MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Localization of acoustic sources has attracted a considerable amount of research attention in recent years. A major obstacle to achieving high localization accuracy is the presence of reverberation, the influence of which obviously increases with the number of active speakers in the room. Human hearing is capable of localizing acoustic sources even in extreme conditions. In this study, we propose to combine a method based on human hearing mechanisms and a modified incremental distributed expectation-maximization (IDEM) algorithm. Rather than using phase difference measurements that are modeled by a mixture of complex-valued Gaussians, as proposed in the original IDEM framework, we propose to use time difference of arrival measurements in multiple subbands and model them by a mixture of real-valued truncated Gaussians. Moreover, we propose to first filter the measurements in order to reduce the effect of the multipath conditions. The proposed method is evaluated using both simulated data and real-life recordings.

Intuitive spoken dialogues are a prerequisite for human-robot interaction. In many practical situations, robots must be able to identify and focus on sources of interest in the presence of interfering speakers. Techniques such as spatial filtering and blind source separation are therefore often used, but rely on accurate knowledge of the source location. In practice, sound emitted in enclosed environments is subject to reverberation and noise. Hence, sound source localization must be robust to both diffuse noise due to late reverberation, as well as spurious detections due to early reflections. For improved robustness against reverberation, this paper proposes a novel approach for sound source tracking that constructively exploits the spatial diversity of a microphone array installed in a moving robot. In previous work, we developed speaker localization approaches using expectation-maximization (EM) approaches and using Bayesian approaches. In this paper we propose to combine the EM and Bayesian approach in one framework for improved robustness against reverberation and noise.

The problem of distributed localization for ad hoc wireless acoustic sensor networks (WASNs) is addressed in this paper. WASNs are characterized by low computational resources in each node and by limited connectivity between the nodes. Novel bi-directional tree-based distributed estimation–maximization (DEM) algorithms are proposed to circumvent these inherent limitations. We show that the proposed algorithms are capable of localizing static acoustic sources in reverberant enclosures without a priori information on the number of sources. Unlike serial estimation procedures (like ring-based algorithms), the new algorithms enable simultaneous computations in the nodes and exhibit greater robustness to communication failures. Specifically, the recursive distributed EM (RDEM) variant is better suited to online applications due to its recursive nature. Furthermore, the RDEM outperforms the other proposed variants in terms of convergence speed and simplicity. Performance is demonstrated by an extensive experimental study consisting of both simulated and actual environments.