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The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to Such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.
The general purpose of this systematic review was to summarize, structure and evaluate the findings on automatic evaluations of exercising. Studies were eligible for inclusion if they reported measuring automatic evaluations of exercising with an implicit measure and assessed some kind of exercise variable. Fourteen nonexperimental and six experimental studies (out of a total N = 1,928) were identified and rated by two independent reviewers. The main study characteristics were extracted and the grade of evidence for each study evaluated. First, results revealed a large heterogeneity in the applied measures to assess automatic evaluations of exercising and the exercise variables. Generally, small to large-sized significant relations between automatic evaluations of exercising and exercise variables were identified in the vast majority of studies. The review offers a systematization of the various examined exercise variables and prompts to differentiate more carefully between actually observed exercise behavior (proximal exercise indicator) and associated physiological or psychological variables (distal exercise indicator). Second, a lack of transparent reported reflections on the differing theoretical basis leading to the use of specific implicit measures was observed. Implicit measures should be applied purposefully, taking into consideration the individual advantages or disadvantages of the measures. Third, 12 studies were rated as providing first-grade evidence (lowest grade of evidence), five represent second-grade and three were rated as third-grade evidence. There is a dramatic lack of experimental studies, which are essential for illustrating the cause-effect relation between automatic evaluations of exercising and exercise and investigating under which conditions automatic evaluations of exercising influence behavior. Conclusions about the necessity of exercise interventions targeted at the alteration of automatic evaluations of exercising should therefore not be drawn too hastily.
The decision to exercise is not only bound to rational considerations but also automatic affective processes. The affective–reflective theory of physical inactivity and exercise (ART) proposes a theoretical framework for explaining how the automatic affective process (type‑1 process) will influence exercise behavior, i.e., through the automatic activation of exercise-related associations and a subsequent affective valuation of exercise. This study aimed to empirically test this assumption of the ART with data from 69 study participants. A single-measurement study, including within-subject experimental variation, was conducted. Automatic associations with exercise were first measured with a single-target implicit association test. The somato-affective core of the participants’ automatic valuation of exercise-related pictures was then assessed via heart rate variability (HRV) analysis, and the affective valence of the valuation was tested with a facial expression (FE; smile and frown) task. Exercise behavior was assessed via self-report. Multiple regression (path) analysis revealed that automatic associations predicted HRV reactivity (β = −0.24, p = .044); the signs of the correlation between automatic associations and the smile FE score was in the expected direction but remained nonsignificant (β = −0.21, p = .078). HRV reactivity predicted self-reported exercise behavior (β = −0.28, p = .013) (the same pattern of results was achieved for the frown FE score). The HRV-related results illustrate the potential role of automatic negative affective reactions to the thought of exercise as a restraining force in exercise motivation. For better empirical distinction between the two ART type‑1 process components, automatic associations and the affective valuation should perhaps be measured separately in the future. The results support the notion that automatic and affective processes should be regarded as essential aspects of the motivation to exercise.
Listening to the heart
(2019)
Objective: The affective-reflective theory of physical inactivity and exercise suggests that the mere thought of exercise can lead to an immediate somato-affective response which, if negative, will drive a physically inactive person to maintain his or her current exercise-avoidant behavior. This study aimed to test the assumption that the somatic core of this affective response can be identified by means of heart rate variability (HRV) analysis. Design: This study followed a within-subject experimental design. Method. Participants were 91 adult men and women whose HR and HRV were monitored whilst they viewed exercise-related and control pictures in a laboratory setting. Results: Analyses revealed a decrease in HRV during the viewing of exercise-related pictures in less physically active participants. These participants reported that the same pictures elicited feelings with relatively low affective valence and arousal. There were no changes in HR.
The harmonic oscillator is a powerful model that can appear as a limit case when examining a nonlinear system. A well known fact is that, without driving, the inclusion of a friction term makes the origin of the phase space-which is a fixed point of the system-linearly stable. In this work, we include a telegraph process as perturbation of the oscillator's frequency, for example, to describe the motion of a particle with fluctuating charge gyrating in an external magnetic field. Increasing intensity of this colored noise is capable of changing the quality of the fixed point. To characterize the stability of the system, we use a stability measure that describes the growth of the displacement of the system's phase space position and express it in a closed form. We expand the respective exponent for light friction and low noise intensity and compare both the exact analytic solution and the expansion to numerical values. Our findings allow stability predictions for several physical systems.
On Earth, chemolithoautothrophic and anaerobic microorganisms such as methanogenic archaea are regarded as model organisms for possible subsurface life on Mars. For this reason, the methanogenic strain Methanosarcina soligelidi (formerly called Methanosarcina spec. SMA-21), isolated from permafrost-affected soil in northeast Siberia, has been tested under Martian thermo-physical conditions. In previous studies under simulated Martian conditions, high survival rates of these microorganisms were observed. In our study we present a method to measure methane production as a first attempt to study metabolic activity of methanogenic archaea during simulated conditions approaching conditions of Mars-like environments. To determine methanogenic activity, a measurement technique which is capable to measure the produced methane concentration with high precision and with high temporal resolution is needed. Although there are several methods to detect methane, only a few fulfill all the needed requirements to work within simulated extraterrestrial environments. We have chosen laser spectroscopy, which is a non-destructive technique that measures the methane concentration without sample taking and also can be run continuously. In our simulation, we detected methane production at temperatures down to -5 degrees C, which would be found on Mars either temporarily in the shallow subsurface or continually in the deep subsurface. The pressure of 50 kPa which we used in our experiments, corresponds to the expected pressure in the Martian near subsurface. Our new device proved to be fully functional and the results indicate that the possible existence of methanogenic archaea in Martian subsurface habitats cannot be ruled out. (C) 2013 Published by Elsevier Ltd.
Personality-dependent space use and movement might be crucially influencing ecological interactions, giving way to individual niche specialization. This new approach challenges classical niche theory with potentially great ecological consequences, but so far has only scarce empirical support. Here, we investigated if and how consistent inter-individual differences in behavior predict space use and movement patterns in free-ranging bank voles (Myodes glareolus) and thereby contribute to individual niche specialization. Individuals were captured and marked from three different subpopulations in North-East Germany. Inter-individual differences in boldness and exploration were quantified via repeated standardized tests directly in the field after capture. Subsequently, space use and movement patterns of a representative sample of the behavioral variation (n=21 individuals) were monitored via automated VHF telemetry for a period of four days, yielding on average 384 locations per individual. Bolder individuals occupied larger home ranges and core areas (estimated via kernel density analyses), moved longer distances, spatially overlapped with fewer conspecifics and preferred different microhabitats based on vegetation cover compared to shyer individuals. We found evidence for personality-dependent space use, movement, and occupation of individual spatial niches in bank voles. Thus, besides dietary niche specialization also spatial dimensions of ecological niches vary among individuals within populations, which may have important consequences for ecological interactions within- and between species.
My niche
(2020)
Intraspecific trait variation is an important determinant of fundamental ecological interactions. Many of these interactions are mediated by behaviour. Therefore, interindividual differences in behaviour should contribute to individual niche specialization. Comparable with variation in morphological traits, behavioural differentiation between individuals should limit similarity among competitors and thus act as a mechanism maintaining within-species variation in ecological niches and facilitating species coexistence. Here, we aimed to test whether interindividual differences in boldness covary with spatial interactions within and between two ecologically similar, co-occurring rodent species (Myodes glareolus, Apodemus agrarius). In five subpopulations in northeast Germany, we quantified individual differences in boldness via repeated standardized tests and spatial interaction patterns via capture-mark- recapture (n = 126) and automated VHF telemetry (n = 36). We found that boldness varied with space use in both species. Individuals of the same population occupied different spatial niches, which resulted in non-random patterns of within- and between-species spatial interactions. Behavioural types mainly differed in the relative importance of intra- versus interspecific competition. Within-species variation along this competition gradient could contribute to maintaining individual niche specialization. Moreover, behavioural differentiation between individuals limits similarity among competitors, which might facilitate the coexistence of functionally equivalent species and, thus, affect community dynamics and local biodiversity.
Matching dependencies (MDs) are data profiling results that are often used for data integration, data cleaning, and entity matching. They are a generalization of functional dependencies (FDs) matching similar rather than same elements. As their discovery is very difficult, existing profiling algorithms find either only small subsets of all MDs or their scope is limited to only small datasets.
We focus on the efficient discovery of all interesting MDs in real-world datasets. For this purpose, we propose HyMD, a novel MD discovery algorithm that finds all minimal, non-trivial MDs within given similarity boundaries. The algorithm extracts the exact similarity thresholds for the individual MDs from the data instead of using predefined similarity thresholds. For this reason, it is the first approach to solve the MD discovery problem in an exact and truly complete way. If needed, the algorithm can, however, enforce certain properties on the reported MDs, such as disjointness and minimum support, to focus the discovery on such results that are actually required by downstream use cases. HyMD is technically a hybrid approach that combines the two most popular dependency discovery strategies in related work: lattice traversal and inference from record pairs. Despite the additional effort of finding exact similarity thresholds for all MD candidates, the algorithm is still able to efficiently process large datasets, e.g., datasets larger than 3 GB.