The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints such as joint limits, self-collisions, and collisions with the environment remains a challenge. In this context, Riemannian geometry offers promising solutions by identifying robot motions with geodesics on the so-called configuration space manifold. While this manifold naturally considers the intrinsic robot dynamics, constraints such as joint limits, self-collisions, and collisions with the environment remain overlooked. In this paper, we propose a modification of the Riemannian metric of the configuration space manifold allowing for the generation of robot motions as geodesics that efficiently avoid given regions. We introduce a class of Riemannian metrics based on barrier functions that guarantee strict region avoidance by systematically generating accelerations away from no-go regions in joint and task space. We evaluate the proposed Riemannian metric to generate energy-efficient, dynamic-aware, and collision-free motions of a humanoid robot as geodesics and sequences thereof.

Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly overlooked when studying the mechanisms of human movement generation. Inspired by recent works, we hypothesize that human motions are planned as sequences of geodesic synergies, and thus correspond to coordinated joint movements achieved with piecewise minimum energy. The underlying computational model is built on Riemannian geometry to account for the inertial characteristics of the body. Through the analysis of various human arm motions, we find that our model segments motions into geodesic synergies, and successfully predicts observed arm postures, hand trajectories, as well as their respective velocity profiles. Moreover, we show that our analysis can further be exploited to transfer arm motions to robots by reproducing individual human synergies as geodesic paths in the robot configuration space.

Summary: Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs which guide endonucleases to mRNAs of actively transcribed transposons in order to prevent their translation. The resulting mRNA fragments induce a positive feedback loop (the 'ping-pong cycle'), which reinforces piRNA production and hence the transposon-silencing effect. PingPongPro is a command-line tool to scan small RNA-Seq data for signs of ping-pong cycle activity. It implements a novel algorithm that combines empirical probabilities in a multi-factor model to accurately identify transposons which are suppressed through the ping-pong cycle. Availability and implementation: Source code, a user manual, and binaries for Microsoft Windows and Linux are available at https://github.com/suhrig/pingpongpro under the GPLv3 license. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: PCR clonal artefacts originating from NGS library preparation can affect both genomic as well as RNA-Seq applications when protocols are pushed to their limits. In RNA-Seq however the artifactual reads are not easy to tell apart from normal read duplication due to natural over-sequencing of highly expressed genes. Especially when working with little input material or single cells assessing the fraction of duplicate reads is an important quality control step for NGS data sets. Up to now there are only tools to calculate the global duplication rates that do not take into account the effect of gene expression levels which leaves them of limited use for RNA-Seq data. RESULTS: Here we present the tool dupRadar, which provides an easy means to distinguish the fraction of reads originating in natural duplication due to high expression from the fraction induced by artefacts. dupRadar assesses the fraction of duplicate reads per gene dependent on the expression level. Apart from the Bioconductor package dupRadar we provide shell scripts for easy integration into processing pipelines. CONCLUSIONS: The Bioconductor package dupRadar offers straight-forward methods to assess RNA-Seq datasets for quality issues with PCR duplicates. It is aimed towards simple integration into standard analysis pipelines as a default QC metric that is especially useful for low-input and single cell RNA-Seq data sets.

A comparison of sea-surface wave measurement sensors - including remote sensing sensors is performed utilising data acquired at the research platform FINO 1 in the German Bight. The advantages and disadvantages in regard to planning, operation and maintenance of offshore activities are described.

MOTIVATION: Statistical assessment of cis-regulatory modules (CRMs) is a crucial task in computational biology. Usually, one concludes from exceptional co-occurrences of DNA motifs that the corresponding transcription factors (TFs) are cooperative. However, similar DNA motifs tend to co-occur in random sequences due to high probability of overlapping occurrences. Therefore, it is important to consider similarity of DNA motifs in the statistical assessment. RESULTS: Based on previous work, we propose to adjust the window size for co-occurrence detection. Using the derived approximation, one obtains different window sizes for different sets of DNA motifs depending on their similarities. This ensures that the probability of co-occurrences in random sequences are equal. Applying the approach to selected similar and dissimilar DNA motifs from human TFs shows the necessity of adjustment and confirms the accuracy of the approximation by comparison to simulated data. Furthermore, it becomes clear that approaches ignoring similarities strongly underestimate P-values for cooperativity of TFs with similar DNA motifs. In addition, the approach is extended to deal with overlapping windows. We derive Chen-Stein error bounds for the approximation. Comparing the error bounds for similar and dissimilar DNA motifs shows that the approximation for similar DNA motifs yields large bounds. Hence, one has to be careful using overlapping windows. Based on the error bounds, one can precompute the approximation errors and select an appropriate overlap scheme before running the analysis. AVAILABILITY: Software to perform the calculation for pairs of position frequency matrices (PFMs) is available at http://mosta.molgen.mpg.de as well as C++ source code for downloading.