Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

IntroductionCutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.Methodology/principal findingsWe fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.SignificanceThese outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets

Percolation Critical Exponents in Scale-Free Networks

We study the behavior of scale-free networks, having connectivity distribution P(k) k^-a, close to the percolation threshold. We show that for networks with 3<a<4, known to undergo a transition at a finite threshold of dilution, the critical exponents are different than the expected mean-field values of regular percolation in infinite dimensions. Networks with 2<a<3 possess only a percolative phase. Nevertheless, we show that in this case percolation critical exponents are well defined, near the limit of extreme dilution (where all sites are removed), and that also then the exponents bear a strong a-dependence. The regular mean-field values are recovered only for a>4.Comment: Latex, 4 page

Economic Consequences of Mandated Grading and Food Safety Assurance: Ex Ante Analysis of the Federal Marketing Order for California Pistachios

Crop Production/Industries, Food Consumption/Nutrition/Food Safety, Marketing,

Discovery of the brightest T dwarf in the northern hemisphere

We report the discovery of a bright (H=12.77) brown dwarf designated SIMP J013656.5+093347. The discovery was made as part of a near-infrared proper motion survey, SIMP (Sondage Infrarouge de Mouvement Propre), which uses proper motion and near-infrared/optical photometry to identify brown dwarf candidates. A low resolution (lambda/dlambda~40) spectrum of this brown dwarf covering the 0.88-2.35 microns wavelength interval is presented. Analysis of the spectrum indicates a spectral type of T2.5+/-0.5. A photometric distance of 6.4+/-0.3 pc is estimated assuming it is a single object. Current observations rule out a binary of mass ratio ~1 and separation >5 AU. SIMP 0136 is the brightest T dwarf in the northern hemisphere and is surpassed only by Eps Indi Bab over the whole sky. It is thus an excellent candidate for detailed studies and should become a benchmark object for the early-T spectral class.Comment: 4 pages, 3 figures, To be published in November 1, 2006 issue of ApJL. Following IAU recommendation, the survey acronym (IBIS) was changed to SIM

MR Spectroscopic Imaging of Peripheral Zone in Prostate Cancer Using a 3T MRI Scanner: Endorectal versus External Phased Array Coils.

Magnetic resonance spectroscopic imaging (MRSI) detects alterations in major prostate metabolites, such as citrate (Cit), creatine (Cr), and choline (Ch). We evaluated the sensitivity and accuracy of three-dimensional MRSI of prostate using an endorectal compared to an external phased array "receive" coil on a 3T MRI scanner. Eighteen patients with prostate cancer (PCa) who underwent endorectal MR imaging and proton (1H) MRSI were included in this study. Immediately after the endorectal MRSI scan, the PCa patients were scanned with the external phased array coil. The endorectal coil-detected metabolite ratio [(Ch+Cr)/Cit] was significantly higher in cancer locations (1.667 ± 0.663) compared to non-cancer locations (0.978 ± 0.420) (P &lt; 0.001). Similarly, for the external phased array, the ratio was significantly higher in cancer locations (1.070 ± 0.525) compared to non-cancer locations (0.521 ± 0.310) (P &lt; 0.001). The sensitivity and accuracy of cancer detection were 81% and 78% using the endorectal 'receive' coil, and 69% and 75%, respectively using the external phased array 'receive' coil

Brief Note: Growth of Pisidium Casertanum (Poli) in West Central Ohio

Author Institution: Department of Biology, University of Dayto

Structure Adaptation in Stochastic Inverse Methods for Integrating Information

[EN] The use of inverse modeling techniques has greatly increased during the past several years because the advances in numerical modeling and increased computing power. Most of these methods require an a priori definition of the stochastic structure of conductivity (K) fields that is inferred only from K measurements. Therefore, the additional conditioning data, that implicitly integrate information not captured by K data, might lead to changes in the a priori model. Different inverse methods allow different degrees of structure adaptation to the whole set of data during the conditioning procedure. This paper illustrates the application of a powerful stochastic inverse method, the Gradual Conditioning (GC) method, to two different sets of data, both non-multiGaussian. One is based on a 2D synthetic aquifer and another on a real-complex case study, the Macrodispersion Experiment (MADE-2), site on Columbus Air Force Base in Mississippi (USA). We have analyzed how additional data change the a priori model on account of the perturbations performed when constraining stochastic simulations to data. Results show how the GC method tends to honour the a priori model in the synthetic case, showing fluctuations around it for the different simulated fields. However, in the 3D real case study, it is shown how the a priori structure is slightly modified not obeying just to fluctuations but possibly to the effect of the additional information on K, implicit in piezometric and concentration data. We conclude that implementing inversion methods able to yield a posteriori structure that incorporate more data might be of great importance in real cases in order to reduce uncertainty and to deal with risk assessment projects.Llopis Albert, C.; Merigó, JM.; Palacios Marqués, D. (2015). Structure Adaptation in Stochastic Inverse Methods for Integrating Information. 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