Vienna University of Technology

About: Vienna University of Technology is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Laser & Context (language use). The organization has 16723 authors who have published 49341 publications receiving 1302168 citations.

Likelihood Consensus and Its Application to Distributed Particle Filtering

Abstract: We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task-based on the past and current measurements of all sensors-using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This “likelihood consensus” method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem.

Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans

Abstract: Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding, and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We find significant differences in RNA, translation, and protein levels suggesting diverse mechanisms of personalized gene expression control. Combined analysis of RNA expression and ribosome occupancy improves the identification of individual protein level differences. Finally, we identify genetic differences that specifically modulate ribosome occupancy--many of these differences lie close to start codons and upstream ORFs. Our results reveal a new level of gene expression variation among humans and indicate that genetic variants can cause changes in protein levels through effects on translation.

Carbon catabolite repression of xylanase I (xyn1) gene expression in Trichoderma reesei

Abstract: The filamentous fungus Trichoderma reesei forms two specific, xylan-inducible xylanases encoded by xyn1 and xyn2 to degrade the beta-1,4-D-xylan backbone of hemicelluloses. This enzyme system is formed in the presence of xylan, but not glucose. The molecular basis of the absence of xylanase I formation on glucose was the purpose of this study. Northern blotting of the xyn1 transcript as well as the use of the Escherichia coli hygromycin B phosphotransferase-encoding gene (hph) as a reporter consistently showed that the basal expression of xyn1 was affected by glucose, whereas its induction by xylan remained uninfluenced. The repression of basal xyn1 transcription is mediated by the carbon catabolite repressor protein Cre1, which in vivo binds to two of four consensus sites (5'-SYG-GRG-3') in the xyn1 promoter, which occurred in the form of an inverted repeat. T. reesei strains, bearing a xyn1::hph reporter construct, in which four nucleotides from the middle of the inverted repeat had been removed, expressed hph on glucose at a level comparable to that observed during growth on a carbon catabolite derepressing carbon source. Northern analysis of xyn1 expression in a T. reesei mutant strain (RUT C-30), which contains a truncated, non-functional cre1 gene, also confirmed basal transcription of xyn1. In this strain, xyn1 transcription was still inducible by xylose or xylan to an even higher degree than in the wild-type strain, suggesting that induction overcomes glucose repression at the level of xyn1 expression. Based on these data, we postulate that basal transcription of xyn1 is repressed by glucose and mediated by an inverted repeat of the consensus motif for Cre1-mediated carbon catabolite repression.

The STRANDS Project: Long-Term Autonomy in Everyday Environments

Abstract: Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.