Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution

Surface myoelectric signals often appear to carry more information than what is resolved in root mean square analysis of the progress curves or in its power spectrum. Time-frequency analysis of myoelectric signals has not yet led to satisfactory results in respect of separating simultaneous events in time and frequency. In this study a time-frequency analysis of the intensities in time series was developed. This intensity analysis uses a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal. Special procedures were developed to calculate intensity in such a way as to approximate the power of the signal in time. Applied to an EMG signal the intensity analysis was called a functional EMG analysis. The method resolves events within the EMG signal. The time when the events occur and their intensity and frequency distribution are well resolved in the intensity patterns extracted from the EMG signal. Averaging intensity patterns from multiple experiments resolve repeatable functional aspects of muscle activation. Various properties of the functional EMG analysis were shown and discussed using model EMG data and real EMG data.     

From response to stimulus: adaptive sampling in sensory physiology

Sensory systems extract behaviorally relevant information from a continuous stream of complex high-dimensional input signals. Understanding the detailed dynamics and precise neural code, even of a single neuron, is therefore a non-trivial task. Automated closed-loop approaches that integrate data analysis in the experimental design ease the investigation of sensory systems in three directions: First, adaptive sampling speeds up the data acquisition and thus increases the yield of an experiment. Second, model-driven stimulus exploration improves the quality of experimental data needed to discriminate between alternative hypotheses. Third, information-theoretic data analyses open up novel ways to search for those stimuli that are most efficient in driving a given neuron in terms of its firing rate or coding quality. Examples from different sensory systems show that, in all three directions, substantial progress can be achieved once rapid online data analysis, adaptive sampling, and computational modeling are tightly integrated into experiments.     

From Markovian to pairwise epidemic models and the performance of moment closure approximations

Many if not all models of disease transmission on networks can be linked to the exact state-based Markovian formulation. However the large number of equations for any system of realistic size limits their applicability to small populations. As a result, most modelling work relies on simulation and pairwise models. In this paper, for a simple SIS dynamics on an arbitrary network, we formalise the link between a well known pairwise model and the exact Markovian formulation. This involves the rigorous derivation of the exact ODE model at the level of pairs in terms of the expected number of pairs and triples. The exact system is then closed using two different closures, one well established and one that has been recently proposed. A new interpretation of both closures is presented, which explains several of their previously observed properties. The closed dynamical systems are solved numerically and the results are compared to output from individual-based stochastic simulations. This is done for a range of networks with the same average degree and clustering coefficient but generated using different algorithms. It is shown that the ability of the pairwise system to accurately model an epidemic is fundamentally dependent on the underlying large-scale network structure. We show that the existing pairwise models are a good fit for certain types of network but have to be used with caution as higher-order network structures may compromise their effectiveness.     

From arctic lemmings to adaptive dynamics: Charles Elton's legacy in population ecology

We shall examine the impact of Charles S. Elton's 1924 article on periodic fluctuations in animal populations on the development of modern population ecology. We argue that his impact has been substantial and that during the past 75 years of research on multi-annual periodic fluctuations in numbers of voles, lemmings, hares, lynx and game animals he has contributed much to the contemporary understanding of the causes and consequences of population regulation. Elton was convinced that the cause of the regular fluctuations was climatic variation. To support this conclusion, he examined long-term population data then available. Despite his firm belief in a climatic cause of the self-repeating periodic dynamics which many species display, Elton was insightful and far-sighted enough to outline many of the other hypotheses since put forward as an explanation for the enigmatic long-term dynamics of some animal populations. An interesting, but largely neglected aspect in Elton's paper is that it ends with speculation regarding the evolutionary consequences of periodic population fluctuations. The modern understanding of these issues will also be scrutinised here. In population ecology, Elton's 1924 paper has spawned a whole industry of research on populations displaying multi-annual periodicity. Despite the efforts of numerous research teams and individuals focusing on the origins of multi-annual population cycles, and despite the early availability of different explanatory hypotheses, we are still lacking rigorous tests of some of these hypotheses and, consequently, a consensus of the causes of periodic fluctuations in animal populations. Although Elton would have been happy to see so much effort spent on cyclic populations, we also argue that it is unfortunate if this focus on a special case of population dynamics should distract our attention from more general problems in population and community dynamics.     

From nestling calls to fledgling silence: adaptive timing of change in response to aerial alarm calls

Young birds and mammals are extremely vulnerable to predators and so should benefit from responding to parental alarm calls warning of danger. However, young often respond differently from adults. This difference may reflect: (i) an imperfect stage in the gradual development of adult behaviour or (ii) an adaptation to different vulnerability. Altricial birds provide an excellent model to test for adaptive changes with age in response to alarm calls, because fledglings are vulnerable to a different range of predators than nestlings. For example, a flying hawk is irrelevant to a nestling in a enclosed nest, but is dangerous to that individual once it has left the nest, so we predict that young develop a response to aerial alarm calls to coincide with fledging. Supporting our prediction, recently fledged white-browed scrubwrens, Sericornis frontalis, fell silent immediately after playback of their parents' aerial alarm call, whereas nestlings continued to calling despite hearing the playback. Young scrubwrens are therefore exquisitely adapted to the changing risks faced during development.     

From Lab to Field Conditions: A Pilot Study on EEG Methodology in Applied Sports Sciences

Although neurophysiological aspects have become more important in sports and exercise sciences in the last years, it was not possible to measure cortical activity during performance outside a laboratory due to equipment limits or movement artifacts in particular. With this pilot study we want to investigate whether Electroencephalography (EEG) data obtained in a laboratory golf putting performance differ from a suitable putting task under field conditions. Therefore, parameters of the working memory (frontal Theta and parietal Alpha 2 power) were recorded during these two conditions. Statistical calculations demonstrated a significant difference only for Theta power at F4 regarding the two putting conditions “field” and “laboratory”. These findings support the idea that brain activity patterns obtained under laboratory conditions are comparable but not equivalent to those obtained under field conditions. Additionally, we were able to show that the EEG methodology seems to be a reliable tool to observe brain activity under field conditions in a golf putting task. However, considering the still existing problems of movement artifacts during EEG measurements, eligible sports and exercises are limited to those being relatively motionless during execution. Further studies are needed to confirm these pilot results.     

From Oscillatory Transcranial Current Stimulation to Scalp EEG Changes: A Biophysical and Physiological Modeling Study

Both biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.     

Phylogentic constraints,adaptive syndromes,and emergent properties: From individuals to population dynamics

The hypothesis is developed that there are causal linkages in evolved insect herbivore life histories and behaviors from phylogenetic constraints to adaptive syndromes to the emergent properties involving ecological interactions and population dynamics. Thus the argument is developed that the evolutionary biology of a species predetermines its current ecology.Phylogenetic Constraints refer to old characters in the phylogeny of a species and a group of species which set limits on the range of life history patterns and behaviors that can evolve. For example, a sawfly is commonly limited to oviposition in soft plant tissue, while plants are growing rapidly.Adaptive Syndromes are evolutionary responses to the phylogenetic constraints that minimize the limitations and maximize larval performance. Such syndromes commonly involve details of female ovipositional behavior and how individuals make choices for oviposition sites relative to plant quality variation which maximize larval survival. Syndromes also involve larval adaptations to the kinds of choices females make in oviposition. The evolutionary biology involved with phylogenetic constraints and adaptive syndromes commonly predetermines the ecological interactions of a species and its population dynamics. Therefore, these ecological interactions are calledEmergent Properties because they are natural consequences of evolved morphology, behavior, and physiology. They commonly strongly influence the three-trophic-level interactions among host plants, insect herbivores, and carnivores, and the relative forces of bottom-up and top-down influences in food webs. The arguments are supported using such examples as galling sawflies and other gallers, shoot-boring moths and beetles, budworms, and forest Macrolepidoptera. The contrasts between outbreak or eruptive species and uncommon and rare species with latent population dynamics are emphasized.     

The prediction of EEG signals using a feedback-structured adaptive rational function filter

 In this article, we present a feedback-structured adaptive rational function filter based on a recursive modified Gram-Schmidt algorithm and apply it to the prediction of an EEG signal that has nonlinear and nonstationary characteristics. For the evaluation of the prediction performance, the proposed filter is compared with other methods, where a single-step prediction and a multi-step prediction are considered for a short-term prediction, and the prediction performance is assessed in normalized mean square error. The experimental results show that the proposed filter shows better performance than other methods considered for the short-term prediction of EEG signals. Received: 22 September 1998 / Accepted in revised form: 29 February 2000     

From DNA to fitness differences: sequences and structures of adaptive variants of Colias phosphoglucose isomerase (PGI)

Colias eurytheme butterflies display extensive allozyme polymorphism in the enzyme phosphoglucose isomerase (PGI). Earlier studies on biochemical and fitness effects of these genotypes found evidence of strong natural selection maintaining this polymorphism in the wild. Here we analyze the molecular features of this polymorphism by sequencing multiple alleles and modeling their structures. PGI is a dimer with rotational symmetry. Each monomer provides a critical residue to the other monomer's catalytic center. Sequenced alleles differ at multiple amino acid positions, including cryptic charge-neutral variation, but most consistent differences among the electromorph alleles are at the charge-changing amino acid sites. Principal candidate sites of selection, identified by structural and functional analyses and by their variants' population frequencies, occur in interpenetrating loops across the interface between monomers, where they may alter subunit interactions and catalytic center geometry. Comparison to a second (and basal) species, Colias meadii, also polymorphic for PGI under natural selection, reveals one fixed amino acid difference between their PGIs, which is located in the interpenetrating loop and accompanies functional differences among their variants. We also study nucleotide variability among the PGI alleles, comparing these data to similar data from another glycolytic enzyme gene, glyceraldehyde-3-phosphate dehydrogenase. Despite extensive nonsynonymous and synonymous polymorphism at PGI in each species, the only base changes fixed between species are the two causing the amino acid replacement; this absence of synonymous fixation yields a significant McDonald-Kreitman test. Analyses of these data suggest historical population expansion. Positive peaks of Tajima's D statistic, representing regions of neutral "hitchhiking," are found around the principal candidate sites of selection. This study provides novel views of molecular-structural mechanisms, and beginnings of historical evidence, for a long-persistent balanced enzyme polymorphism at PGI in these and perhaps other species.     

The adaptive classification of the dynamic spectral patterns in the human EEG]

The classification technique of single spectra based on a matrix of intercorrelation between these spectra and the fixed set of standard spectral patterns (SP) has been put forward. Including in the classification technique a special procedure for automatic adaptation of the standard SP to given EEG records makes it possible to reduce the number of unclassified single spectra to a minimum (6-10%), which we can ignore during comparative analysis of the EEG classification profiles. Using the universal set of standard SP makes it possible to compare the results of classification of different EEG records. The results of the analysis of classification profiles of human multichannel EEG during performance of the memory task on perception of visual images are described in the paper. It has been shown that both the total EEG reorganization associated with the alpha rhythm blockade during eyes opening and less noticeable EEG shifts accompanying changes in the stages of cognitive activity are underlain by a rather differentiated transformations of relative contributions of each type of the SP into the total power spectrum. It has been revealed that a relatively small part (15-20%) of the elementary EEG segments participates in the reorganization of the EEG classification profile.     

Correction of functional disturbances during pregnancy by the method of adaptive EEG biofeedback training

The applicability and efficiency of the electroencephalogram (EEG)-based method of biofeedback (BFB) training in correcting functional disorders in pregnancy have been evaluated in an obstetric/gynecological clinic in 65 pregnant women. Each of them took part in 2–12 examinations, which differed in voluntarily regulated EEG rhythm (a decrease in the θ, an increase in the α, or a decrease in the β rhythm), left-or right-hemispheric location of the EEG derivations, and the duration of the delay (3 or 30 s) of auditory feedback stimuli. As compared to the baseline, the most distinct and significant changes were observed in the α and θ rhythms during medical procedures, testifying to a general relaxing effect of the EEG BFB. The greatest number of successful trials (83.3 ± 4.4%) was observed for the BFB sessions aimed at decreasing the θ activity, and the lowest number (32.0 ± 6.7%), for the attempts at decreasing the EEG β rhythm. Voluntary control of the α rhythm of the EEG yielded a moderate number of successful trials (63.7 ± 5.3%), but it was characterized by the lowest efficiency (demonstrating minimal differences in positive changes in the functional state between successful and unsuccessful trials). The comparatively low effectiveness of voluntary BFB control of the α rhythm may have been determined by its high heterogeneity and suggests the necessity of using more narrowband EEG components in BFB sessions.     

AIPAR: ab initio parametrization of intermolecular potentials for computer simulations

An unambiguous, fully ab initio and automated technique denoted AIPAR (ab initio parametrization) implemented in the SJBR program has been proposed to yield intermolecular interaction potentials between polar molecules and water. The AIPAR procedure has been applied to several organic molecules covering a wide range of structure and functional groups, namely methanol, acetone (propanone), methanethiol (methyl mercaptan), imidazole (1,3-diazole), oxazole and furan. The AIPAR-derived sets of parameters compare well with the empirical OPLS ones, mainly when the all-atoms model is employed in the OPLS procedure. Monte Carlo simulations were performed for an aqueous solution of methanol and for an equimolar binary mixture methanol–water using the AIPAR and OPLS parameters. The thermodynamic and geometric results obtained with the parameters obtained with the AIPAR procedure compare favorably with the OPLS simulations, even for the binary mixture, demonstrating the precision, robustness and transferability of the parameters obtained with the AIPAR procedure.Figure Superimposed configurations of water (without the hydrogen atoms) around the methanol molecule obtained with the AGOA procedure.