Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Nonlinear analysis of human physical activity patterns in health and disease.

The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.

Vibration Training Elicits a Mixed Aerobic and Resistance-type Exercise in Sedentary Subjects

PURPOSE: We hypothesize that untrained subjects can benefit from a greater cardiovascular stimulation than trained athletes, resembling classical aerobic-type activity, in addition to eliciting strength gains.METHODS: 3 groups of male subjects, inactive (SED), endurance trained (END) and strength trained (STR) underwent fitness (VO2max) and lower-body strength tests (isokinetic). Subjects were submitted to a session of oscillating VT, composed of 3 exercises (isometric half-squat, dynamic squat, dynamic squat with added load), each of 3 minutes duration, and repeated at 3 vibration frequencies (20, 26 and 32 Hz). VO2, heart rate and Borg scale were monitored.RESULTS: 27 healthy subjects (10 SED, 9 END and 8 STR), mean age 24.5 (SED), 25.0 (STR) and 29.8 (END) were included. VO2max was significantly different as expected (47.9 vs. 52.9 vs. 63.9 mL?min-1?kg-1, resp. for SED, STR and END). Isokinetic dominant leg extensors strength was higher in STR (3.32 N?m?kg-1 vs. 2.60 and 2.74 in SED and END). During VT, peak oxygen consumption (% of VO2max) attained was 59.3 in SED, 50.8 in STR and 48.0 in END (P<0.001 between SED and other subjects). Peak heart rate (% of heart rate max) was 82.7 in SED, 80.4 in STR and 72.4 in END. In SED, dynamic exercises without extra load elicited 51.0 % of VO2max and 72.1 % of heart rate max, and perceived effort reached 15.1/20.CONCLUSIONS: VT is an unconventional type of exercise, known to enhance strength, bone density, balance and flexibility. Users are attracted by the relative passivity. In SED, VT elicits sufficient cardiovascular response to benefit overall fitness in addition to the strength effects. VT's higher acceptance as an exercise in sedentary people, compared to jogging or cycling, can lead to better adherence to physical activity. Although long-term effects of VT on health are not available, we believe this type of mixed aerobic and resistance-type exercise can be beneficial on multiple health parameters, especially cardiovascular health.

Stimulus statistics shape oscillations in nonlinear recurrent neural networks.

Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Continuing Medical Education Self-Assessment Questions Vol. 50, No. 2 - April 2014. Focal vibration in neurorehabilitation.

During the last decade, many studies have been carried out to understand the effects of focal vibratory stimuli at various levels of the central nervous system and to study pathophysiological mechanisms of neurological disorders as well as the therapeutic effects of focal vibration in neurorehabilitation. This review aimed to describe the effects of focal vibratory stimuli in neurorehabilitation including the neurological diseases or disorders like stroke, spinal cord injury, multiple sclerosis, Parkinson's' disease and dystonia. In conclusion, focal vibration stimulation is well tolerated, effective and easy to use, and it could be used to reduce spasticity, to promote motor activity and motor learning within a functional activity, even in gait training, independent from etiology of neurological pathology. Further studies are needed in the future well- designed trials with bigger sample size to determine the most effective frequency, amplitude and duration of vibration application in the neurorehabilitation.