Robot assisted aortic surgery

Wisselink, W. [Promotor]Cuesta Valentin, M.A. [Promotor

Treatment of Pulmonary Sequestrations by Means of Endovascular Embolization: Future or Fashion?

Bronchopulmonary sequestration is a rare malformation of the lower respiratory tract. Several methods of treatment have been described since the first publication. We present two cases of female adult patients with bronchopulmonary sequestration. In the first patient an unsuccessful attempt to treat the bronchopulmonary sequestration by means of arterial embolization is described. She was subsequently treated by means of surgical resection, which was the primary treatment for the second patient. Although endovascular techniques are becoming promising, in our opinion surgical resection remains the unique treatment for bronchopulmonary sequestration

A general lower bound for collaborative tree exploration

We consider collaborative graph exploration with a set of $k$ agents. All agents start at a common vertex of an initially unknown graph and need to collectively visit all other vertices. We assume agents are deterministic, vertices are distinguishable, moves are simultaneous, and we allow agents to communicate globally. For this setting, we give the first non-trivial lower bounds that bridge the gap between small ($k \leq \sqrt n$) and large ($k \geq n$) teams of agents. Remarkably, our bounds tightly connect to existing results in both domains. First, we significantly extend a lower bound of $\Omega(\log k / \log\log k)$ by Dynia et al. on the competitive ratio of a collaborative tree exploration strategy to the range $k \leq n \log^c n$ for any $c \in \mathbb{N}$. Second, we provide a tight lower bound on the number of agents needed for any competitive exploration algorithm. In particular, we show that any collaborative tree exploration algorithm with $k = Dn^{1+o(1)}$ agents has a competitive ratio of $\omega(1)$, while Dereniowski et al. gave an algorithm with $k = Dn^{1+\varepsilon}$ agents and competitive ratio $O(1)$, for any $\varepsilon > 0$ and with $D$ denoting the diameter of the graph. Lastly, we show that, for any exploration algorithm using $k = n$ agents, there exist trees of arbitrarily large height $D$ that require $\Omega(D^2)$ rounds, and we provide a simple algorithm that matches this bound for all trees

Kinase Activity Profiling of Gram-Negative Pneumonia

Pneumonia is a severe disease with high morbidity and mortality. A major causative pathogen is the Gram-negative bacterium Klebsiella (K.) pneumoniae. Kinases play an integral role in the transduction of intracellular signaling cascades and regulate a diverse array of biological processes essential to immune cells. The current study explored signal transduction events during murine Gram-negative pneumonia using a systems biology approach. Kinase activity arrays enable the analysis of 1,024 consensus sequences of protein kinase substrates. Using a kinase activity array on whole lung lysates, cellular kinase activities were determined in a mouse model of K. pneumoniae pneumonia. Notable kinase activities also were validated with phospho-specific Western blots. On the basis of the profiling data, mitogen-activated protein kinase (MAPK) signaling via p42 mitogen-activated protein kinase (p42) and p38 mitogen-activated protein kinase (p38) and transforming growth factor β (TGFβ) activity were reduced during infection, whereas v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) (SRC) activity generally was enhanced. AKT signaling was represented in both metabolic and inflammatory (mitogen-activated protein kinase kinase 2 [MKK], apoptosis signal-regulating kinase/mitogen-activated protein kinase kinase kinase 5 [ASK] and v-raf murine sarcoma viral oncogene homolog B1 [b-RAF]) context. This study reaffirms the importance of classic inflammation pathways, such as MAPK and TGFβ signaling and reveals less known involvement of glycogen synthase kinase 3β (GSK-3β), AKT and SRC signaling cassettes in pneumonia

Divergence Measure Between Chaotic Attractors

We propose a measure of divergence of probability distributions for quantifying the dissimilarity of two chaotic attractors. This measure is defined in terms of a generalized entropy. We illustrate our procedure by considering the effect of additive noise in the well known H\'enon attractor. Comparison of two H\'enon attractors for slighly different parameter values, has shown that the divergence has complex scaling structure. Finally, we show how our approach allows to detect non-stationary events in a time series.Comment: 9 pages, 6 figure

Estimating the distribution of dynamic invariants: illustrated with an application to human photo-plethysmographic time series

Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence intervals, however, such confidence intervals do not reflect variability in the underlying dynamics. We propose a surrogate based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing physiological states and show that correlation dimension and entropy are insufficient to differentiate between these states. In contrast, algorithmic complexity can clearly differentiate between all four rhythms