The canonical correlation between the biological markers of performance and physical fitness in high level judo athletes

The objective of this research was to verify the relationship between biological markers of performance of elite judo athletes and performance in different physical fitness tests. Twenty-one judo athletes were involved in the present observational and correlational study. Dermatoglyphic variables and the 2D:4D digit ratio were considered as biological markers, while the physical fitness variables analyzed were body fat, maximal strength, muscular power, the aerobic and anaerobic profile, and performance in specific tests. The statistics involved canonical correlations and a multivariate technique. A high and significant canonical correlation was observed between groups of variables, the first expressed by 1=0.999 (p<0.0001) and the second by 2=0.997 (p<0.001). It appears that, beyond height and body mass, total ridge count, pattern intensity for fingers and 2D:4D had more canonical loading. The physical fitness component of the first canonical variable incorporated, with high intensity were: the sum of skinfold thickness, the bench press onerepetition maximum (1RM), upper and lower body aerobic power. In the second canonical variable, physical fitness was composed of the squat 1RM, suspension time on the bar, the SJFT-index, and mean power during the upper body Wingate test. The data of this investigation showed the interdependence between biological markers of performance and physical fitness in high level judo athletes.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

High-level cytoplasmic cyclin D1 expression in lymph node metastases from prostate cancer independently predicts early biochemical failure and death in surgically treated patients

To test the prognostic significance of cyclin D1 in nodal-positive prostate cancer.

Auditory motion direction encoding in auditory cortex and high-level visual cortex

The aim of this functional magnetic resonance imaging (fMRI) study was to identify human brain areas that are sensitive to the direction of auditory motion. Such directional sensitivity was assessed in a hypothesis-free manner by analyzing fMRI response patterns across the entire brain volume using a spherical-searchlight approach. In addition, we assessed directional sensitivity in three predefined brain areas that have been associated with auditory motion perception in previous neuroimaging studies. These were the primary auditory cortex, the planum temporale and the visual motion complex (hMT/V5+). Our whole-brain analysis revealed that the direction of sound-source movement could be decoded from fMRI response patterns in the right auditory cortex and in a high-level visual area located in the right lateral occipital cortex. Our region-of-interest-based analysis showed that the decoding of the direction of auditory motion was most reliable with activation patterns of the left and right planum temporale. Auditory motion direction could not be decoded from activation patterns in hMT/V5+. These findings provide further evidence for the planum temporale playing a central role in supporting auditory motion perception. In addition, our findings suggest a cross-modal transfer of directional information to high-level visual cortex in healthy humans.