It is supposed that increased glycolysis is crucial for the energy supply during tumor progression. Unfortunately, the relevance of glycolysis in cervical neoplasia is unknown, but what is certain is the fact that cervical cancer shows a high expression of glucose membrane transporters, which are necessary for glucose uptake as an energy source. Transketolase-like enzyme 1 (TKTL1) and the oncogene p-Akt have been described to play an important role in glycolysis during tumorigenesis. Thus, we were interested in their expression in cervical tissue.We examined the expression of TKTL1 and p-Akt in 80 formalin-fixed, paraffin-embedded cervical specimens: 20 benign cervical tissues, 20 low-grade squamous intraepithelial lesions, 20 high-grade intraepithelial lesions, and 20 invasive squamous cell carcinomas (ISCC).Immunhistochemical analyses revealed that the intensity of the expression of TKTL1 and p-Akt increases significantly with an increase in the histopathological grade of cervical tissues.The results suggest that both TKTL1 and p-Akt play an important role in the progression of cervical neoplasia, which may be due to their impact on glycolysis.
The $k$-means algorithm is one of the most widely used clustering heuristics. Despite its simplicity, analyzing its running time and quality of approximation is surprisingly difficult and can lead to deep insights that can be used to improve the algorithm. In this paper we survey the recent results in this direction as well as several extension of the basic $k$-means method.
In the Steiner Forest problem, we are given a graph and a collection of source-sink pairs, and the goal is to find a subgraph of minimum total length such that all pairs are connected. The problem is APX-Hard and can be 2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein, and Ravi from 1995. We give a local-search-based constant-factor approximation for the problem. Local search brings in new techniques to an area that has for long not seen any improvements and might be a step towards a combinatorial algorithm for the more general survivable network design problem. Moreover, local search was an essential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic Steiner Forest is still wide open. It is easy to see that any constant factor local search algorithm requires steps that add/drop many edges together. We propose natural local moves which, at each step, either (a) add a shortest path in the current graph and then drop a bunch of inessential edges, or (b) add a set of edges to the current solution. This second type of moves is motivated by the potential function we use to measure progress, combining the cost of the solution with a penalty for each connected component. Our carefully-chosen local moves and potential function work in tandem to eliminate bad local minima that arise when using more traditional local moves.
Im Juni 2007 hat das Europa-Kolleg Hamburg gemeinsam mit mehreren Anwaltskanzleien das 4. Hamburger Kartellrechtssymposium zum Thema „Patente, Prognosen, Kooperationen: Aktuelle Fragen der Kartellrechtsanwendung“ durchgeführt. Mit dem vorliegenden Band werden nun die Beiträge zum vierten Symposium der Öffentlichkeit zugänglich gemacht, ergänzt durch einige Anhänge, in denen Dokumente abgedruckt werden, auf die sich die Beiträge bezogen haben. Das facettenreiche Thema war veranlasst durch verschiedene aktuelle Entwicklungen im Spannungsfeld zwischen gewerblichen Schutzrechen und Kartellrecht, im Bereich der Beurteilung von Unternehmenszusammenschlüssen auf der Grundlage von Marktprognosen sowie im Bereich der Einkaufsgemeinschaften. Die Referate haben diese Entwicklungen im Einzelnen analysiert und zur Diskussion gestellt. Zu den Teilnehmern am Symposium gehörten neben den Referenten aus der Wissenschaft sowie aus der anwaltlichen und der kartellbehördlichen Praxis vor allem Vertreter aus Anwaltschaft, Wirtschaft, Verwaltung und Justiz sowie Studentinnen und Studenten des Postgraduiertenstudiengangs Master of European Studies, der in Kooperation mit der Universität Hamburg am Europa-Kolleg Hamburg durchgeführt wird.
Adult granulosa cell tumors of the ovary (GCTs) are sex cord stromal tumors of unpredictable behaviour. Up to now, the prediction of the relapsing/malignant potential remains difficult. CD56 (NCAM) in GCTs was previously described in only two studies. However, the expression of its isoforms was not examined.30 GCTs (16 primaries, 14 relapses) were investigated immunohistochemically with antibodies against Pan-CD56 (CD56Pan) and the isoform with 140/180 kDa length (CD56140/180 kDa). The reaction was assessed with respect to percentage of positive cells and intensity of staining.In all GCTs, CD56Pan was expressed, but differences were found between primaries and relapses. The percentage of CD56Pan positive tumor cells was lower in relapses, whereas CD56140/180 kDa showed a higher staining intensity in the latter.Expression of CD56 is an additional sensitive and helpful immunohistochemical tool for histopathologists diagnosing a GCT. It does not seem possible to provide a validly individual risk assessment. However, the different expression of CD56 isoforms might indicate important changes in the course to a more malignant behaviour.
We study metric data structures for curves in doubling spaces, such as trajectories of moving objects in Euclidean $\mathbb{R}^d$, where the distance between two curves is measured using the discrete Fréchet distance. We design data structures in an \emph{asymmetric} setting where the input is a curve (or a set of $n$ curves) each of complexity $m$ and the queries are with curves of complexity $k\ll m$. We show that there exist approximate data structures that are independent of the input size $N = d \cdot n \cdot m$ and we study how to maintain them dynamically if the input is given in the stream. Concretely, we study two types of data structures: (i) distance oracles, where the task is to store a compressed version of the input curve, which can be used to answer queries for the distance of a query curve to the input curve, and (ii) nearest-neighbor data structures, where the task is to preprocess a set of input curves to answer queries for the input curve closest to the query curve. In both cases we are interested in approximation. For curves embedded in Euclidean $\mathbb{R}^d$ with constant $d$, our distance oracle uses space in $\mathcal{O}((k \log(ε^{-1}) ε^{-d})^k)$ ($ε$ is the precision parameter). The oracle performs $(1+ε)$-approximate queries in time in $\mathcal{O}(k^2)$ and is deterministic. We show how to maintain this distance oracle in the stream using polylogarithmic additional memory. In the stream, we can dynamically answer distance queries to the portion of the stream seen so far in $\mathcal{O}(k^4 \log^2 m)$ time. We apply our techniques to the second problem, approximate near neighbor (ANN) data structures, and achieve an exponential improvement in the dependency on the complexity of the input curves compared to the state of the art.
Summary— Loop diuretics of the benzoic acid and aryloxyacetic acid families inhibit Na+K+Cl − cotransport. The ranking order of potencies measured in the thick ascending limb of Henle's loop and the ranking order of affinities for [ 3 H]piretanide receptors on renal plasma membranes are the same. Potencies and affinities correlate well (correlation coefficient r = 0.959 for the medulla and r = 0.951 for the cortex). Therefore, measurement of [ 1 H]piretanide binding is proposed to facilitate screening for loop diuretic action.
