RESUMO
BACKGROUND AND PURPOSE: Boxing is associated with a high risk of head injuries and increases the likelihood of chronic traumatic encephalopathy. This study explores the effects of sub-concussive impacts on boxers by applying both linear and nonlinear analysis methods to electroencephalogram (EEG) data. METHODS: Twenty-one boxers were selected (mean ± SD, age 28.38 ± 5.5 years; weight 67.55 ± 8.90 kg; years of activity 6.76 ± 5.45; education 14.19 ± 3.08 years) and divided into 'beginner' and 'advanced' groups. The Montreal Cognitive Assessment and the Frontal Assessment Battery were administered; EEG data were collected in both eyes-open (EO) and eyes-closed (EC) conditions during resting states. Analyses of EEG data included normalized power spectral density (nPSD), power law exponent (PLE), detrended fluctuation analysis and multiscale entropy. Statistical analyses were used to compare the groups. RESULTS: Significant differences in nPSD and PLE were observed between the beginner and advanced boxers, with advanced boxers showing decreased mean nPSD and PLE (nPSD 4-7 Hz, p = 0.013; 8-13 Hz, p = 0.003; PLE frontal lobe F3 EC, p = 0.010). Multiscale entropy analysis indicated increased entropy at lower frequencies and decreased entropy at higher frequencies in advanced boxers (F3 EC, p = 0.024; occipital lobe O1 EO, p = 0.029; occipital lobe O2 EO, p = 0.036). These changes are similar to those seen in Alzheimer's disease. CONCLUSION: Nonlinear analysis of EEG data shows potential as a neurophysiological biomarker for detecting the asymptomatic phase of chronic traumatic encephalopathy in boxers. This methodology could help monitor athletes' health and reduce the risk of future neurological injuries in sports.
RESUMO
Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked models in native-like solutions. The best performing clustering approaches we tested indeed lead to more than double the number of cases for which at least one correct solution can be included within the top ten ranked models.