Body Mass Index Is Independently Associated with the Presence of Ischemia in Myocardial Perfusion Imaging.

Background and Objectives: Obesity has been linked to various cardiovascular risk factors, increased incidence of coronary artery disease, and myocardial perfusion defects. The aim of this study was to investigate if body mass index (BMI) and waist circumference (WC) were associated with myocardial perfusion defects. Materials and Methods: A total of 308 consecutive patients who had myocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) and a complete medical record on file were studied retrospectively. Results: The median age was 69 (61−76) years, the BMI was 27.6 (24.4−30.7) kg/m2, and the WC was 110 (102−118) cm. Of the 308 patients, 239 patients (77.6%) had myocardial ischemia. A positive test for ischemia was more frequent in men compared to women (72 vs. 28%, p < 0.001). Within the male group, BMI and WC were not significantly different between the ischemia and non-ischemia groups. In contrast, within the female group, both BMI (30.2 vs. 27.1 kg/m2, p = 0.002) and WC (112 vs. 105.5 cm, p = 0.020) were significantly higher in the ischemia group. Multivariable logistic regression showed that male sex and BMI were the only two independent predictors of ischemia in our patient population. Conclusions: This study showed that BMI was an independent predictor of ischemia in our patient population.

Hybrid bone SPECT/CT reveals spleen calcification in sickle cell mutation and beta-thalassemia.

We present a case of a 65 years-old male with sickle cell mutation and beta-thalassemia (Hb S/ß-Thal), who had whole-body bone scan evaluation for osteomyelitis. The examination revealed high radiopharmaceutical uptake in the left abdomen. Further evaluation with hybrid single photon emission computed tomography/computed tomography (SPECT/CT) showed calcification of approximately the entire spleen, in the context of sickle cell anemia. This report highlights the role of SPECT/CT in such cases.

Estimating topological entropy using ordinal partition networks.

We propose a computationally simple and efficient network-based method for approximating topological entropy of low-dimensional chaotic systems. This approach relies on the notion of an ordinal partition. The proposed methodology is compared to the three existing techniques based on counting ordinal patterns-all of which derive from collecting statistics about the symbolic itinerary-namely (i) the gradient of the logarithm of the number of observed patterns as a function of the pattern length, (ii) direct application of the definition of topological permutation entropy, and (iii) the outgrowth ratio of patterns of increasing length. In contrast to these alternatives, our method involves the construction of a sequence of complex networks that constitute stochastic approximations of the underlying dynamics on an increasingly finer partition. An ordinal partition network can be computed using any scalar observable generated by multidimensional ergodic systems, provided the measurement function comprises a monotonic transformation if nonlinear. Numerical experiments on an ensemble of systems demonstrate that the logarithm of the spectral radius of the connectivity matrix produces significantly more accurate approximations than existing alternatives-despite practical constraints dictating the selection of low finite values for the pattern length.

Markov modeling via ordinal partitions: An alternative paradigm for network-based time-series analysis.

Mapping time series to complex networks to analyze observables has recently become popular, both at the theoretical and the practitioner's level. The intent is to use network metrics to characterize the dynamics of the underlying system. Applications cover a wide range of problems, from geoscientific measurements to biomedical data and financial time series. It has been observed that different dynamics can produce networks with distinct topological characteristics under a variety of time-series-to-network transforms that have been proposed in the literature. The direct connection, however, remains unclear. Here, we investigate a network transform based on computing statistics of ordinal permutations in short subsequences of the time series, the so-called ordinal partition network. We propose a Markovian framework that allows the interpretation of the network using ergodic-theoretic ideas and demonstrate, via numerical experiments on an ensemble of time series, that this viewpoint renders this technique especially well-suited to nonlinear chaotic signals. The aim is to test the mapping's faithfulness as a representation of the dynamics and the extent to which it retains information from the input data. First, we show that generating networks by counting patterns of increasing length is essentially a mechanism for approximating the analog of the Perron-Frobenius operator in a topologically equivalent higher-dimensional space to the original state space. Then, we illustrate a connection between the connectivity patterns of the networks generated by this mapping and indicators of dynamics such as the hierarchy of unstable periodic orbits embedded within a chaotic attractor. The input is a scalar observable and any projection of a multidimensional flow suffices for reconstruction of the essential dynamics. Additionally, we create a detailed guide for parameter tuning. We argue that there is no optimal value of the pattern length m, rather it admits a scaling region akin to traditional embedding practice. In contrast, the embedding lag and overlap between successive patterns can be chosen exactly in an optimal way. Our analysis illustrates the potential of this transform as a complementary toolkit to traditional time-series methods.

Physiotherapist perceptions of optimal sitting and standing posture.

BACKGROUND: Assessment of, and advice about, spinal posture is common when people with spinal pain present to physiotherapists. Most descriptions regarding optimal spinal posture have been qualitative in nature. OBJECTIVES: To determine the beliefs of physiotherapists regarding optimal sitting and standing posture. DESIGN: Online survey. METHOD: 544 Greek physiotherapists selected an optimal sitting (choice of seven) and standing (choice of five) posture, while providing justification for their choice. RESULTS: Education regarding optimal sitting and standing posture was considered "considerably" or "very" important by 93.9% of participants. Three different sitting postures, and two different standing postures, were selected as the optimal posture by 97.5% and 98.2% of physiotherapists respectively. While this reflects a lack of complete consensus on optimal posture, the most commonly selected postures were all some variation of upright lordotic sitting, in contrast slouched spinal curves (sitting) or forward head posture (sitting and standing) almost never being selected as optimal. Interestingly, participants used similar arguments (e.g. natural curves, muscle activation) to justify their selection regardless of the spinal configuration of each selected posture. CONCLUSIONS: These results reinforce previous data suggesting that upright lordotic sitting postures are considered optimal, despite a lack of strong evidence that any specific posture is linked to better health outcomes. While postural re-education may play a role in the management of spinal pain for some patients, awareness of such widespread and stereotypical beliefs regarding optimal posture may be useful in clinical assessment and management.

Regenerating time series from ordinal networks.

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitter.

It has been established that the count of ordinal patterns, which do not occur in a time series, called forbidden patterns, is an effective measure for the detection of determinism in noisy data. A very recent study has shown that this measure is also partially robust against the effects of irregular sampling. In this paper, we extend said research with an emphasis on exploring the parameter space for the method's sole parameter-the length of the ordinal patterns-and find that the measure is more robust to under-sampling and irregular sampling than previously reported. Using numerically generated data from the Lorenz system and the hyper-chaotic Rössler system, we investigate the reliability of the relative proportion of ordinal patterns in periodic and chaotic time series for various degrees of under-sampling, random depletion of data, and timing jitter. Discussion and interpretation of results focus on determining the limitations of the measure with respect to optimal parameter selection, the quantity of data available, the sampling period, and the Lyapunov and de-correlation times of the system.

Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular sampling.

We are motivated by real-world data that exhibit severe sampling irregularities such as geological or paleoclimate measurements. Counting forbidden patterns has been shown to be a powerful tool towards the detection of determinism in noisy time series. They constitute a set of ordinal symbolic patterns that cannot be realised in time series generated by deterministic systems. The reliability of the estimator of the relative count of forbidden patterns from irregularly sampled data has been explored in two recent studies. In this paper, we explore highly irregular sampling frequency schemes. Using numerically generated data, we examine the reliability of the estimator when the sampling period has been drawn from exponential, Pareto and Gamma distributions of varying skewness. Our investigations demonstrate that some statistical properties of the sampling distribution are useful heuristics for assessing the estimator's reliability. We find that sampling in the presence of large chronological gaps can still yield relatively accurate estimates as long as the time series contains sufficiently many densely sampled areas. Furthermore, we show that the reliability of the estimator of forbidden patterns is poor when there is a high number of sampling intervals, which are larger than a typical correlation time of the underlying system.