Prolonged but not short negative energy condition restored corticoadrenal leptin sensitivity in the hypothalamic obese rat.

BACKGROUND/AIM: We have reported that neonatal treatment with monosodium L-glutamate (MSG), which causes damage to the arcuate nucleus, leads to severe hyperleptinemia and reduced adrenal leptin receptor (ob-Rb) expression in adulthood. As a result, rats given MSG neonatally display corticoadrenal leptin-resistance, a defect that is overridden by normalization of corticoadrenal hyperfunction. The aim of the present study was to determine whether negative energy conditions could correct corticoadrenal cell dysfunction in rats given MSG neonatally. METHODS: Normal (CTR) and MSG-treated female rats were subjected to food removal for 1-5 days, or prolonged (24-61 days) food restriction (FR). Plasma levels of several biomarkers and in vitro corticoadrenal function were evaluated following starvation or FR. RESULTS: Fasting for 1-5 days reduced plasma leptin levels in CTR and MSG rats, compared to levels in the respective groups fed ad libitum(p < 0.05), but adrenal leptin-resistance was unchanged. With prolonged FR, isolated adrenal cells from MSG rats became sensitive to leptin, which lowered ACTH-induced glucocorticoid release. This restoration of leptin response was associated with normalization of adrenal ob-Rb gene expression. CONCLUSION: Dietary restriction in some leptin-resistant obese phenotypes may normalize adrenocortical function.

Brazilian maize genotypes sensitivity to water deficit estimated through a simple crop yield model

The objective of this work was to determine the sensitivity of maize (Zea mays) genotypes to water deficit, using a simple agrometeorological crop yield model. Crop actual yield and agronomic data of 26 genotypes were obtained from the Maize National Assays carried out in ten locations, in four Brazilian states, from 1998 to 2006. Weather information for each experimental location and period were obtained from the closest weather station. Water deficit sensitivity index (Ky) was determined using the crop yield depletion model. Genotypes can be divided into two groups according to their resistance to water deficit. Normal resistance genotypes had Ky ranging from 0.4 to 0.5 in vegetative period, 1.4 to 1.5 in flowering, 0.3 to 0.6 in fruiting, and 0.1 to 0.3 in maturing period, whereas the higher resistance genotypes had lower values, respectively 0.2-0.4, 0.7-1.2, 0.2-0.4, and 0.1-0.2. The general Ky for the total growing season was 2.15 for sensitive genotypes and 1.56 for the resistant ones. Model performance was acceptable to evaluate crop actual yield, whose average errors estimated for each genotype ranged from -5.7% to +5.8%, and whose general mean absolute error was 960 kg ha-1 (10%).

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Spectral analysis and limit behaviours in a spring-mass system

We consider a model for a damped spring-mass system that is a strongly damped wave equation with dynamic boundary conditions. In a previous paper we showed that for some values of the parameters of the model, the large time behaviour of the solutions is the same as for a classical spring-mass damper ODE. Here we use spectral analysis to show that for other values of the parameters, still of physical relevance and related to the effect of the spring inner viscosity, the limit behaviours are very different from that classical ODE

Mating type, mefenoxam sensitivity, and pathotype diversity in Phytophthora infestans isolates from tomato in Brazil

The objective of this work was to characterize 79 Phytophthora infestans isolates collected in tomato (Solanum lycopersicum) fields, as to mating type, mefenoxam sensitivity, and pathotype composition. The isolates were sampled in 2006 and 2007 in seven Brazilian states as well as in the Distrito Federal. They were characterised as to mating type (n=79), sensitivity to fungicide mefenoxam (n=79), and virulence to three major resistance genes Ph-1, Ph-2, and Ph-3/Ph-4 (n=62). All isolates were of the mating type A1. Resistant isolates were detected in all sampled states, and its average frequency was superior to 50%. No difference was detected in pathotype diversity, neither between subpopulations collected in 2006 and 2007 nor between isolates grouped as resistant or intermediately sensitive to mefenoxam. All major resistance genes were overcome at different frequencies: Ph-1, 88.7%; Ph-2, 64.5%; and Ph-3/Ph-4, 25.8%. Isolates with virulence genes able to overcome all major resistance genes were detected at low frequencies. Tomato breeding programs in Brazil must avoid the development of cultivars with resistance based exclusively on major genes.

Analysis of sensitivity with respect to a compositional parameter : A comment on 'Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling' by L. Loosvelt and co-authors

A comment about the article “Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling” writen by L. Loosvelt and co-authors. The present comment is centered in three specific points. The first one is related to the fact that the authors avoid the use of ilr-coordinates. The second one refers to some generalization of sensitivity analysis when input parameters are compositional. The third tries to show that the role of the Dirichlet distribution in the sensitivity analysis is irrelevant

Sensitivity of single-voxel 1H-MRS in investigating the metabolism of the activated human visual cortex at 7 T.

Proton magnetic resonance spectroscopy (1H-MRS) has been used in a number of studies to noninvasively assess the temporal changes of lactate in the activated human brain. However, the results have not been consistent. The aim of the present study was to test the sensitivity of 1H-MRS during functional experiments at the highest magnetic field currently available for human studies (7 T). Stability and reproducibility of the measurements were evaluated from LCModel analysis of time series of spectra measured during a visual stimulation paradigm and by examination of the difference between spectra obtained at rest and during activation. The sensitivity threshold to detect concentration changes was 0.2 micromol/g for most of the quantified metabolites. The possible variations of metabolite concentrations during visual stimulation were within the same range (+/-0.2 micromol/g). In addition, the influence of a small line-narrowing effect due to the blood oxygenation level-dependent (BOLD) T2* changes on the estimated concentrations was simulated. Quantification of metabolites was, in general, not affected beyond 1% by line-width changes within 0.5 Hz.

The relationship between virtual body ownership and temperature sensitivity.

In the rubber hand illusion tactile stimulation seen on a rubber hand, that is synchronous with tactile stimulation felt on the hidden real hand, can lead to an illusion of ownership over the rubber hand. This illusion has been shown to produce a temperature decrease in the hidden hand, suggesting that such illusory ownership produces disownership of the real hand. Here we apply immersive virtual reality (VR) to experimentally investigate this with respect to sensitivity to temperature change. Forty participants experienced immersion in a VR with a virtual body (VB) seen from a first person perspective. For half the participants the VB was consistent in posture and movement with their own body, and in the other half there was inconsistency. Temperature sensitivity on the palm of the hand was measured before and during the virtual experience. The results show that temperature sensitivity decreased in the consistent compared to the inconsistent condition. Moreover, the change in sensitivity was significantly correlated with the subjective illusion of virtual arm ownership but modulated by the illusion of ownership over the full virtual body. This suggests that a full body ownership illusion results in a unification of the virtual and real bodies into one overall entity - with proprioception and tactile sensations on the real body integrated with the visual presence of the virtual body. The results are interpreted in the framework of a"body matrix" recently introduced into the literature.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Sensitivity of Spring Phenology to Warming Across Temporal and Spatial Climate Gradients in Two Independent Databases

Disparate ecological datasets are often organized into databases post hoc and then analyzed and interpreted in ways that may diverge from the purposes of the original data collections. Few studies, however, have attempted to quantify how biases inherent in these data (for example, species richness, replication, climate) affect their suitability for addressing broad scientific questions, especially in under-represented systems (for example, deserts, tropical forests) and wild communities. Here, we quantitatively compare the sensitivity of species first flowering and leafing dates to spring warmth in two phenological databases from the Northern Hemisphere. One-PEP725-has high replication within and across sites, but has low species diversity and spans a limited climate gradient. The other-NECTAR-includes many more species and a wider range of climates, but has fewer sites and low replication of species across sites. PEP725, despite low species diversity and relatively low seasonality, accurately captures the magnitude and seasonality of warming responses at climatically similar NECTAR sites, with most species showing earlier phenological events in response to warming. In NECTAR, the prevalence of temperature responders significantly declines with increasing mean annual temperature, a pattern that cannot be detected across the limited climate gradient spanned by the PEP725 flowering and leafing data. Our results showcase broad areas of agreement between the two databases, despite significant differences in species richness and geographic coverage, while also noting areas where including data across broader climate gradients may provide added value. Such comparisons help to identify gaps in our observations and knowledge base that can be addressed by ongoing monitoring and research efforts. Resolving these issues will be critical for improving predictions in understudied and under-sampled systems outside of the temperature seasonal mid-latitudes.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.