An Evaluation of Compression Schemes for Wireless Networks

Data gathering, either for event recognition or for monitoring applications is the primary intention for sensor network deployments. In many cases, data is acquired periodically and autonomously, and simply logged onto secondary storage (e.g. flash memory) either for delayed offline analysis or for on demand burst transfer. Moreover, operational data such as connectivity information, node and network state is typically kept as well. Naturally, measurement and/or connectivity logging comes at a cost. Space for doing so is limited. Finding a good representative model for the data and providing clever coding of information, thus data compression, may be a means to use the available space to its best. In this paper, we explore the design space for data compression for wireless sensor and mesh networks by profiling common, publicly available algorithms. Several goals such as a low overhead in terms of utilized memory and compression time as well as a decent compression ratio have to be well balanced in order to find a simple, yet effective compression scheme.

PET of somatostatin receptor-positive tumors using 64Cu- and 68Ga-somatostatin antagonists: the chelate makes the difference

Somatostatin-based radiolabeled peptides have been successfully introduced into the clinic for targeted imaging and radionuclide therapy of somatostatin receptor (sst)-positive tumors, especially of subtype 2 (sst2). The clinically used peptides are exclusively agonists. Recently, we showed that radiolabeled antagonists may be preferable to agonists because they showed better pharmacokinetics, including higher tumor uptake. Factors determining the performance of radioantagonists have only scarcely been studied. Here, we report on the development and evaluation of four (64)Cu or (68)Ga radioantagonists for PET of sst2-positive tumors.

Making a Difference Beyond the Workplace

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Efficient implementation of discrete Pascal transform using difference operators

An alternative way is provided to define the discrete Pascal transform using difference operators to reveal the fundamental concept of the transform, in both one- and two-dimensional cases, which is extended to cover non-square two-dimensional applications. Efficient modularised implementations are proposed.

What makes the difference between the natural course of a remaining type B dissection after type A repair and a primary type B aortic dissection?

To analyse the outcome and need for intervention [surgery or thoracic endovascular aortic repair (TEVAR)] in patients after surgery for remaining type B dissection after type A repair and primary type B aortic dissection.