Abstract
The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.

Abstract
This article analyzes experimentally the limitations observed in a 45 km Raman amplified 4 x 40 Gb/s transmission system operating under multiple pump and single high pump power configurations. A signal-to-noise ratio of 19.2 dB, an extinction ratio of 10.4 dB and a jitter of 1097 fs are achieved for moderate pump powers and higher order stimulated Brillouin scattering lines give rise to system degradation for pump power levels more than 0.5 W. (C) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:14031407, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26827

Abstract
In soccer, like in business, results are often the best indicator of a team's performance in a certain competition but insufficient to a coach to asses his team performance. As a consequence, measurement tools play an important role in this particular field. In this research work, a performance tool for soccer, based only in Cartesian coordinates is presented. Capable of calculating final game statistics, suisber of shots, the calculus methodology analyzes the game in a sequential manner, starting with the identification of the kick event (the basis for detecting all events), which is related with a positive variation in the ball's velocity vector. The achieved results were quite satisfactory, mainly due to the number of successfully detected events in the validation process (based on manual annotation). For the majority of the statistics, these values are above 92% and only in the case of shots do these values drop to numbers between 74 and 85%. In the future, this methodology could be improved, especially regarding the shot statistics, integrated with a real-time localization system, or expanded for other collective sports games, such as hockey or basketball.

Abstract
In this article we investigate the combination of meta-learning and optimization algorithms for parameter selection. We discuss our general proposal as well as present the recent develop-ments and experiments performed using Support Vector Machines (SVMs). Meta-learning was combined to single and multi-objective optimization techniques to select SVM parameters. The hybrid meth-ods derived from the proposal presented better results on predictive accuracy than the use of traditional optimization techniques.

Abstract
Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and illumination gradients when viewing the inner surface of the gastrointestinal tract. In this paper, we study the invariance properties of Gabor filters and propose a novel descriptor, the autocorrelation Gabor features (AGF). We show that our proposed AGF is invariant to scale, rotation, and illumination changes in the images. We integrate these new features in a texton framework (Texton-AGF) to classify images from two complementary gastroenterology imaging scenarios (chromoendoscopy and narrow-band imaging) broadly into three different groups: normal, precancerous, and cancerous. Results show that they compare favorably to using state-of-the-art texture descriptors for both imaging modalities.

Abstract
The use of textile yarns as data transmission media enables the design of non-obtrusive well-fitting garments as wearable systems. However, textile conductive yarns show impedances higher than those presented by common metal conductors and these values can change with the textile condition and level of stretching, affecting the integrity of the transmitted signal. This work proposes a built-in self-test methodology to test textile yarn interconnections for conventional stuck-at, open and short faults, as well as to verify signal integrity. The test procedure being proposed relies on sampling to generate a digital signal that results from comparing the received signal with pre-defined voltage levels. The obtained signature allows to verify signal integrity parameters: VLmax, VHmin and rise and fall times. Simulation results confirm the validity of the methodology being proposed. © 2012 IEEE.