Abstract: The present invention provides a method for diagnosing or detecting an autoimmune disease in an individual, the method comprising or consisting of the steps of (a) providing a sample obtained from an individual to be tested; and (b) measuring the presence and/or amount in the test sample of one or more biomarkers selected from the group defined in Table 1(A), Table 1(B), Table 1(C), Table 1(D) and/or Table 1(E) wherein the presence and/or amount in the sample of the one or more biomarker(s) is indicative of an autoimmune disease in the individual. The invention also provides an array and a kit suitable for use in the methods of the invention.

Abstract: 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.

Abstract: In a packet communication system, the delay time needed in a jitter buffer is determined, enabling a smooth data feed to an application without excessive delays, by methods and apparatus that vary the size of the jitter buffer based on an estimated variation of packet transmission delay derived from the times of arrival of stored packets. A variance buffer stores variances of the times of arrival of stored packets, and the estimated variation of packet transmission delay is derived from the stored variances. The size of the jitter buffer can be changed preferentially during periods of discontinuous packet transmission.

Abstract: 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.

Abstract: 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.