A new disruption vector (pDHO) to obtain heterothallic strains from both Saccharomyces cerevisiae and Saccharomyces pastorianus

Yeasts are responsible for several traits in fermented beverages, including wine and beer, and their genetic manipulation is often necessary to improve the quality of the fermentation product. Improvement of wild-type strains of Saccharomyces cerevisiae and Saccharomyces pastorianus is difficult due to their homothallic character and variable ploidy level. Homothallism is determined by the HO gene in S. cerevisiae and the Sc-HO gene in S. pastorianus. In this work, we describe the construction of an HO disruption vector (pDHO) containing an HO disruption cassette and discuss its use in generating heterothallic yeast strains from homothallic Saccharomyces species.

Hot spots for diversity of Magnaporthe oryzae physiological races in irrigated rice fields in Brazil

The objective of this work was to evaluate the Magnaporthe oryzae pathotype diversity in new commercial irrigated rice fields in the Araguaia River Valley, state of Tocantins, Brazil. The causal agent of rice blast has heavily affected rice production in the region. Despite the efforts of breeding programs, blast resistance breakdown has been recorded shortly after the release of new resistant cultivars developed for the region. Among the causes of resistance breakage is the capacity of the fungus to rapidly develop new pathotypes. A sample of 479 M. oryzae monosporic isolates was obtained and tested using the international rice blast differential set. Isolate collections were made in small areas designed as trap nurseries and in scattered sites in their vicinity. Analysis of 250 M. oryzae isolates from three trap nurseries indicated the presence of 45 international M. oryzae races belonging to seven pathotype groups (IA-IG). In the isolates tested, 61 M. oryzae pathotypes belonging to all but the IH group were detected. The new areas of irrigated rice in the Araguaia River Valley have the highest diversity of M. oryzae pathotypes reported so far in Brazil.

Robust vaccine-elicited cellular immune responses in breast milk following systemic Simian Immunodeficiency Virus DNA prime and live virus vector boost vaccination of lactating rhesus monkeys.

Breast milk transmission of HIV remains an important mode of infant HIV acquisition. Enhancement of mucosal HIV-specific immune responses in milk of HIV-infected mothers through vaccination may reduce milk virus load or protect against virus transmission in the infant gastrointestinal tract. However, the ability of HIV/SIV strategies to induce virus-specific immune responses in milk has not been studied. In this study, five uninfected, hormone-induced lactating, Mamu A*01(+) female rhesus monkey were systemically primed and boosted with rDNA and the attenuated poxvirus vector, NYVAC, containing the SIVmac239 gag-pol and envelope genes. The monkeys were boosted a second time with a recombinant Adenovirus serotype 5 vector containing matching immunogens. The vaccine-elicited immunodominant epitope-specific CD8(+) T lymphocyte response in milk was of similar or greater magnitude than that in blood and the vaginal tract but higher than that in the colon. Furthermore, the vaccine-elicited SIV Gag-specific CD4(+) and CD8(+) T lymphocyte polyfunctional cytokine responses were more robust in milk than in blood after each virus vector boost. Finally, SIV envelope-specific IgG responses were detected in milk of all monkeys after vaccination, whereas an SIV envelope-specific IgA response was only detected in one vaccinated monkey. Importantly, only limited and transient increases in the proportion of activated or CCR5-expressing CD4(+) T lymphocytes in milk occurred after vaccination. Therefore, systemic DNA prime and virus vector boost of lactating rhesus monkeys elicits potent virus-specific cellular and humoral immune responses in milk and may warrant further investigation as a strategy to impede breast milk transmission of HIV.

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.

Rpe65-Gene Transfer Using an Integration-Deficient Lentiviral Vector

Purpose: We previously demonstrated efficient retinal rescue of RPE65 mouse models (Rpe65-/- (Bemelmans et al, 2006) and Rpe65R91W/R91W mice) using a HIV1-derived lentiviral vector encoding for the mouse RPE65 cDNA. In order to optimize a lentiviral vector as an alternative tool for RPE65-derived Leber Congenital Amaurosis clinical trials, we evaluated the efficiency of an integration-deficient lentiviral vector (IDLV) encoding the human RPE65 cDNA to restore retinal function in the Rpe65R91W/R91W mice. Methods: An HIV-1-derived lentiviral vector expressing either the hrGFPII or the human Rpe65 cDNA under the control of a 0.8 kb fragment of the human Rpe65 promoter (R0.8) was produced by transient transfection of 293T cells. A LQ-integrase mutant was used to generate the IDLV vectors. IDLV-R0.8-hRPE65 or hrGFPII were injected subretinally into 1 month-old Rpe65R91W/R91W mice. Functional rescue was assessed by ERG (1 and 3 months post-injection) and cone survival by immunohistology. Results: An increased light sensitivity was detected by scotopic ERG in animals injected with IDLV-R0.8-hRPE65 compared to hrGFPII-treated animals or untreated mice. However the improvement was delayed compared to integration-proficient LV and observed at 3 months but not 1 month post-injection. Immunolabelling of cone markers showed an increased number of cones in the transduced area compared to control groups. Conclusions: The IDLV-R0.8-hRPE65 vectors allow retinal improvement in the Rpe65R91W/R91W mice. Both rod function and cone survival were demonstrated even if there is a delay in the rescue as assessed by scotopic ERG. Integration-deficient vectors minimize insertional mutagenesis and thus are safer candidates for human application. Further experiments using large animals are now needed to validate correct gene transfer and expression of the RPE65 gene as well as tolerance of the vector after subretinal injection before envisaging a clinical trial application.

Nonperturbative semiclassical stability of de Sitter spacetime for small metric deviations

We consider the linearized semiclassical Einstein equations for small deviations around de Sitter spacetime including the vacuum polarization effects of conformal fields. Employing the method of order reduction, we find the exact solutions for general metric perturbations (of scalar, vector and tensor type). Our exact (nonperturbative) solutions show clearly that in this case de Sitter is stable with respect to small metric deviations and a late-time attractor. Furthermore, they also reveal a breakdown of perturbative solutions for a sufficiently long evolution inside the horizon. Our results are valid for any conformal theory, even self-interacting ones with arbitrarily strong coupling.

Minimization of Nyquist ghosting for echo-planar imaging at ultra-high fields based on a "negative readout gradient" strategy.

PURPOSE: To improve the traditional Nyquist ghost correction approach in echo planar imaging (EPI) at high fields, via schemes based on the reversal of the EPI readout gradient polarity for every other volume throughout a functional magnetic resonance imaging (fMRI) acquisition train. MATERIALS AND METHODS: An EPI sequence in which the readout gradient was inverted every other volume was implemented on two ultrahigh-field systems. Phantom images and fMRI data were acquired to evaluate ghost intensities and the presence of false-positive blood oxygenation level-dependent (BOLD) signal with and without ghost correction. Three different algorithms for ghost correction of alternating readout EPI were compared. RESULTS: Irrespective of the chosen processing approach, ghosting was significantly reduced (up to 70% lower intensity) in both rat brain images acquired on a 9.4T animal scanner and human brain images acquired at 7T, resulting in a reduction of sources of false-positive activation in fMRI data. CONCLUSION: It is concluded that at high B(0) fields, substantial gains in Nyquist ghost correction of echo planar time series are possible by alternating the readout gradient every other volume.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

In vivo Structural Imaging of the Cerebellum, the Contribution of Ultra-High Fields.

This review covers some of the contributions to date from cerebellar imaging studies performed at ultra-high magnetic fields. A short overview of the general advantages and drawbacks of the use of such high field systems for imaging is given. One of the biggest advantages of imaging at high magnetic fields is the improved spatial resolution, achievable thanks to the increased available signal-to-noise ratio. This high spatial resolution better matches the dimensions of the cerebellar substructures, allowing a better definition of such structures in the images. The implications of the use of high field systems is discussed for several imaging sequences and image contrast mechanisms. This review covers studies which were performed in vivo in both rodents and humans, with a special focus on studies that were directed towards the observation of the different cerebellar layers.

A model of the wind direction signature in the Stokes emission vector from the ocean surface at microwave frequencies

This paper presents a model of the Stokes emission vector from the ocean surface. The ocean surface is described as an ensemble of facets with Cox and Munk's (1954) Gram-Charlier slope distribution. The study discusses the impact of different up-wind and cross-wind rms slopes, skewness, peakedness, foam cover models and atmospheric effects on the azimuthal variation of the Stokes vector, as well as the limitations of the model. Simulation results compare favorably, both in mean value and azimuthal dependence, with SSM/I data at 53° incidence angle and with JPL's WINDRAD measurements at incidence angles from 30° to 65°, and at wind speeds from 2.5 to 11 m/s.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Arbuscular mycorrhizal fungi in soil aggregates from fields of "murundus" converted to agriculture

The objective of this work was to evaluate the spore density and diversity of arbuscular mycorrhizal fungi (AMF) in soil aggregates from fields of "murundus" (large mounds of soil) in areas converted and not converted to agriculture. The experiment was conducted in a completely randomized design with five replicates, in a 5x3 factorial arrangement: five areas and three aggregate classes (macro-, meso-, and microaggregates). The evaluated variables were: spore density and diversity of AMF, total glomalin, total organic carbon (TOC), total extraradical mycelium (TEM), and geometric mean diameter (GMD) of soil aggregates. A total of 21 AMF species was identified. Spore density varied from 29 to 606 spores per 50 mL of soil and was higher in microaggregates and in the area with 6 years of conversion to agriculture. Total glomalin was higher between murundus in all studied aggregate classes. The area with 6 years showed lower concentration of TOC in macroaggregates (8.6 g kg-1) and in microaggregates (10.1 g kg-1). TEM was greater at the top of the murundus in all aggregate classes. GMD increased with the conversion time to agriculture. The density and diversity of arbuscular mycorrhizal spores change with the conversion of fields of murundus into agriculture.