Analysis of the physiological stress profile: the interrupted time-series design

Most research employing physiological stress profiling procedures has relied upon statistical analyses that are flawed in two ways: These analyses typically average the physiological levels observed across a series of observations during any one phase of the profile, thereby ignoring rate of change as a relevant parameter, and the problem of autocorrelation, or the natural correlation of time-series observations of the same physiologic activity, is unaddressed. We hope to introduce the biofeedback field to the technique of interrupted time-series analysis, which effectively deals with these two flaws. Interrupted time-series analysis additionally permits statistical conclusions based upon the stress profile of a single individual, thereby providing an objective basis for decisions concerning the effectiveness of training or the timing of changes in training for a single client. We describe the application of the technique to the analysis of a stress profile of a subject diagnosed as hypertensive.     

Biological Time Series Analysis Using a Context Free Language: Applicability to Pulsatile Hormone Data

We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals.     

Pre-service Teachers and Stress During Microteaching: An Experimental Investigation of the Effectiveness of Relaxation Training with Biofeedback on Psychological and Physiological Indices of Stress

Pre-service teacher stress is an understudied research area, with the majority of research focusing on subjective reports of stress. The present study sought to examine the influence of stress-reduction techniques on both subjective and objective indicators of stress during microteaching in preservice teachers. A sample of 44 preservice teachers were randomly assigned to one of three intervention groups; biofeedback, relaxation, or control. Participants in the biofeedback group received relaxation-assisted biofeedback training designed to teach participants the physiological signs of the stress response using HeartMath monitor, along with the HeartMath Quick Coherence® technique. Those in the relaxation group were given training in the HeartMath Quick Coherence relaxation technique, with no biofeedback training. Finally, those in the control group did not receive any relaxation or biofeedback training. Using a repeated-measures design, both psychological and physiological indices of stress were measured before and after students engaged in microteaching approaches. Examination of the psychological ratings identified that feelings of calm increased across time; this showed that participants were more comfortable with the microteaching situation with repeated practice. However, none of the physiological interventions were effective in reducing stress. The present study highlights practice as a useful strategy to reduce stress in microteaching situations and points to the importance of employing evidence-based interventions when attempting to reduce stress.     

Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population

We investigated the isozyme profiles of antioxidant enzymes in cultivars and lines with different seed productivity in cool climate conditions as a step towards understanding the physiological and genetical mechanisms underlying chilling tolerance in soybean. While no difference in superoxide dismutase, or catalase isozyme profiles was observed among the cultivars and lines tested, we found polymorphism in the ascorbate peroxidase isozyme profile; there were two types, with or without a cytosolic isoform (APX1). The cultivars and lines lacking APX1 proved more tolerant to chilling temperatures, as evaluated by yielding ability. The genotype-dependent deficiency of APX1 was consistent in plants and tissues under various oxidative stress conditions including the exposure to low-temperatures. In addition, the genetic analysis of progeny derived from crossing between cultivars differing in the isozyme profile indicated that the APX1 deficiency is controlled by a single recessive gene (apx1), and is inherited independently of the genes that have previously been identified for their association with chilling tolerance. Molecular and linkage analyses suggested that the variant gene of the APX1-absent genotype coding for a cytosolic APX, which contained a single nucleotide substitution and a single nucleotide deletion in the coding region, is responsible for the genotype-dependent deficiency of APX1. The association of APX1 deficiency with chilling tolerance is discussed in detail.     

Effects of biofeedback-assisted attention training in a college population

A program of stress management employing open-focus attention-training workshops was developed at Baruch College to bring the benefits of stress reduction to students. The purpose of the research reported here was to evaluate the results of the open-focus attention-training technique. Open-focus technique without biofeedback training was used for two semesters. Biofeedback training was incorporated in the third semester. In the first study, changes in grade point average (GPA), stress-related symptoms, and physiological measures were examined. The experimental subjects' stress data for this study was reported previously (Valdés, 1985). In the second study, changes in the same variables for experimental and control subjects were evaluated. Students in the control group showed decreased GPA, while those who participated in open-focus training showed a trend toward improved GPA. Stress-related symptoms associated with anxiety and management of emotional problems showed significant posttraining improvement, as did physiological measures in all of the biofeedback modalities in which the experimental subjects were specifically trained. The results support the hypothesis that the workshops were successful in reducing stress levels, and suggest that further controlled research be conducted to verify these findings, and to identify the most effective components of the training procedure.Portions of this report were presented at the Fifteenth Annual Meeting of the Biofeedback Society of America, Alburquerque, March 1984. Special thanks and acknowledgement are due to Dr. Manus Rabinowitz, professor of statistics and computer information systems; Ms. Angela Anselmo, M. S. Ed., of the Department of Compensatory Programs, and Mr. Eric Laner, a graduate assistant. Professor Rabinowitz was responsible for the study, development, and evaluation of statistical data, tests, and computer programs. Ms. Anselmo assisted in conducting the workshops.     

Multimetric statistical analysis of behavior in mice selected for high and low levels of isolation-induced male aggression

Behavioral observations as a matrix of probabilistic changes of postures and acts are multiple measurements that could introduce variability to statistical analysis. We propose the multimetric statistical algorithm that supplements the linear analysis of variance by pair correlation, factor and discriminant function analyses. Although these methods were utilized mostly in behavioral studies, the combined use in frame of one behavioral test was not done before. In present study statistical techniques were applied to analyze social behavior in Turku aggressive (TA) and Turku non-aggressive (TNA) mouse lines, bidirectional selected for offensive aggression towards an unknown male. Each statistical technique amplified new details of mouse behavioral profiles that give possibility to describe TA and TNA subjects in terms of Cloninger's model of personality. Also, it was identified that TA mice displayed fighting-biting aggression while TNA mice demonstrated immobile defensive strategy. Hypothetical discriminant formula was found for each mouse behavioral genotype that might be used to identify behavioral profile and line affiliation of unknown subjects.     

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time-series approach, a space-for-time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time-series approach made accurate short-term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space-for-time approach made more accurate long-term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time-scales where the other two approaches performed poorly. A weighted average of time-series and space-for-time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.     

Effects of biofeedback-assisted attention training in a college population

A program of stress management employing open-focus attention-training workshops was developed at Baruch College to bring the benefits of stress reduction to students. The purpose of the research reported here was to evaluate the results of the open-focus attention-training technique. Open-focus technique without biofeedback training was used for two semesters. Biofeedback training was incorporated in the third semester. In the first study, changes in grade point average (GPA), stress-related symptoms, and physiological measures were examined. The experimental subjects' stress data for this study was reported previously (Valdés, 1985). In the second study, changes in the same variables for experimental and control subjects were evaluated. Students in the control group showed decreased GPA, while those who participated in open-focus training showed a trend toward improved GPA. Stress-related symptoms associated with anxiety and management of emotional problems showed significant posttraining improvement, as did physiological measures in all of the biofeedback modalities in which the experimental subjects were specifically trained. The results support the hypothesis that the workshops were successful in reducing stress levels, and suggest that further controlled research be conducted to verify these findings, and to identify the most effective components of the training procedure.     

Fluctuating Asymmetry as a Bioindicator of Stress: Comparing Efficacy of Analyses Involving Multiple Traits

Researchers have suggested fluctuating asymmetry (FA) as an indicator of environmental stress and have usually tested this assertion by examining relations between FA of single traits and stress. Fluctuating asymmetry stress relations are real but are typically weak and difficult to detect. Researchers would like to maximize the probability of detecting FA-stress relations when they exist. We assert that analyses based on the FA of multiple traits may provide better methods for detecting stress. In this article, we used computer simulations to compare the ability of six analyses to detect differences in FA between stressed and unstressed populations. We show that the optimal analysis depends upon the underlying form of the FA distributions. We also show that two of the analyses had inflated Type I errors in some situations. Finally, we quantify the advantage of our preferred analysis over those of single-trait FA in detecting stress.     

The NIRS Analysis Package: noise reduction and statistical inference

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.     

Biodesensitization: Biofeedback-controlled systematic desensitization of the stress response to infant crying

Infant crying can be a source of parental stress both psychologically and physiologically and also may be an antecedent to physical child abuse or neglect. Biodesensitization is a new therapeutic technique that allows people to control the source of stress and develop self-control over their physiological responses to the stress-eliciting stimuli. Randomly assigned between three groups, 15 female participants were either given EEG biofeedback pretraining without stress, pretraining while listening to infant crying, or no stress management pretraining while listening to crying. After the pretraining manipulation all participants had biodesensitization training while listening to infant crying. Compared to control participants who were habituated to crying, stress management training significantly reduced the EEG cortical arousal as well as perceived arousal, and anxiety associated with listening to infant crying. The shift in participants' EEG power spectrum produced by infant crying was significantly correlated with perceived arousal and this relationship was strengthened after biofeedback training. In conjunction with other research, the experimental results suggest that stress management training may help ameliorate an aversive response to infant crying and possibly prevent child abuse as a response to physiological hyperreactivity.The author is grateful to Stan Sadava, Shoshana Tyson, and Joanna Manning for their helpful comments on this paper.     

Physiological response of tomatoes at drought,heat and their combination followed by recovery

In nature, crops encounter a combination of abiotic stresses that severely limit yield. Our aim was to dynamically expose the changes of tomatoes' physiological parameters to drought, heat and their combination and thereby clarify the relationship between the responses to single and combined stress. We studied the effect of single and combined drought and heat stresses on the shoot and root of two tomato cultivars (Sufen No.14 as CV1; Jinlingmeiyu as CV2). After being exposed to combined stress for 6 days, the dry weight of shoot and root significantly decreased. The F_q′/F_m′ (quantum yield of photosystem II) was significantly lower in CV1 upon drought and combined stress and in CV2 subjected to combined stress (between days 4 and 6) compared to control. The relative water content during combined stress was significantly lower than control from day 4 to recovery day 2. On days 3 and 6, the water loss rate significantly increased under heat stress and decreased at drought and combined stress, respectively. The combined stress caused severe damages on photosystem II and chloroplast ultrastructure. The root activity after stress recovered even though drought significantly increased the activity from day 2 to day 6. Combined stress result in complex responses during tomato growth. The CV1 was more heat tolerant than CV2, but there was no varietal difference at drought and combined stress. This study contributes to the understanding of the underlying physiological response mechanism of plant to combined stress and crop improvement by providing valuable information for abiotic stress‐tolerant tomato breeding.     

Analyzing Clustered Data: Why and How to Account for Multiple Observations Nested within a Study Participant?

A conventional study design among medical and biological experimentalists involves collecting multiple measurements from a study subject. For example, experiments utilizing mouse models in neuroscience often involve collecting multiple neuron measurements per mouse to increase the number of observations without requiring a large number of mice. This leads to a form of statistical dependence referred to as clustering. Inappropriate analyses of clustered data have resulted in several recent critiques of neuroscience research that suggest the bar for statistical analyses within the field is set too low. We compare naïve analytical approaches to marginal, fixed-effect, and mixed-effect models and provide guidelines for when each of these models is most appropriate based on study design. We demonstrate the influence of clustering on a between-mouse treatment effect, a within-mouse treatment effect, and an interaction effect between the two. Our analyses demonstrate that these statistical approaches can give substantially different results, primarily when the analyses include a between-mouse treatment effect. In a novel analysis from a neuroscience perspective, we also refine the mixed-effect approach through the inclusion of an aggregate mouse-level counterpart to a within-mouse (neuron level) treatment as an additional predictor by adapting an advanced modeling technique that has been used in social science research and show that this yields more informative results. Based on these findings, we emphasize the importance of appropriate analyses of clustered data, and we aim for this work to serve as a resource for when one is deciding which approach will work best for a given study.     

Forest savanna ecotone dynamics in India as revealed by carbon isotope ratios of soil organic matter

Linear programming models of diet selection (LP) have been criticized as being too sensitive to variations in parameter values that have not been or may not be able to be measured with a high degree of precision (small standard error). Therefore, LP's predictions have been questioned, even though the predicted diet choices agree very well with observations in 400 published tests. The philosophical and statistical aspects of this criticism of LP are reviewed in light of the ability to test any nontrivial ecological theory. It is argued that measures of error in field data may not meet simple statistical definitions, and thereby, may make sensitivity analyses that use the error measures overly conservative. Furthermore, the important issue in testing ecological theory may not be the statistical confidence in a single test, but whether or not the theory withstands repeated tests.     

Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life

Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum’s one gene—one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems’ responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.     

How integrated are behavioral and endocrine stress response traits? A repeated measures approach to testing the stress-coping style model

It is widely expected that physiological and behavioral stress responses will be integrated within divergent stress‐coping styles (SCS) and that these may represent opposite ends of a continuously varying reactive–proactive axis. If such a model is valid, then stress response traits should be repeatable and physiological and behavioral responses should also change in an integrated manner along a major axis of among‐individual variation. While there is some evidence of association between endocrine and behavioral stress response traits, few studies incorporate repeated observations of both. To test this model, we use a multivariate, repeated measures approach in a captive‐bred population of Xiphophorus birchmanni. We quantify among‐individual variation in behavioral stress response to an open field trial (OFT) with simulated predator attack (SPA) and measure waterborne steroid hormone levels (cortisol, 11‐ketotestosterone) before and after exposure. Under the mild stress stimulus (OFT), (multivariate) behavioral variation among individuals was consistent with a strong axis of personality (shy–bold) or coping style (reactive–proactive) variation. However, behavioral responses to a moderate stressor (SPA) were less repeatable, and robust statistical support for repeatable endocrine state over the full sampling period was limited to 11‐ketotestosterone. Although post hoc analysis suggested cortisol expression was repeatable over short time periods, qualitative relationships between behavior and glucocorticoid levels were counter to our a priori expectations. Thus, while our results clearly show among‐individual differences in behavioral and endocrine traits associated with stress response, the correlation structure between these is not consistent with a simple proactive–reactive axis of integrated stress‐coping style. Additionally, the low repeatability of cortisol suggests caution is warranted if single observations (or indeed repeat measures over short sampling periods) of glucocorticoid traits are used in ecological or evolutionary studies focussed at the individual level.     

The mode-of-action by network identification (MNI) algorithm: a network biology approach for molecular target identification

This protocol details the use of the mode-of-action by network identification (MNI) algorithm to identify the gene targets of a drug treatment based on gene-expression data. Investigators might also use the MNI algorithm to identify the gene mediators of a disease or the physiological state of cells and tissues. The MNI algorithm uses a training data set of hundreds of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue. The model describes combinatorial influences of genes on one another. The algorithm then uses the model to filter the expression profile of a particular experimental treatment and thereby distinguish the molecular targets or mediators of the treatment response from hundreds of additional genes that also exhibit expression changes. It takes approximately 1 h per run, although run time is significantly affected by the size of the genome and data set.