Agostinho C. Rosa

A simple format for exchange of digitized polygraphic recordings

A simple digital format supporting the technical aspects of exchange and storage of polygraphic signals has been specified. Implementation of the format is simple and independent of hard- or software environments. It allows for any local montages, transducers, prefiltering, sampling frequencies, etc. At present, 7 laboratories in various countries have used the format for exchanging sleep-wake recordings. These exchanges have made it possible to create a common database of sleep records, to compare the analysis algorithms local to the various laboratories to each other by applying these algorithms to identical signals, and to set up a computer-aided interlaboratory evaluation of manual and automatic analysis methods.

The intranuclear mobility of messenger RNA binding proteins is ATP dependent and temperature sensitive.

fAter being released from transcription sites, messenger ribonucleoprotein particles (mRNPs) must reach the nuclear pore complexes in order to be translocated to the cytoplasm. Whether the intranuclear movement of mRNPs results largely from Brownian motion or involves molecular motors remains unknown. Here we have used quantitative photobleaching techniques to monitor the intranuclear mobility of protein components of mRNPs tagged with GFP. The results show that the diffusion coefficients of the poly(A)-binding protein II (PABP2) and the export factor TAP are significantly reduced when these proteins are bound to mRNP complexes, as compared with nonbound proteins. The data further show that the mobility of wild-type PABP2 and TAP, but not of a point mutant variant of PABP2 that fails to bind to RNA, is significantly reduced when cells are ATP depleted or incubated at 22°C. Energy depletion has only minor effects on the intranuclear mobility of a 2,000-kD dextran (which corresponds approximately in size to 40S mRNP particles), suggesting that the reduced mobility of PABP2 and TAP is not caused by a general alteration of the nuclear environment. Taken together, the data suggest that the mobility of mRNPs in the living cell nucleus involves a combination of passive diffusion and ATP-dependent processes.

Gray-scale image enhancement as an automatic process driven by evolution

Image enhancement is the task of applying certain transformations to an input image such as to obtain a visually more pleasant, more detailed, or less noisy output image. The transformation usually requires interpretation and feedback from a human evaluator of the output result image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. This paper introduces a new automatic image enhancement technique driven by an evolutionary optimization process. We propose a novel objective criterion for enhancement, and attempt finding the best image according to the respective criterion. Due to the high complexity of the enhancement criterion proposed, we employ an evolutionary algorithm (EA) as a global search strategy for the best enhancement. We compared our method with other automatic enhancement techniques, like contrast stretching and histogram equalization. Results obtained, both in terms of subjective and objective evaluation, show the superiority of our method.

Individual alpha neurofeedback training effect on short term memory

Memory performance has been reported to be associated with electroencephalogram (EEG) alpha activity. This study aimed to improve short term memory performance by individual alpha neurofeedback training (NFT). With appropriate protocol designed for NFT, the experimental results showed that the participants were able to learn to increase the relative amplitude in individual alpha band during NFT and short term memory performance was significantly enhanced by 20 sessions of NFT. More importantly, further analysis revealed that the improvement of short term memory was positively correlated with the increase of the relative amplitude in the individual upper alpha band during training. In addition, effective strategies for individual alpha training varied among individuals and the most successful mental strategies were related to positive thinking.

Arousal, EEG spectral power and pulse transit time in UARS and mild OSAS subjects

OBJECTIVES Upper airway resistance syndrome (UARS) and obstructive sleep apnea syndrome (OSAS) are associated with arousals and autonomic activation. Pulse transit time (PTT) has been used to recognize transient arousals. We examined the accuracy of PTT to recognize arousals, and the relationship between PTT deflection and visual and non-visual arousals. METHODS Ten UARS and 10 mild OSAS subjects were studied via polysomnography including measurement of esophageal pressure. Electroencephalogram (EEG) spectral power was obtained from central leads. Seven types of events were identified, depending upon the presence or absence of: a sleep-related respiratory event (SRRE), i.e. apnea, hypopnea, and abnormal breathing effort; a PTT signal; or a visually scored arousal (>1.5s). RESULTS One thousand four hundred forty-six events were identified in 20 subjects. Fifty-nine percent of all SRREs were associated with a PTT signal and a visual EEG arousal. Nineteen percent of SRREs had no EEG arousals at their termination, and 7.4% had no associated PTT signal. Delta power was significantly increased when non-visual EEG arousals were scored. The time delay for PTT was determined by the presence or absence of EEG arousal. The sensitivity of PTT to recognize EEG arousal was 90.4% and the specificity was 16.8%. The sensitivity and specificity of PTT to recognize SRRE was 90.7 and 21.9%, respectively. CONCLUSIONS These results preclude the use of PTT by itself. SRREs induce an activation with positive PTT response but without arousal in 14% of cases. This PTT response, however, is much slower than that occurring with arousal. UARS and mild OSAS do not respond in the same way to SRREs, particularly during rapid eye movement sleep.

An experimental study on dynamic random variation of population size

This paper presents an empirical comparative study of evolutionary algorithms with the purpose of determining if, for an evolutionary algorithm (EA), there exists any intrinsic advantage in using a dynamical population size control strategy over an initial arbitrarily setting of the population size, even without any explicitly defined control strategy. A brief survey of previous work on population size parameter control is presented, covering both static and dynamical methods. A classification framework for dynamical control of population size in EAs is proposed. Several strategies are proposed, characterized and applied to well-known binary and numerical test functions, both uni- and multi-modal, and with different degrees of complexity. For the case of the simple generational genetic algorithm, different dynamical strategies and the fixed population size are compared, in terms of the best absolute fitness and the improvement capability. Results indicate that, when no previous information exists, choosing a dynamic random variation control strategy for the population size is a reasonable choice, outperforming blind choices for the fixed settings.

Varying the population size of artificial foraging swarms on time varying landscapes

In this paper we present a Swarm Search Algorithm with varying population of agents based on a previous model with fixed population which proved its effectiveness on several computation problems [6,7,8]. We will show that the variation of the population size provides the swarm with mechanisms that improves its self-adaptability and causes the emergence of a more robust self-organized behavior, resulting in a higher efficiency on searching peaks and valleys over dynamic search landscapes represented here by several three-dimensional mathematical functions that suddenly change over time.

Self-regulated population size in evolutionary algorithms

In this paper we analyze a new method for an adaptive variation of Evolutionary Algorithms (EAs) population size: the Self-Regulated Population size EA (SRP-EA). An empirical evaluation of the method is provided by comparing the new proposal with the CHC algorithm and other well known EAs with varying population. A fitness landscape generator was chosen to test and compare the algorithms: the Spears multimodal function generator. The performance of the algorithms was measured in terms of success rate, quality of the solutions and evaluations needed to attain them over a wide range of problem instances. We will show that SRP-EA performs well on these tests and appears to overcome some recurrent drawbacks of traditional EAs which lead them to local optima premature convergence. Also, unlike other methods, SRP-EA seems to self-regulate its population size according to the state of the search.

niGAVaPS — outbreeding in genetic algorithms

This paper presents niGAVaPS (non-incest Genetic Algorithm with Variable Population Size), a genetic algorithm that mimics some mechanisms of evolution in natural environment populations. It prevents incest by forbidding the recombination of closely related individuals, based on their ancestry. The degree of ancestry considered is adjustable, niGAVaPS tunes the population size parameter according to the state of the search process. Each chromosome has a lifetime, which corresponds to the number of generations in which it will remain in the population. This value is calculated according to the chromosome fitness and population characteristics at the moment of its creation. Preventing incest supposedly helps maintaining population diversity thus avoiding premature convergence. The purpose of our work is to test these ideas on a range of problems, as far as algorithm convergence and quality of solutions found are concerned.

High school weekly timetabling by evolutionary algorithms

This paper describes a method for generating high school timetables using an Evolutionary Algorithm (E.A.). Given c classes. t teachers and r classrooms it is required to build a set of c+t+r timetables based on the needs of the school and satisfying constraints in the assignment of the lessons. A problem specific chromosome representation and the use of a repair function during titness evaluation helps the algorithm, keeping the search close to valid solutions. Our new operator, ‘Bad Genes Mutation’, greatly improved the algorithm’s speed and results. Test results on a large high school are presented. 1 THE SCHOOL TlMETABLlNG PROBLEM In this section we describe in detail the schooltimetabling problem and discuss the hard and soft [Z] constraints considered in the assignment of a lesson. 1.7 Defining the Problem Conditions We define time slot duration as the greatest common factor of all the different lesson durations i.e. if we only have sixty-minute and ninety-minute lessons then the time slot duration will be th’irty minutes. We define the dimension (number of timeslots) of each timetable as: dimension = rrulnber-of-days-wit/l-lessons * number-hne_slotsJerJay The most common division of timetables into time slots used in schools is a sixty-minute time slot. ten times a day and five days a week, as shown in Figure 1. p-ission ~0 m&e di&al or hard copies Of dt Oc @ Oftis wk for p-d 0~ classroom use is pnttd without fee pm”ia thJ copime no( made OT distributed fOr FOffi 01 COll’UnS~