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The creativity and emergence of biological and psychological behavior tend to be nonlinear, and correspondingly, biological and psychological measures contain degrees of irregularity. The linear model might fail to reduce these measurements to a sum of independent random factors (yielding a stable mean for the measurement), implying nonlinear changes over time. The present work reviews some of the concepts implicated in nonlinear changes over time and details the mathematical steps involved in their identification. It introduces multifractality as a mathematical framework helpful in determining whether and to what degree the measured series exhibits nonlinear changes over time. These mathematical steps include multifractal analysis and surrogate data production for resolving when multifractality entails nonlinear changes over time. Ultimately, when measurements fail to fit the structures of the traditional linear model, multifractal modeling allows for making those nonlinear excursions explicit, that is, to come up with a quantitative estimate of how strongly events may interact across timescales. This estimate may serve some interests as merely a potentially statistically significant indicator of independence failing to hold, but we suspect that this estimate might serve more generally as a predictor of perceptuomotor or cognitive performance.

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. •Replicability in dynamic functional connectivity state measures was investigated.•Twenty-eight samples each with two hundred and fifty rest-fMRI datasets were studied.•State profiles were modelled using two (clustering and fuzzy meta-state) approaches.•Both approaches showed high consistency for a range of model orders.•Surrogate testing confirmed state summary measures to be statistically significant.

Discrete chaotic maps are a mathematical basis for many useful applications. One of the most common is chaos-based pseudorandom number generators (PRNGs), which should be computationally cheap and controllable and possess necessary statistical properties, such as mixing and diffusion. However, chaotic PRNGs have several known shortcomings, e.g., being prone to chaos degeneration, falling in short periods, and having a relatively narrow parameter range. Therefore, it is reasonable to design novel simple chaotic maps to overcome these drawbacks. In this study, we propose a novel fractal chaotic tent map, which is a generalization of the well-known tent map with a fractal function introduced into the right-hand side. We construct and investigate a PRNG based on the proposed map, showing its high level of randomness by applying the NIST statistical test suite. The application of the proposed PRNG to the task of generating surrogate data and a surrogate testing procedure is shown. The experimental results demonstrate that our approach possesses superior accuracy in surrogate testing across three distinct signal types—linear, chaotic, and biological signals—compared to the MATLAB built-in randn() function and PRNGs based on the logistic map and the conventional tent map. Along with surrogate testing, the proposed fractal tent map can be efficiently used in chaos-based communications and data encryption tasks.

Among Acinetobacter species, A. baumannii and other closely related species are commonly implicated in nosocomial infections. These organisms are usually multidrug resistant (MDR), and therapeutic options to treat A. baumannii infections are very limited. Clinicians have been resorting to older antimicrobial agents to treat infections caused by MDR A. baumannii, and some of these agents have documented toxicity and/or are not optimized for the infection type to be treated. Recent clinical experience supported by antimicrobial susceptibility data suggests that minocycline has greater activity than other tetracyclines and glycylcyclines against various MDR pathogens that have limited therapeutic options available, including Acinetobacter species. An intravenous formulation of minocycline has recently become available for clinical use, and in contrast to most older tetracyclines, minocycline has high activity against Acinetobacter species. In this report, we summarized some of the characteristics of the tetracycline class, and quantified the minocycline activity against contemporary (2007–2011) isolates and its potential therapeutic role against a collection of 5477 A. baumannii and other relevant gram-negative organisms when compared directly with tetracycline, doxycycline, and other broad-spectrum antimicrobial agents. Acinetobacter baumannii strains were highly resistant to all agents tested, with the exception of minocycline (79.1% susceptible) and colistin (98.8% susceptible). Minocycline (minimum inhibitory concentration that inhibits 50% and 90% of the isolates [MIC50/90]: 1/8 μg/mL) displayed greater activity than doxycycline (MIC50/90: 2/>8 μg/mL) and tetracycline hydrochloride (HCL) (only 30.2% susceptible) against A. baumannii isolates, and was significantly more active than other tetracyclines against Burkholderia cepacia, Escherichia coli, Serratia marcescens, and Stenotrophomonas maltophilia isolates. In vitro susceptibility testing using tetracycline HCL as a surrogate for the susceptibility other tetracyclines fails to detect minocycline-susceptible isolates and the potential utility of minocycline for the treatment of many MDR A. baumannii infections and other difficult-to-treat species, where there are often limited choices of antimicrobials.

Harit Kumar,1 Nitin Kumar2 1Department of Microbiology, Maharishi Markandeshwar Institute of Medical Sciences & Research, Maharishi Markandeshwar (Deemed to be) University Mullana, Mullana, Haryana, India; 2Department of Microbiology, Xavier University School of Medicine, Oranjestad, Aruba, NetherlandsCorrespondence: Nitin Kumar, Department of Microbiology, Xavier University School of Medicine, Oranjestad, Aruba, Netherlands, Email nitingoel222@gmail.com

Background Tedizolid is a novel oxazolidinone antibacterial with potent activity against a wide range of Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci. Although tedizolid is approved by the US Food and Drug Administration (FDA) for treatment of patients with acute bacterial skin and skin structure infection, commercial susceptibility testing products for tedizolid are not currently available. This study evaluated the usefulness of applying linezolid susceptibility test results as a surrogate for predicting susceptibility to tedizolid in clinically significant Gram-positive pathogens. Methods Gram-positive isolates (N=10,702) were obtained from annual surveillance programs conducted between 2009 and 2012, from 3 tedizolid clinical trials, and from a preclinical study of the antibacterial activity of tedizolid. Susceptibility testing of linezolid and tedizolid was performed using the reference broth microdilution method in accordance with Clinical and Laboratory Standards Institute methods. Results The minimum inhibitory concentration (MIC) distribution for tedizolid and linezolid against this set of isolates was consistent with that of previous reports. Scatter plot analysis of relevant subsets of organisms was performed and showed high categorical agreement between linezolid and tedizolid MIC results (>99% for staphylococci and streptococci; >98% for enterococci). Very major error rates (ie, tedizolid false-susceptible errors) were very low and within acceptable limits for a surrogate agent: S. aureus and other staphylococcal species, 0%; Enterococcus spp, 0.2%; and Streptococcus spp, 0%. Conclusions High categorical agreement between MIC values for tedizolid and linezolid and low very major error rates were shown for all organism groups tested, supporting the use of linezolid as a reliable surrogate for tedizolid susceptibility testing.

In vitro permeation studies using nail clippings or nail plates are commonly used in the development of transungual formulations. However, there are ethical, safety and cost issues associated with sourcing such tissues. Herein, we describe a preliminary approach is described for the design and manufacture of a human nail model surrogate based on 3D printing. To evaluate these 3D printed constructs, nails were mounted in conventional glass Franz cells and a commercial antifungal lacquer formulation containing ciclopirox olamine was applied daily to the surrogate printed surfaces for a period of 14 days. On days 8 and 14, the surfaces of the 3D printed nails were washed with ethanol to remove excess formulation. Confocal Raman spectroscopy (CRS) was used to profile the drug in the 3D printed nail. At the end of the Franz cell studies, no drug was observed in the receptor phase. CRS studies confirmed penetration of the active into the model nails with reproducible depth profiles. Our ongoing work is focused on synthesising commercial and non-commercial printable resins that can replicate the physical and chemical characteristics of the human nail. This will allow further evaluation of actives for ungual therapy and advance the development of the surrogate nail tissue model.

We present a methodology for the non-linear analysis of long-term ecological time series. Using a time-delay embedding procedure, we investigated the complexity of 1-dimensional signals and compared their structure to their stochastic counterparts. Recurrence-based statistics and surrogate testing were used for this purpose. The method was first tested on elementary models for illustrative purposes and its potentialities and limits reviewed. We then investigated the time structure of 6 Mediterranean bluefin tuna trap catches known to display long-term fluctuations. These time series showed significantly more determinism than linear stochastic processes, displaying more recurring patterns/states than stochastic surrogates. The short term structure of these series also showed a direct and delayed negative feedback in their rate of increase. These results were homogeneous over all studied time series. An interaction between climate forcing and the life history traits of bluefin tuna was postulated to explain the first result, while the density-dependent pattern may be explained either by density-dependence processes during early stages, or by non-successive (non-yearly) spawning events in the Mediterranean.

The development of suitable techniques for quantifying mechanical and electrophysiological aspects of uterine contractions has been an active area of research. The uterus is a physiological system consisting of a large number of interacting muscle cells. The activity of these cells evolves with time, a trait characteristic of a dynamical system. While such complex physiological systems are non-linear by their very nature, whether this non-linearity is exhibited in the external recording is far from trivial. Traditional techniques such as spectral analysis have been used in the past, but these techniques implicitly assume that the process generating the contractions is linear and hence may be biased. In this tutorial review, a systematic approach using a hierarchy of surrogate algorithms is used to determine the nature of the process generating the contractions produced during labor. The results reveal that uterine contractions are probably generated by non-linear processes. The contraction segments were obtained through simultaneous recordings of the electrical and magnetic signals corresponding to the electrophysiological activity of the uterus and then analyzed. The electrical activity was recorded by placement of non-invasive electrodes onto the maternal abdomen and magnetic activity was recorded non-invasively using a superconducting quantum interference device (SQUID).

In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM2.5 (particulate matter of diameter less than 2.5 μ m ) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM2.5 daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM2.5 concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.