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Year of publication
- 2014 (304) (remove)
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- Fachbereich Medizintechnik und Technomathematik (66)
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Keywords
Persistent infection with the high-risk Human Papillomavirus type 16 (HPV 16) is the causative event for the development of cervical cancer and other malignant tumors of the anogenital tract and of the head and neck. Despite many attempts to develop therapeutic vaccines no candidate has entered late clinical trials. An interesting approach is a DNA based vaccine encompassing the nucleotide sequence of the E6 and E7 viral oncoproteins. Because both proteins are consistently expressed in HPV infected cells they represent excellent targets for immune therapy. Here we report the development of 8 DNA vaccine candidates consisting of differently rearranged HPV-16 E6 and E7 sequences within one molecule providing all naturally occurring epitopes but supposedly lacking transforming activity. The HPV sequences were fused to the J-domain and the SV40 enhancer in order to increase immune responses. We demonstrate that one out of the 8 vaccine candidates induces very strong cellular E6- and E7- specific cellular immune responses in mice and, as shown in regression experiments, efficiently controls growth of HPV 16 positive syngeneic tumors. This data demonstrates the potential of this vaccine candidate to control persistent HPV 16 infection that may lead to malignant disease. It also suggests that different sequence rearrangements influence the immunogenecity by an as yet unknown mechanism.
Because of its minor environmental impact, electricity generation using wind power is getting remarkable. The further growth of the wind industry depends on technological solutions to the challenges in production and construction of the turbines. Wind turbine tower vibrations, which limit power generation efficiency and cause fatigue problems with high maintenance costs, count as one of the main structural difficulties in the wind energy sector. To mitigate tower vibrations auxiliary measures are necessary. The effectiveness of tuned mass damper is verified by means of a numeric study on a 5 MW onshore reference wind turbine. Hereby, also seismic-induced vibrations and soil–structure interaction are considered. Acquired results show that tuned mass damper can effectively reduce resonant tower vibrations and improve the fatigue life of wind turbines. This chapter is also concerned with tuned liquid column damper and a semiactive application of it. Due to its geometric versatility and low prime costs, tuned liquid column dampers are a good alternative to other damping measures, in particular for slender structures like wind turbines.
Planning the layout and operation of a technical system is a common task
for an engineer. Typically, the workflow is divided into consecutive stages: First,
the engineer designs the layout of the system, with the help of his experience or of
heuristic methods. Secondly, he finds a control strategy which is often optimized
by simulation. This usually results in a good operating of an unquestioned sys-
tem topology. In contrast, we apply Operations Research (OR) methods to find a
cost-optimal solution for both stages simultaneously via mixed integer program-
ming (MILP). Technical Operations Research (TOR) allows one to find a provable
global optimal solution within the model formulation. However, the modeling error
due to the abstraction of physical reality remains unknown. We address this ubiq-
uitous problem of OR methods by comparing our computational results with mea-
surements in a test rig. For a practical test case we compute a topology and control
strategy via MILP and verify that the objectives are met up to a deviation of 8.7%.
Anthrakidis, Anette ; Herrmann, Ulf ; Mirza, M. F. ; Schorn, Christian ; Schwarzer, Klemens ; Wedding, Philipp ; Weis, Fabian
Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.