Molecular characterization of grapevine from Santa Catarina, Brazil, using microsatellite markers

The objective of this work was to characterize the grape germplasm in Santa Catarina, Brazil, using microsatellite DNA markers (simple sequence repeats - SSR). The DNA samples were collected from leaves and shoots of accessions of public and private collections from the counties Urussanga, Nova Trento, Rodeio, São Joaquim, Campos Novos, Videira, and Água Doce. Ten SSR loci (VVS2, VVMD5, VVMD7, VVMD27, VrZAG62, VrZAG79, VVMD25, VVMD28, VVMD31, and VVMD32) were analysed by capillary electrophoresis. Molecular profiling was conducted for 190 grapevines (European, American, and hybrids), and 67 genotypes were obtained. The data were compared with each other and with those from the literature and from online databases, in order to identify varieties and discover cases of synonymy and homonymy. Forty molecular profiles corresponded to known varieties, while 27 genotypes were described for the first time. The existence of typical germplasm composed mainly of American and hybrid varieties is an important finding for local viticulture. Applications of the results rely on quality control and certification at the nursery level. Increasing precision in the characterization of grapevine genotypes may help breeding programs.

Mapping the human connectome at multiple scales with diffusion spectrum MRI.

The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

Spatial mapping of exciton lifetimes in single ZnO nanowires

We investigate the spatial dependence of the exciton lifetimes in single ZnO nanowires. We have found that the free exciton and bound exciton lifetimes exhibit a maximum at the center of nanowires, while they decrease by 30% towards the tips. This dependence is explained by considering the cavity-like properties of the nanowires in combination with the Purcell effect. We show that the lifetime of the bound-excitons scales with the localization energy to the power of 3/2, which validates the model of Rashba and Gurgenishvili at the nanoscale.

Comparison of microsatellite instability and chromosomal instability in predicting survival of patients with T3N0 colorectal cancer.

BACKGROUND: At least 2 apparently independent mechanisms, microsatellite instability (MSI) and chromosomal instability, are implicated in colorectal tumorigenesis. Their respective roles in predicting clinical outcomes of patients with T3N0 colorectal cancer remain unknown. METHODS: Eighty-eight patients with a sporadic T3N0 colon or rectal adenocarcinoma were followed up for a median of 67 months. For chromosomal instability analysis, Ki-ras mutations were determined by single-strand polymerase chain reaction, and p53 protein staining was studied by immunohistochemistry. For MSI analysis, DNA was amplified by polymerase chain reaction at 7 microsatellite targets (BAT25, BAT26, D17S250, D2S123, D5S346, transforming growth factor receptor II, and BAX). RESULTS: Overall 5-year survival rate was 72%. p53 protein nuclear staining was detected in 39 patients (44%), and MSI was detected in 21 patients (24%). MSI correlated with proximal location (P <.001) and mucinous content (P <.001). In a multivariate analysis, p53 protein expression carried a significant risk of death (relative risk = 4.0, 95% CI = 1.6 to 10.1, P =.004). By comparison, MSI was not a statistically significant prognostic factor for survival in this group (relative risk = 2.2, 95% CI = 0.6 to 7.3, P =.21). CONCLUSIONS: p53 protein overexpression provides better prognostic discrimination than MSI in predicting survival of patients with T3N0 colorectal cancer. Although MSI is associated with specific clinicopathologic parameters, it did not predict overall survival in this group. Assessment of p53 protein expression by immunocytochemistry provides a simple means to identify a subset of T3N0 patients with a 4-times increased risk for death.

Automated Lesion Detection-Based Symptome Mapping In Patients With Sensory Deficits

The human brain is the most complex structure known. With its high number of cells, number of connections and number of pathways it is the source of every thought in the world. It consumes 25% of our oxygen and suffers very fast from a disruption of its supply. An acute event, like a stroke, results in rapid dysfunction referable to the affected area. A few minutes without oxygen and neuronal cells die and subsequently degenerate. Changes in the brains incoming blood flow alternate the anatomy and physiology of the brain. All stroke events leave behind a brain tissue lesion. To rapidly react and improve the prediction of outcome in stroke patients, accurate lesion detection and reliable lesion-based function correlation would be very helpful. With a number of neuroimaging and clinical data of cerebral injured patients this study aims to investigate correlations of structural lesion locations with sensory functions.

Feasibility of direct mapping of cerebral fluorodeoxy-D-glucose metabolism in situ at subcellular resolution using soft X-ray fluorescence.

Glucose metabolism is difficult to image with cellular resolution in mammalian brain tissue, particularly with (18) fluorodeoxy-D-glucose (FDG) positron emission tomography (PET). To this end, we explored the potential of synchrotron-based low-energy X-ray fluorescence (LEXRF) to image the stable isotope of fluorine (F) in phosphorylated FDG (DG-6P) at 1 μm(2) spatial resolution in 3-μm-thick brain slices. The excitation-dependent fluorescence F signal at 676 eV varied linearly with FDG concentration between 0.5 and 10 mM, whereas the endogenous background F signal was undetectable in brain. To validate LEXRF mapping of fluorine, FDG was administered in vitro and in vivo, and the fluorine LEXRF signal from intracellular trapped FDG-6P over selected brain areas rich in radial glia was spectrally quantitated at 1 μm(2) resolution. The subsequent generation of spatial LEXRF maps of F reproduced the expected localization and gradients of glucose metabolism in retinal Müller glia. In addition, FDG uptake was localized to periventricular hypothalamic tanycytes, whose morphological features were imaged simultaneously by X-ray absorption. We conclude that the high specificity of photon emission from F and its spatial mapping at ≤1 μm resolution demonstrates the ability to identify glucose uptake at subcellular resolution and holds remarkable potential for imaging glucose metabolism in biological tissue. © 2012 Wiley Periodicals, Inc.

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.

Characterization of thirty two microsatellite loci for three Atlanto-Mediterranean echinoderm species

Thirty two microsatellites were optimized from 454 pyrosequencing libraries for three Atlanto-Mediterranean echinoderms: Coscinasterias tenuispina, Echinaster sepositus and Arbacia lixula. We observed different frequency of microsatellite types (di-, tri-, tetra- and pentanucleotide) throughout the genome of the species, but no significant differences were observed in allele richness among different microsatellite repeats. No loci showed linkage disequilibrium. Heterozygosity deficit and departure from Hardy Weinberg equilibrium were observed for some loci, in two species, probably due to high levels of inbreeding. Heterozygosity excess observed in C. tenuispina could be explained by selection against homozygotes and/or outcrossing.

MRS glucose mapping and PET joining forces: re-evaluation of the lumped constant in the rat brain under isoflurane anaesthesia.

Although numerous positron emission tomography (PET) studies with (18) F-fluoro-deoxyglucose (FDG) have reported quantitative results on cerebral glucose kinetics and consumption, there is a large variation between the absolute values found in the literature. One of the underlying causes is the inconsistent use of the lumped constants (LCs), the derivation of which is often based on multiple assumptions that render absolute numbers imprecise and errors hard to quantify. We combined a kinetic FDG-PET study with magnetic resonance spectroscopic imaging (MRSI) of glucose dynamics in Sprague-Dawley rats to obtain a more comprehensive view of brain glucose kinetics and determine a reliable value for the LC under isoflurane anaesthesia. Maps of Tmax /CMRglc derived from MRSI data and Tmax determined from PET kinetic modelling allowed to obtain an LC-independent CMRglc . The LC was estimated to range from 0.33 ± 0.07 in retrosplenial cortex to 0.44 ± 0.05 in hippocampus, yielding CMRglc between 62 ± 14 and 54 ± 11 μmol/min/100 g, respectively. These newly determined LCs for four distinct areas in the rat brain under isoflurane anaesthesia provide means of comparing the growing amount of FDG-PET data available from translational studies.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Outcrossing rate between 'Haden' and 'Tommy Atkins' mangoes estimated using microsatellite and AFLP markers

The objective of this work was to estimate outcrossing rates between Haden and Tommy Atkins mango cultivars, using AFLP and microsatellite markers. Progenies of an isolated 'Haden' plant, identified in a 'Tommy Atkins' commercial orchard, in Petrolina, PE, Brazil, were analyzed. Total DNA was isolated from the progeny leaves and used for AFLP and microsatellite reactions. Multilocus outcrossing rates (t m) were estimated by direct count of AFLP or microsatellite markers and by the mLTR software. Outcrossing rates ranged from 0.85 to 0.87 with the analysis based on seven AFLP markers, and from 0.83 to 0.91 based on three microsatellite primers. No unexpected band patterns were observed for 'Haden' and 'Tommy Atkins'. The estimates obtained with the mLTR software were close to those obtained by direct AFLP and microsatellite allele counting, which indicates that the multilocus model was appropriate for this kind of study. The microsatellites mMiCIR005, mMiCIR030, and mMiCIR036 can be used to elucidate the origin of 'Haden' and 'Tommy Atkins' seedlings.