Heart rate recovery after exercise: relations to heart rate variability and complexity

Physical exercise is associated with parasympathetic withdrawal and increased sympathetic activity resulting in heart rate increase. The rate of post-exercise cardiodeceleration is used as an index of cardiac vagal reactivation. Analysis of heart rate variability (HRV) and complexity can provide useful information about autonomic control of the cardiovascular system. The aim of the present study was to ascertain the association between heart rate decrease after exercise and HRV parameters. Heart rate was monitored in 17 healthy male subjects (mean age: 20 years) during the pre-exercise phase (25 min supine, 5 min standing), during exercise (8 min of the step test with an ascending frequency corresponding to 70% of individual maximal power output) and during the recovery phase (30 min supine). HRV analysis in the time and frequency domains and evaluation of a newly developed complexity measure - sample entropy - were performed on selected segments of heart rate time series. During recovery, heart rate decreased gradually but did not attain pre-exercise values within 30 min after exercise. On the other hand, HRV gradually increased, but did not regain rest values during the study period. Heart rate complexity was slightly reduced after exercise and attained rest values after 30-min recovery. The rate of cardiodeceleration did not correlate with pre-exercise HRV parameters, but positively correlated with HRV measures and sample entropy obtained from the early phases of recovery. In conclusion, the cardiodeceleration rate is independent of HRV measures during the rest period but it is related to early post-exercise recovery HRV measures, confirming a parasympathetic contribution to this phase.

A high-copy T7 Escherichia coli expression vector for the production of recombinant proteins with a minimal N-terminal His-tagged fusion peptide

We report here the construction of a vector derived from pET3-His and pRSET plasmids for the expression and purification of recombinant proteins in Escherichia coli based on T7 phage RNA polymerase. The resulting pAE plasmid combined the advantages of both vectors: small size (pRSET), expression of a short 6XHis tag at N-terminus (pET3-His) and a high copy number of plasmid (pRSET). The small size of the vector (2.8 kb) and the high copy number/cell (200-250 copies) facilitate the subcloning and sequencing procedures when compared to the pET system (pET3-His, 4.6 kb and 40-50 copies) and also result in high level expression of recombinant proteins (20 mg purified protein/liter of culture). In addition, the vector pAE enables the expression of a fusion protein with a minimal amino-terminal hexa-histidine affinity tag (a tag of 9 amino acids using XhoI restriction enzyme for the 5'cloning site) as in the case of pET3-His plasmid and in contrast to proteins expressed by pRSET plasmids (a tag of 36 amino acids using BamHI restriction enzyme for the 5'cloning site). Thus, although proteins expressed by pRSET plasmids also have a hexa-histidine tag, the fusion peptide is much longer and may represent a problem for some recombinant proteins.

Learning about brain physiology and complexity from the study of the epilepsies

The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a variety of networks, from widespread anatomical brain circuits to local molecular environments. One logical question would be: are those complex brain networks always producing maladaptive emergent properties compatible with epileptogenic substrates? The present review will deal with this question and will try to answer it by illustrating several points from the literature and from our laboratory data, with examples at the behavioral, electrophysiological, cellular and molecular levels. We conclude that, because the brain is a complex system compatible with the production of emergent properties, including plasticity, its functions should be approached using an integrated view. Concepts such as brain networks, graphics theory, neuroinformatics, and e-neuroscience are discussed as new transdisciplinary approaches dealing with the continuous growth of information about brain physiology and its dysfunctions. The epilepsies are discussed as neurobiological models of complex systems displaying maladaptive plasticity.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

Modelling Energy Consumption of a Switch using Fuzzy-Rule Classifier

The energy consumption of IT equipments is becoming an issue of increasing importance. In particular, network equipments such as routers and switches are major contributors to the energy consumption of internet. Therefore it is important to understand how the relationship between input parameters such as bandwidth, number of active ports, traffic-load, hibernation-mode and their impact on energy consumption of a switch. In this paper, the energy consumption of a switch is analyzed in extensive experiments. A fuzzy rule-based model of energy consumption of a switch is proposed based on the result of experiments. The model can be used to predict the energy saving when deploying new switches by controlling the parameters to achieve desired energy consumption and subsequent performance. Furthermore, the model can also be used for further researches on energy saving techniques such as energy-efficient routing protocol, dynamic link shutdown, etc.

Toward a Reliable Classifier For Arrhythmia Diagnosis

Illnesses related to the heart are one of the major reasons for death all over the world causing many people to lose their lives in last decades. The good news is that many of those sicknesses are preventable if they are spotted in early stages. On the other hand, the number of the doctors are much lower than the number of patients. This will makes the auto diagnosing of diseases even more and more essential for humans today. Furthermore, when it comes to the diagnosing methods and algorithms, the current state of the art is lacking a comprehensive study on the comparison between different diagnosis solutions. Not having a single valid diagnosing solution has increased the confusion among scholars and made it harder for them to take further steps. This master thesis will address the issue of reliable diagnosing algorithm. We investigate ECG signals and the relation between different diseases and the heart’s electrical activity. Also, we will discuss the necessary steps needed for auto diagnosing the heart diseases including the literatures discussing the topic. The main goal of this master thesis is to find a single reliable diagnosing algorithm and quest for the best classifier to date for heart related sicknesses. Five most suited and most well-known classifiers, such as KNN, CART, MLP, Adaboost and SVM, have been investigated. To have a fair comparison, the ex-periment condition is kept the same for all classification methods. The UCI repository arrhythmia dataset will be used and the data will not be preprocessed. The experiment results indicates that AdaBoost noticeably classifies different diseases with a considera-bly better accuracy.

Exact property estimation from diffusion Monte Carlo with minimal stochastic reconfiguration /

Our objective is to develop a diffusion Monte Carlo (DMC) algorithm to estimate the exact expectation values, ($o|^|^o), of multiplicative operators, such as polarizabilities and high-order hyperpolarizabilities, for isolated atoms and molecules. The existing forward-walking pure diffusion Monte Carlo (FW-PDMC) algorithm which attempts this has a serious bias. On the other hand, the DMC algorithm with minimal stochastic reconfiguration provides unbiased estimates of the energies, but the expectation values ($o|^|^) are contaminated by ^, an user specified, approximate wave function, when A does not commute with the Hamiltonian. We modified the latter algorithm to obtain the exact expectation values for these operators, while at the same time eliminating the bias. To compare the efficiency of FW-PDMC and the modified DMC algorithms we calculated simple properties of the H atom, such as various functions of coordinates and polarizabilities. Using three non-exact wave functions, one of moderate quality and the others very crude, in each case the results are within statistical error of the exact values.

The effects of language complexity in a counterattitudinal appeal to laypeople : an application of the elaboration likelihood model of persuasion to a legal issue

The effects of a complexly worded counterattitudinal appeal on laypeople's attitudes toward a legal issue were examined, using the Elaboration Likelihood Model (ELM) of persuasion as a theoretical framework. This model states that persuasion can result from the elaboration and scrutiny of the message arguments (i.e., central route processing), or can result from less cognitively effortful strategies, such as relying on source characteristics as a cue to message validity (i.e., peripheral route processing). One hundred and sixty-seven undergraduates (85 men and 81 women) listened to eitller a low status or high status source deliver a counterattitudinal speech on a legal issue. The speech was designed to contain strong or weak arguments. These arguments were 'worded in a simple and, therefore, easy to comprehend manner, or in a complex and, therefore, difficult to comprehend manner. Thus, there were three experimental manipulations: argument comprehensibility (easy to comprehend vs. difficult to comprehend), argumel11 strength (weak vs. strong), and source status (low vs. high). After listening to tIle speec.J] participants completed a measure 'of their attitude toward the legal issue, a thought listil1g task, an argument recall task,manipulation checks, measures of motivation to process the message, and measures of mood. As a result of the failure of the argument strength manipulation, only the effects of the comprehel1sibility and source status manipulations were tested. There was, however, some evidence of more central route processing in the easy comprehension condition than in the difficult comprehension condition, as predicted. Significant correlations were found between attitude and favourable and unfavourable thoughts about the legal issue with easy to comprehend arguments; whereas, there was a correlation only between attitude and favourable thoughts 11 toward the issue with difficult to comprehend arguments, suggesting, perhaps, that central route processing, \vhich involves argument scrutiny and elaboration, occurred under conditions of easy comprehension to a greater extent than under conditions of difficult comprehension. The results also revealed, among other findings, several significant effects of gender. Men had more favourable attitudes toward the legal issue than did women, men recalled more arguments from the speech than did women, men were less frustrated while listening to the speech than were ,vomen, and men put more effort into thinking about the message arguments than did women. When the arguments were difficult to comprehend, men had more favourable thoughts and fewer unfavourable thoughts about the legal issue than did women. Men and women may have had different affective responses to the issue of plea bargaining (with women responding more negatively than men), especially in light of a local and controversial plea bargain that occurred around the time of this study. Such pre-existing gender differences may have led to tIle lower frustration, the greater effort, the greater recall, and more positive attitudes for men than for WOlnen. Results· from this study suggest that current cognitive models of persuasion may not be very applicable to controversial issues which elicit strong emotional responses. Finally, these data indicate that affective responses, the controversial and emotional nature ofthe issue, gender and other individual differences are important considerations when experts are attempting to persuade laypeople toward a counterattitudinal position.

Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.

Minimal Contact Intervention for Tobacco Dependence

This study was conducted to measure the degree of adherence by public health care providers to a policy that requires them to implement minimal contact intervention for tobacco cessation with their clients. This study also described what components of the intervention may have contributed to the adherence of the policy and how health care providers felt about adhering to the policy. The intervention consisted of a policy for implementation of minimal contact intervention, changes to documentation, a health care provider mentor trained, a training session for health care providers, and ongoing paper and people supports for implementation. Data for this study were collected through a health care provider questionnaire, focus group interviews, and a compliance protocol including a chart audit. The findings of this study showed a high degree of adherence to the policy, that health care providers thought minimal contact intervention was important to conduct with their clients, and that health care providers felt supported to implement the intervention. No statistically significant difference was found between new and experienced health care providers on 17 of the 18 questions on the health care provider questionnaire. However there was a statistically significant difference between new and experienced health care providers with respect to their perception that “clients often feel like they have to accept tobacco cessation information from me.” Changes could be made to the minimal contact intervention and to documentation of the intervention. Implications for future research include implementation within other programs within Hamilton Public Health Services and implementation of this model within other public health units and other types of health care providers within Ontario.

Production Linkages Between Informal and Formal Activities Considering Domestic and Imported Inputs: an Application of the Minimal-Flow-Analysis Method to Senegal.

We apply to the Senegalese input-output matrix of 1990, disagregated into formal and informal activities, a recently designed structural analytical method (Minimal-Flow-Analysis) which permits to depict the direct and indirect production likanges existing between activities.

On Complexity of Lobbying in Multiple Referenda

In this paper we show that lobbying in conditions of “direct democracy” is virtually impossible, even in conditions of complete information about voters preferences, since it would require solving a very computationally hard problem. We use the apparatus of parametrized complexity for this purpose.

Analyse quantitative des cyanotoxines d'eau douce par LDTD-APCI-MS/MS

Avec la hausse mondiale de la fréquence des floraisons de cyanobactéries (CB), dont certaines produisent des cyanotoxines (CT), le développement d’une méthode de détection/quantification rapide d’un maximum de CT s’impose. Cette méthode permettrait de faire un suivi quotidien de la toxicité de plans d’eau contaminés par des CB et ainsi d’émettre rapidement des avis d’alerte appropriés afin de protéger la santé publique. Une nouvelle technologie utilisant la désorption thermique induite par diode laser (LDTD) couplée à l’ionisation chimique sous pression atmosphérique (APCI) et reliée à la spectrométrie de masse en tandem (MS/MS) a déjà fait ses preuves avec des temps d'analyse de l’ordre de quelques secondes. Les analytes sont désorbés par la LDTD, ionisés en phase gazeuse par APCI et détectés par la MS/MS. Il n’y a donc pas de séparation chromatographique, et la préparation de l’échantillon avant l’analyse est minimale selon la complexité de la matrice contenant les analytes. Parmi les quatre CT testées (microcystine-LR, cylindrospermopsine, saxitoxine et anatoxine-a (ANA-a)), seule l’ANA-a a généré une désorption significative nécessaire au développement d’une méthode analytique avec l’interface LDTD-APCI. La forte polarité ou le poids moléculaire élevé des autres CT empêche probablement leur désorption. L’optimisation des paramètres instrumentaux, tout en tenant compte de l’interférence isobarique de l’acide aminé phénylalanine (PHE) lors de la détection de l’ANA-a par MS/MS, a généré une limite de détection d’ANA-a de l’ordre de 1 ug/L. Celle-ci a été évaluée à partir d’une matrice apparentée à une matrice réelle, démontrant qu’il serait possible d’utiliser la LDTD pour effectuer le suivi de l’ANA-a dans les eaux naturelles selon les normes environnementales applicables (1 à 12 ug/L). Il a été possible d’éviter l’interférence isobarique de la PHE en raison de sa très faible désorption avec l’interface LDTD-APCI. En effet, il a été démontré qu’une concentration aussi élevée que 500 ug/L de PHE ne causait aucune interférence sur le signal de l’ANA-a.