DMclust, a Density-based Modularity Method for Accurate OTU Picking of 16S rRNA Sequences.

Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is a crucial step in analyzing metagenomic data. Although many methods have been developed, how to obtain an appropriate balance between clustering accuracy and computational efficiency is still a major challenge. A novel density-based modularity clustering method, called DMclust, is proposed in this paper to bin 16S rRNA sequences into OTUs with high clustering accuracy. The DMclust algorithm consists of four main phases. It first searches for the sequence dense group defined as n-sequence community, in which the distance between any two sequences is less than a threshold. Then these dense groups are used to construct a weighted network, where dense groups are viewed as nodes, each pair of dense groups is connected by an edge, and the distance of pairwise groups represents the weight of the edge. Then, a modularity-based community detection method is employed to generate the preclusters. Finally, the remaining sequences are assigned to their nearest preclusters to form OTUs. Compared with existing widely used methods, the experimental results on several metagenomic datasets show that DMclust has higher accurate clustering performance with acceptable memory usage.

Rider trunk and bicycle pose estimation with fusion of force/inertial sensors.

Estimation of human pose in physical human-machine interactions such as bicycling is challenging because of highly-dimensional human motion and lack of inexpensive, effective motion sensors. In this paper, we present a computational scheme to estimate both the rider trunk pose and the bicycle roll angle using only inertial and force sensors. The estimation scheme is built on a rider-bicycle dynamic model and the fusion of the wearable inertial sensors and the bicycle force sensors. We take advantages of the attractive properties of the robust force measurements and the motion-sensitive inertial measurements. The rider-bicycle dynamic model provides the underlying relationship between the force and the inertial measurements. The extended Kalman filter-based sensor fusion design fully incorporates the dynamic effects of the force measurements. The performance of the estimation scheme is demonstrated through extensive indoor and outdoor riding experiments.