Tasavallan Presidentin tervehdyssanat Euroopan turvallisuus- ja yhteistyökonferenssin avajaisissa Helsingissä 3.7.1973 = speech of welcome by the President of the Republic, Dr. Urho Kekkonen, at the inaugural session of CSCE in Helsinki, July 3rd, 1973

Puhe

Aging effects on selective attention-related electroencephalographic patterns during face encoding.

Previous electrophysiological studies revealed that human faces elicit an early visual event-related potential (ERP) within the occipito-temporal cortex, the N170 component. Although face perception has been proposed to rely on automatic processing, the impact of selective attention on N170 remains controversial both in young and elderly individuals. Using early visual ERP and alpha power analysis, we assessed the influence of aging on selective attention to faces during delayed-recognition tasks for face and letter stimuli, examining 36 elderly and 20 young adults with preserved cognition. Face recognition performance worsened with age. Aging induced a latency delay of the N1 component for faces and letters, as well as of the face N170 component. Contrasting with letters, ignored faces elicited larger N1 and N170 components than attended faces in both age groups. This counterintuitive attention effect on face processing persisted when scenes replaced letters. In contrast with young, elderly subjects failed to suppress irrelevant letters when attending faces. Whereas attended stimuli induced a parietal alpha band desynchronization within 300-1000 ms post-stimulus with bilateral-to-right distribution for faces and left lateralization for letters, ignored and passively viewed stimuli elicited a central alpha synchronization larger on the right hemisphere. Aging delayed the latency of this alpha synchronization for both face and letter stimuli, and reduced its amplitude for ignored letters. These results suggest that due to their social relevance, human faces may cause paradoxical attention effects on early visual ERP components, but they still undergo classical top-down control as a function of endogenous selective attention. Aging does not affect the face bottom-up alerting mechanism but reduces the top-down suppression of distracting letters, possibly impinging upon face recognition, and more generally delays the top-down suppression of task-irrelevant information.

Speech prognosis and need of pharyngeal flap for non syndromic vs syndromic Pierre Robin Sequence

BACKGROUND: The aim of this retrospective study was to evaluate speech outcome and need of a pharyngeal flap in children born with nonsyndromic Pierre Robin Sequence (nsPRS) vs syndromic Pierre Robin Sequence (sPRS). METHODS: Pierre Robin Sequence was diagnosed when the triad microretrognathia, glossoptosis, and cleft palate were present. Children were classified at birth in 3 categories depending on respiratory and feeding problems. The Borel-Maisonny classification was used to score the velopharyngeal insufficiency. RESULTS: The study was based on 38 children followed from 1985 to 2006. For the 25 nsPRS, 9 (36%) pharyngeal flaps were performed with improvements of the phonatory score in the 3 categories. For the 13 sPRS, 3 (23%) pharyngeal flaps were performed with an improvement of the phonatory scores in the 3 children. There was no statistical difference between the nsPRS and sPRS groups (P = .3) even if we compared the children in the 3 categories (P = .2). CONCLUSIONS: Children born with nsPRS did not have a better prognosis of speech outcome than children born with sPRS. Respiratory and feeding problems at birth did not seem to be correlated with speech outcome. This is important when informing parents on the prognosis of long-term therapy

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.