Motor deficits in schizophrenia quantified by nonlinear analysis of postural sway.

Motor dysfunction is a consistently reported but understudied aspect of schizophrenia. Postural sway area was examined in individuals with schizophrenia under four conditions with different amounts of visual and proprioceptive feedback: eyes open or closed and feet together or shoulder width apart. The nonlinear complexity of postural sway was assessed by detrended fluctuation analysis (DFA). The schizophrenia group (n = 27) exhibited greater sway area compared to controls (n = 37). Participants with schizophrenia showed increased sway area following the removal of visual input, while this pattern was absent in controls. Examination of DFA revealed decreased complexity of postural sway and abnormal changes in complexity upon removal of visual input in individuals with schizophrenia. Additionally, less complex postural sway was associated with increased symptom severity in participants with schizophrenia. Given the critical involvement of the cerebellum and related circuits in postural stability and sensorimotor integration, these results are consistent with growing evidence of motor, cerebellar, and sensory integration dysfunction in the disorder, and with theoretical models that implicate cerebellar deficits and more general disconnection of function in schizophrenia

Quantified HI Morphology I: Multi-Wavelengths Analysis of the THINGS Galaxies

Galaxy evolution is driven to a large extent by interactions and mergers with other galaxies and the gas in galaxies is extremely sensitive to the interactions. One method to measure such interactions uses the quantified morphology of galaxy images. Well-established parameters are Concentration, Asymmetry, Smoothness, Gini, and M20 of a galaxy image. Thus far, the application of this technique has mostly been restricted to restframe ultra-violet and optical images. However, with the new radio observatories being commissioned (MeerKAT, ASKAP, EVLA, WSRT/APERTIF, and ultimately SKA), a new window on the neutral atomic hydrogen gas (HI) morphology of a large numbers of galaxies will open up. The quantified morphology of gas disks of spirals can be an alternative indicator of the level and frequency of interaction. The HI in galaxies is typically spatially more extended and more sensitive to low-mass or weak interactions. In this paper, we explore six morphological parameters calculated over the extent of the stellar (optical) disk and the extent of the gas disk for a range of wavelengths spanning UV, Optical, Near- and Far-Infrared and 21 cm (HI) of 28 galaxies from The HI Nearby Galaxy Survey (THINGS). Though the THINGS sample is small and contains only a single ongoing interaction, it spans both non-interacting and post-interacting galaxies with a wealth of multi-wavelength data. We find that the choice of area for the computation of the morphological parameters is less of an issue than the wavelength at which they are measured. The signal of interaction is as good in the HI as in any of the other wavelengths in which morphology has been used to trace the interaction rate to date, mostly star-formation dominated ones (near- and far-ultraviolet). The Asymmetry and M20 parameters are the ones which show the most promise as tracers of interaction in 21 cm line observations.Comment: 16 pages, 11 figure, table 1, accepted by MNRAS, appendix not include

Neutron activation analysis traces copper artifacts to geographical point of origin

Impurities remaining in the metallic copper are identified and quantified by spectrographic and neutron activation analysis. Determination of the type of ore used for the copper artifact places the geographic point of origin of the artifact

Inference of synchrosqueezing transform -- toward a unified statistical analysis of nonlinear-type time-frequency analysis

We provide a statistical analysis of a tool in nonlinear-type time-frequency analysis, the synchrosqueezing transform (SST), for both the null and non-null cases. The intricate nonlinear interaction of different quantities in the SST is quantified by carefully analyzing relevant multivariate complex Gaussian random variables. Several new results for such random variables are provided, and a central limit theorem result for the SST is established. The analysis sheds lights on bridging time-frequency analysis to time series analysis and diffusion geometry

Effect of extreme data loss on long-range correlated and anti-correlated signals quantified by detrended fluctuation analysis

We investigate how extreme loss of data affects the scaling behavior of long-range power-law correlated and anti-correlated signals applying the DFA method. We introduce a segmentation approach to generate surrogate signals by randomly removing data segments from stationary signals with different types of correlations. These surrogate signals are characterized by: (i) the DFA scaling exponent $\alpha$ of the original correlated signal, (ii) the percentage $p$ of the data removed, (iii) the average length $\mu$ of the removed (or remaining) data segments, and (iv) the functional form of the distribution of the length of the removed (or remaining) data segments. We find that the {\it global} scaling exponent of positively correlated signals remains practically unchanged even for extreme data loss of up to 90%. In contrast, the global scaling of anti-correlated signals changes to uncorrelated behavior even when a very small fraction of the data is lost. These observations are confirmed on the examples of human gait and commodity price fluctuations. We systematically study the {\it local} scaling behavior of signals with missing data to reveal deviations across scales. We find that for anti-correlated signals even 10% of data loss leads to deviations in the local scaling at large scales from the original anti-correlated towards uncorrelated behavior. In contrast, positively correlated signals show no observable changes in the local scaling for up to 65% of data loss, while for larger percentage, the local scaling shows overestimated regions (with higher local exponent) at small scales, followed by underestimated regions (with lower local exponent) at large scales. Finally, we investigate how the scaling is affected by the statistics of the remaining data segments in comparison to the removed segments