Background: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances. Results: LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples. Conclusion: The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery.

Background.The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances.Methods.Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged.Results.We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources.Discussion.These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.

Mu rhythm is an idling rhythm that originates in the sensorimotor cortex during rest. The frequency of mu rhythm, which is well established in adults, is 8–12 Hz, whereas the limited results available from children suggest a frequency as low as 5.4 Hz at 6 months of age, which gradually increases to the adult value. Understanding the normal development of mu rhythm has important theoretical and clinical implications since we still know very little about this signal in infants and how it develops with age. We measured mu rhythm over the left hemisphere using a pediatric magnetoencephalography (MEG) system in 25 infants (11–47 weeks), 18 preschool children (2–5 years) and 6 adults (20–39 years) for two 5-min sessions during two intermixed conditions: a rest condition in which the hands were at rest, and a prehension condition in which the subject squeezed a pipette with his/her right hand. In all participants, mu rhythm was present over the frontoparietal area during the rest condition, but was clearly suppressed during the prehension condition. Mu rhythm peak frequency, determined from the amplitude spectra, increased rapidly as a function of age from 2.75 Hz at 11 weeks to 8.25 Hz at 47 weeks (r² = 0.83). It increased very slowly during the preschool period (3.1 ± 0.9 years; 8.5 ± 0.54 Hz). The frequency in these children was, however, lower than in adults (10.3 ± 1.2 Hz). Our results show a rapid maturation in spontaneous mu rhythm during the first year of life.