Single track coincidence measurements of fluorescent and plastic nuclear track detectors in therapeutic carbon beams

In this paper we present a method for single track coincidence measurements using two different track detector materials. We employed plastic and fluorescent nuclear track detectors (PNTDs and FNTDs) in the entrance channel of a monoenergetic carbon ion beam covering the therapeutically useful energy range from 80 to 425 MeV/u. About 99 % of all primary particle tracks detected by both detectors were successfully matched, while 1 % of the particles were only detected by the FNTDs because of their superior spatial resolution. We conclude that both PNTDs and FNTDs are suitable for clinical carbon beam dosimetry with a detection efficiency of at least 98.82 % and 99.83 % respectively, if irradiations are performed with low fluence in the entrance channel of the ion beam. The investigated method can be adapted to other nuclear track detectors and offers the possibility to characterize new track detector materials against well-known detectors. Further, by combining two detectors with a restricted working range in the presented way a hybrid-detector system can be created with an extended and optimized working range.Comment: 14 pages, 8 figures, 2 table

Cryogenic Ion Trapping Systems with Surface-Electrode Traps

We present two simple cryogenic RF ion trap systems in which cryogenic temperatures and ultra high vacuum pressures can be reached in as little as 12 hours. The ion traps are operated either in a liquid helium bath cryostat or in a low vibration closed cycle cryostat. The fast turn around time and availability of buffer gas cooling made the systems ideal for testing surface-electrode ion traps. The vibration amplitude of the closed cycled cryostat was found to be below 106 nm. We evaluated the systems by loading surface-electrode ion traps with $^{88}$Sr$^+$ ions using laser ablation, which is compatible with the cryogenic environment. Using Doppler cooling we observed small ion crystals in which optically resolved ions have a trapped lifetime over 2500 minutes.Comment: 10 pages, 13 EPS figure

Cardiorespiratory synchronization: is it a real phenomenon

Abstract In this work we present a quantitative approach to the analysis of cardiorespiratory synchronization, which is a newly discovered phenomenon. The primary aim of this Introduction Modulation of heart rate (HR) by respiration, which is the main source of heart rate variability, is long known. This phenomenon has been studied extensively, and although it is not fully understood, its physiological determinants have been unveiled. Lately, the study of phase synchronization in chaotic oscillators has led to the discovery of another aspect of cardiorespiratory interaction: synchronization between respiration and HR [I]. Cardiorespiratory synchronization (CS) was observed in young athletes in coexistence with modulation of HR by respiration. The synchronization was found using a novel visualization tool, the Synchrogram [ 11. The Synchrogram enables to visually detect epochs of synchrony between two noisy signals, with any rational frequency ratio. The qualitative analysis of cardiorespiratory interaction presented in [1,2] raises two questions: a) is cardiorespiratory synchronization a real phenomenon, The two questions are related. Associating distinct physiological conditions to CS negates the hypothesis of CS being random. Indeed, preliminary results indicate that CS is associated with lower HR variability, and more specifically, with reduced values of parasympathetic activity [l-21. In this work, we apply the approach of surrogate data analysis to the study of CS, in order to answer the first question. Surrogate data 'analysis is a widely used approach in the field of nonlinear dynamics, especially when trying to assess a functional relation between an attribute of a system to one of its features. The essence of surrogate analysis is the construction of a (surrogate) data set from the original data, while preserving all features of the data, except for the one whose influence is being tested. A difference in the measured attribute between the real and surrogate data then indicates that it is related to that specific feature that is absent in the surrogates. Our analysis relates the heart-respiration coupling to the synchronization between them. The surrogates were constructed by considering the interaction between respiration and heart rate taken from different subjects. Avoiding randomization of the signals themselves, as commonly done in surrogate data analysis, preserves all features of the cardiorespiratory system, except for the coupling between the two subsystems. We applied a previously developed algorithm, which enables to quantify CS [3], to the analysis of the real and surrogate data. We then compared the statistical properties of the observed CS in both real and surrogate data. Our results show that synchronization appears in both real and surrogate data, although significantly less in the surrogates. Cardiorespiratory synchronization should therefore enter the cadre of cardiorespiratory interactions. Unveiling its physiological determinants and relating cardiorespiratory pathologies to CS will undoubtedly increase our knowledge of this complex system

Multifractality in Human Heartbeat Dynamics

Recent evidence suggests that physiological signals under healthy conditions may have a fractal temporal structure. We investigate the possibility that time series generated by certain physiological control systems may be members of a special class of complex processes, termed multifractal, which require a large number of exponents to characterize their scaling properties. We report on evidence for multifractality in a biological dynamical system --- the healthy human heartbeat. Further, we show that the multifractal character and nonlinear properties of the healthy heart rate are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure.Comment: 19 pages, latex2e using rotate and epsf, with 5 ps figures; to appear in Nature, 3 June, 199

Validity of the Polar V800 heart rate monitor to measure RR intervals at rest

Purpose To assess the validity of RR intervals and short-term heart rate variability (HRV) data obtained from the Polar V800 heart rate monitor, in comparison to an electrocardiograph (ECG). Method Twenty participants completed an active orthostatic test using the V800 and ECG. An improved method for the identification and correction of RR intervals was employed prior to HRV analysis. Agreement of the data was assessed using intra-class correlation coefficients (ICC), Bland–Altman limits of agreement (LoA), and effect size (ES). Results A small number of errors were detected between ECG and Polar RR signal, with a combined error rate of 0.086 %. The RR intervals from ECG to V800 were significantly different, but with small ES for both supine corrected and standing corrected data (ES 0.999 for both supine and standing corrected intervals. When analysed with the same HRV software no significant differences were observed in any HRV parameters, for either supine or standing; the data displayed small bias and tight LoA, strong ICC (>0.99) and small ES (≤0.029). Conclusions The V800 improves over previous Polar models, with narrower LoA, stronger ICC and smaller ES for both the RR intervals and HRV parameters. The findings support the validity of the Polar V800 and its ability to produce RR interval recordings consistent with an ECG. In addition, HRV parameters derived from these recordings are also highly comparable

Transmission of Methicillin‐Resistant Staphylococcus aureus Infection Through Solid Organ Transplantation: Confirmation Via Whole Genome Sequencing

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109324/1/ajt12898.pd

Characterization of Sleep Stages by Correlations of Heartbeat Increments

We study correlation properties of the magnitude and the sign of the increments in the time intervals between successive heartbeats during light sleep, deep sleep, and REM sleep using the detrended fluctuation analysis method. We find short-range anticorrelations in the sign time series, which are strong during deep sleep, weaker during light sleep and even weaker during REM sleep. In contrast, we find long-range positive correlations in the magnitude time series, which are strong during REM sleep and weaker during light sleep. We observe uncorrelated behavior for the magnitude during deep sleep. Since the magnitude series relates to the nonlinear properties of the original time series, while the signs series relates to the linear properties, our findings suggest that the nonlinear properties of the heartbeat dynamics are more pronounced during REM sleep. Thus, the sign and the magnitude series provide information which is useful in distinguishing between the sleep stages.Comment: 7 pages, 4 figures, revte

Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and τ(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at τ(j) being a common instant on two scales between t and τ(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings