Detecting nanoscale vibrations as signature of life.

The existence of life in extreme conditions, in particular in extraterrestrial environments, is certainly one of the most intriguing scientific questions of our time. In this report, we demonstrate the use of an innovative nanoscale motion sensor in life-searching experiments in Earth-bound and interplanetary missions. This technique exploits the sensitivity of nanomechanical oscillators to transduce the small fluctuations that characterize living systems. The intensity of such movements is an indication of the viability of living specimens and conveys information related to their metabolic activity. Here, we show that the nanomotion detector can assess the viability of a vast range of biological specimens and that it could be the perfect complement to conventional chemical life-detection assays. Indeed, by combining chemical and dynamical measurements, we could achieve an unprecedented depth in the characterization of life in extreme and extraterrestrial environments.

La maladie des vibrations [The vibration syndrome].

The vibration syndrome is an often unrecognized occupational neurovascular disease with a prevalence of more than 70% in certain high-risk occupations. Early recognition is crucial because continued exposure to vibration can lead to irreversible ischemic injury and loss of digits. Digital ischemia due to the vibration syndrome may be due to a vasospastic phenomenon, an organic microangiopathy or arterial thrombosis. Demyelinating neuropathy and carpal tunnel syndrome are often associated. Many pathophysiological mechanisms are implicated: hyperactivity of the central sympathetic nervous system, release of plasma endothelin-1 and loss of calcitonin-gene-related-peptide vasoregulation. Investigation tests, treatment and the European Community Directive for the protection of workers are also presented.

Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

A fully adaptive numerical approximation for a two-dimensional epidemic model with nonlinear cross-diffusion

An epidemic model is formulated by a reactionâeuro"diffusion system where the spatial pattern formation is driven by cross-diffusion. The reaction terms describe the local dynamics of susceptible and infected species, whereas the diffusion terms account for the spatial distribution dynamics. For both self-diffusion and cross-diffusion, nonlinear constitutive assumptions are suggested. To simulate the pattern formation two finite volume formulations are proposed, which employ a conservative and a non-conservative discretization, respectively. An efficient simulation is obtained by a fully adaptive multiresolution strategy. Numerical examples illustrate the impact of the cross-diffusion on the pattern formation.

Whole-body vibration training: metabolic cost of synchronous, side-alternating or no vibrations.

Whole-body vibration training improves strength and can increase maximal oxygen consumption ([·V]O(2max)). No study has compared the metabolic demand of synchronous and side-alternating whole-body vibration. We measured [·V]O₂ and heart rate during a typical synchronous or side-alternating whole-body vibration session in 10 young female sedentary participants. The 20-min session consisted of three sets of six 45-s exercises, with 15 s recovery between exercises. Three conditions were randomly tested on separate days: synchronous at 35 Hz and 4 mm amplitude, side-alternating at 26 Hz and 7.5 mm amplitude (peak acceleration matched at 20 g in both vibration conditions), and no vibrations. Mean [·V]O₂ (expressed as %[·V]O(2max)) did not differ between conditions: 29.7 ± 4.2%, 32.4 ± 6.5%, and 28.7 ± 6.7% for synchronous, side-alternating, and no vibrations respectively (P = 0.103). Mean heart rate (% maximal heart rate) was 65.6 ± 7.3%, 69.8 ± 7.9%, and 64.7 ± 5.6% for synchronous, side-alternating, and no vibrations respectively, with the side-alternating vibrations being significantly higher (P = 0.019). When analysing changes over exercise sessions, mean [·V]O₂ was higher for side-alternating (P < 0.001) than for synchronous and no vibrations. In conclusion, side-alternating whole-body vibration elicits higher heart rate responses than synchronous or no vibrations, and could elevate [·V]O₂, provided the session lasts more than 20 min.

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.

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.

The effect of mountain bike suspensions on vibrations and off-road uphill performance.

AIM: This study evaluates the effect of front suspension (FS) and dual suspension (DS) mountain-bike on performance and vibrations during off-road uphill riding. METHODS: Thirteen male cyclists (27+/-5 years, 70+/-6 kg, VO(2max)59+/-6 mL.kg(-1).min(-1), mean+/-SD) performed, in a random sequence, at their lactate threshold, an off-road uphill course (1.69 km, 212 m elevation gain) with both type of bicycles. Variable measured: a) VO(2) consumption (K4b2 analyzer, Cosmed), b) power output (SRM) c) gain in altitude and d) 3-D accelerations under the saddle and at the wheel (Physilog, EPFL, Switzerland). Power spectral analy- sis (Fourier) was performed from the vertical acceleration data. RESULTS: Respectively for the FS and DS mountain bike: speed amounted to 7.5+/-0.7 km.h(-1) and 7.4+/-0.8 km.h(-1), (NS), energy expenditure 1.39+/-0.16 kW and 1.38+/-0.18, (NS), gross efficiency 0.161+/-0.013 and 0.159+/-0.013, (NS), peak frequency of vibration under the saddle 4.78+/-2.85 Hz and 2.27+/-0.2 Hz (P<0.01) and median-frequency of vertical displacements of the saddle 9.41+/-1.47 Hz and 5.78+/-2.27 Hz (P<0.01). CONCLUSION: Vibrations at the saddle level of the DS bike are of low frequencies whereas those of the FS bike are mostly of high frequencies. In the DS bike, the torque produced by the cyclist at the pedal level may generate low frequency vibrations. We conclude that the DS bike absorbs more high frequency vibrations, is more comfortable and performs as well as the FS bicycle.