A taxonomy of innovation networks

In this discussion paper we develop a theory-based typology of innovation networks with a special focus on public-private collaboration. This taxonomy is theoretically based on the concept of life cycles which is transferred to the context of innovation networks as well as on the mode of network formation which can occur either spontaneous or planned. The taxonomy distinguishes six different types of networks and incorporates two plausible alternative developments that eventually lead to a similar network structure of the two types of networks. From this, important conclusions and recommendations for network actors and policy makers are drawn. --

Habitual Criminal Statutes: Shield or Sword

An essential part of future collision avoidance systems is to be able to predict road curvature. This can be based on vision data, but the lateral movement of leading vehicles can also be used to support road geometry estimation. This paper presents a method for detecting lane departures, including lane changes, of leading vehicles. This information is used to adapt the dynamic models used in the estimation algorithm in order to accommodate for the fact that a lane departure is in progress. The goal is to improve the accuracy of the road geometry estimates, which is affected by the motion of leading vehicles. The significantly improved performance is demonstrated using sensor data from authentic traffic environments

La Crosse virus NSs sequesters Elongin C - a possible mechanism for inducingdegradation of the largest subunit of RNA polymerase II

Viruses in the Bunyaviridae family cause disease in humans ranging from a mild transient fever to viral haemorrhagic fever. The orthobunyavirus genus is the largest within the family and contains the La Crosse virus (LACV). LACV is endemic in the USA, causing 85 % of neuroinvasive viral disease in children under the age of 15. The main pathogenicity factor of LACV is the NSs protein, an inhibitor of the type I interferon (IFN) induction. Previous work in our group identified the mechanism of LACV NSs inhibition. During infection, the NSs protein of LACV induces the proteasomal degradation of the largest subunit, RPB1, in transcription elongating RNA polymerase II. As a possible host cell interactor of LACV NSs that could mediate the degradation of RPB1, was Elongin C identified. Elongin C has been described to have two main functions in the cell: 1) as a subunit of the Elongin complex that increases RNA polymerase II transcription elongation rates, and 2) as a subunit of several cellular and viral ubiquitin E3 ligases. Here, I demonstrate that LACV NSs specifically sequesters Elongin C from the nucleoli, but does not change the sub-cellular localization of the other two subunits of the Elongin complex, Elongin A and B. The LACV NSs re-localization of Elongin C from the nucleoli had minimal effects on the nucleolar structure or the localization of a major nucleolar protein, Nucleolin. The re-localization of Elongin C by LACV NSs could be prevented by inhibiting the main protein export factor of the nucleus, CRM1, but the same inhibition did not rescue RPB1 from degradation. However, siRNA mediated knockdown of Elongin C partially rescued RPB1 from degradation concomitantly with a partially rescued of type I IFN induction. In attempts to map the functional domains of LACV NSs, I was able to dissect the inhibition of general host cell transcription and type I IFN induction. All LACV NSs mutants, generated at conserved sites in the NSs protein throughout the orthobunyavirus genus, had lost the ability to inhibit type I IFN induction while they all retained the inhibition of general transcription. However, two of the mutants did not show robust phenotypes, requiring further studies to clarify their respective roles. For the rest of the mutants, the inhibition of general transcription correlated with RPB1 degradation, while the loss of type I IFN inhibition correlated partly with loss of Elongin C re-localization and/or inhibition of transcriptionally active RPB1. Thus, I have established that the re-localization of Elongin C by LACV NSs might play a role in type I IFN inhibition. Furthermore, I was able to dissect the inhibition of general host transcription and type I IFN induction transcription, pointing towards two different mechanisms of inhibition. General transcription is inhibited by RPB1 degradation, while type I IFN inhibition correlates partly with RNA polymerase II elongation inhibition

Nonlinear state space smoothing using the conditional particle filter

To estimate the smoothing distribution in a nonlinear state space model, we apply the conditional particle filter with ancestor sampling. This gives an iterative algorithm in a Markov chain Monte Carlo fashion, with asymptotic convergence results. The computational complexity is analyzed, and our proposed algorithm is successfully applied to the challenging problem of sensor fusion between ultra-wideband and accelerometer/gyroscope measurements for indoor positioning. It appears to be a competitive alternative to existing nonlinear smoothing algorithms, in particular the forward filtering-backward simulation smoother.Comment: Accepted for the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 201

Data Consistency Approach to Model Validation

In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modelling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data. This is achieved by automatically gauging the models' ability to generate data that is similar to the observed data. Importantly, the criterion follows from the model class itself and is therefore directly applicable to a broad range of inference problems with varying data types, ranging from independent univariate data to high-dimensional time-series. The proposed data consistency criterion is illustrated, evaluated and compared to several well-established methods using three synthetic and two real data sets