains data in all temperature ranges with a single classifier. The performances of the proposed systems are evaluated on a testbed. The proposed method can operate without modifying the CAN protocol because it is based on the characteristics of the physical layer. In addition, security can be enhanced redundantly by the system running independently without authentication protocols.