Multidirectional In Vivo Characterization of Skin Using Wiener Nonlinear Stochastic System Identification Techniques

A triaxial force-sensitive microrobot was developed to dynamically perturb skin in multiple deformation modes, in vivo. Wiener static nonlinear identification was used to extract the linear dynamics and static nonlinearity of the force–displacement behavior of skin. Stochastic input forces were applied to the volar forearm and thenar eminence of the hand, producing probe tip perturbations in indentation and tangential extension. Wiener static nonlinear approaches reproduced the resulting displacements with variances accounted for (VAF) ranging 94–97%, indicating a good fit to the data. These approaches provided VAF improvements of 0.1–3.4% over linear models. Thenar eminence stiffness measures were approximately twice those measured on the forearm. Damping was shown to be significantly higher on the palm, whereas the perturbed mass typically was lower. Coefficients of variation (CVs) for nonlinear parameters were assessed within and across individuals. Individual CVs ranged from 2% to 11% for indentation and from 2% to 19% for extension. Stochastic perturbations with incrementally increasing mean amplitudes were applied to the same test areas. Differences between full-scale and incremental reduced-scale perturbations were investigated. Different incremental preloading schemes were investigated. However, no significant difference in parameters was found between different incremental preloading schemes. Incremental schemes provided depth-dependent estimates of stiffness and damping, ranging from 300 N/m and 2 Ns/m, respectively, at the surface to 5 kN/m and 50 Ns/m at greater depths. The device and techniques used in this research have potential applications in areas, such as evaluating skincare products, assessing skin hydration, or analyzing wound healing.Foundation for Research, Science & Technology (N.Z.) (Grants UOA21647.001 and NERF 9077/3608892)Tertiary Education Commission of New Zealand (Medical Technologies Centre of Research Excellence (MedTech CoRE)

A Case of Urogenital Human Schistosomiasis from a Non-endemic Area

© 2015 Calvo-Cano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The attached file is the published version of the article

Use of materials in nest construction by Pied Flycatchers Ficedula hypoleuca reflects localised habitat and geographical location.

Capsule Pied Flycatchers use different materials to construct their nests according to localised habitat and geographical location. Aims This study tested the hypotheses that birds would use the leaves they normally encountered within their breeding territories and that nest composition varied between geographical locations. Methods In Lancashire, Pied Flycatcher nests were collected from nestboxes built in locations dominated by different tree species and were deconstructed to determine which materials were used. Results Materials found in nests generally reflected the localised habitat around the nest rather than showing evidence of active collection from distant sources of material. Nests from Lancashire were significantly different in composition when compared with published data for nests from north Wales and central Spain. The use of moss was dominated by the use of one species in all but two nests. Conclusion Pied Flycatchers exhibit plasticity in nest construction behaviour because they were opportunistic in their choice of most nesting materials although they may be selective in their choice of moss

Interchangeability of position tracking technologies; can we merge the data?

Purpose: The purpose of this study was to assess the interchangeability of position tracking metrics obtained using global positioning systems (GPS) versus those obtained by a semi-automatic high definition (HD) optical camera system. Methods: Data was collected from a cohort of 29 elite soccer players (age: 23.1 ± 5.1 years, height: 180.4 ± 5.8 cm, mass: 74.6 ± 6.7 kg) in four matches played in four different stadiums. In two matches 10Hz GPS (GPS-1, StatSports, Belfast, UK) were used, while in the other two matches augmented 10Hz GPS (GPS-2, StatSports, Belfast, UK) were used. All four matches were analysed concomitantly using six semi-automated HD motion cameras sampling at 25Hz (TRACAB, Chyronhego, New York, USA). Results: Mean bias was between 6-10% for GPS-1 and 1-4% for GPS-2 respectively. No proportional bias was found (p > 0.184). The SEE within calibration functions (expressed in % to mean) was between 5-22% for GPS-1 and 4-14% for GPS-2. While some significant differences existed between GPS-1 and TRACAB (total distance and high-speed), positional tracking variables were highly correlated between GPS-1, GPS-2 and TRACAB (r2 > 0.92) with GPS-2 displaying stronger correlations (> r2 = 0.96). Conclusion: In the present study augmented GPS technology (GPS-2) and the TRACAB camera system provided interchangeable measures of positional tracking metrics to allow concurrent assessment and monitoring of training and competition in soccer players. However, we recommend practitioners evaluate their own systems to identify where errors exist and re-calibrate accordingly to confidently interchange data

A unified vegetation index for quantifying the terrestrial biosphere

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change

A unified vegetation index for quantifying the terrestrial biosphere

[EN] Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.G.C.-V. was supported by the European Research Council (ERC) under the ERC Consolidator Grant 2014 project SEDAL (647423). M.C.-T. and F.J.G.-H. were supported by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA-SAF). SR research was financially supported by the NASA Earth Observing System MODIS project (grant NNX08AG87A). J.A.G. acknowledges the support of NASA ABoVE award number NNX15AT78A. S.W. acknowledges funding from the Emmy Noether Programme (GlobFluo project) of the German Research Foundation (GU 1276/1-1) as well as funding from the European Union's Horizon 2020 research and innovation program under grant agreement 776186 (CHE project) and agreement 776810 (VERIFY project).Camps-Valls, G.; Campos-Taberner, M.; Moreno-Martínez, Á.; Walther, S.; Duveiller, G.; Cescatti, A.; Mahecha, MD.... (2021). A unified vegetation index for quantifying the terrestrial biosphere. Science Advances. 7(9):1-11. https://doi.org/10.1126/sciadv.abc74471117

A statistical modeling framework for characterising uncertainty in large datasets: Application to ocean colour

Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data

Effect of air movement on the thermal insulation of avian nests

Capsule: Air movement over a nest increases the rate of cooling within the nest cup but the walls provide good thermal insulation. Aims: This study compared nests of six bird species of the families Fringillidae and Motacillidae to investigate the insulative properties in still and moving air treatments. It was hypothesized that differences in nest size and moving air would differ between species and would have a significant effect on insulatory values of the nests. Methods: Nest dimensions were measured for a total of 35 nests from six species. Thermal properties of the nests were recorded using temperature loggers within nests placed in a wind tunnel under still and moving air conditions. Results: Insulatory values and internal nest cooling rates were significantly increased by moving air. There was no significant difference between species for the thermal properties of nests but nest mass correlated with greater insulatory values and a lower rate of cooling within the nest cup. Nest wall thickness had no significant effect on the thermal characteristics of the nests. Conclusion: The use of a constructed nest mitigated the effects of air movement but the differences between species reflected difference in nest mass rather than wall thickness