Leland Stanford Junior University

Siemens Elema Ab

Mattias Ohlsson

Carsten Peterson

Richard Blankenbecler

Lars Edenbrandt

Markus Ringner

Bo Heden

Olle Pahlm

Ralf Rittner

This invention provides a method for protein structure alignment. More particularly, the present invention provides a method for identification, classification and prediction of protein structures. The present invention involves two key ingredients. First, an energy or cost function formulation of the problem simultaneously in terms of binary (Potts) assignment variables and real-valued atomic coordinates. Second, a minimization of the energy or cost function by an iterative method, where in each iteration (1) a mean field method is employed for the assignment variables and (2) exact rotation and/or translation of atomic coordinates is performed, weighted with the corresponding assignment variables.

Method for protein structure alignment

An analysis apparatus such as an ECG apparatus has a control unit which includes an artificial neural network for discovering signal-recording electrodes which are erroneously attached to a patient. At least one artificial neural network is taught by being fed measurement signals from both correctly recorded measurements and from erroneously recorded measurements. The artificial neural network is then able to identify erroneous attachments with great accuracy from recorded measurement signals.

Medical apparatus for analyzing electrical signals from a patient

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Lund, Sweden

Mattias Ohlsson