902 resultados para Prediction of species potential distribution


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Zero valent iron nanoparticles (nZVI) are considered very promising for the remediation of contaminated soils and groundwaters. However, an important issue related to their limited mobility remains unsolved. Direct current can be used to enhance the nanoparticles transport, based on the same principles of electrokinetic remediation. In this work, a generalized physicochemical model was developed and solved numerically to describe the nZVI transport through porous media under electric field, and with different electrolytes (with different ionic strengths). The model consists of the Nernst–Planck coupled system of equations, which accounts for the mass balance of ionic species in a fluid medium, when both the diffusion and electromigration of the ions are considered. The diffusion and electrophoretic transport of the negatively charged nZVI particles were also considered in the system. The contribution of electroosmotic flow to the overall mass transport was included in the model for all cases. The nZVI effective mobility values in the porous medium are very low (10−7–10−4 cm2 V−1 s−1), due to the counterbalance between the positive electroosmotic flow and the electrophoretic transport of the negatively charged nanoparticles. The higher the nZVI concentration is in the matrix, the higher the aggregation; therefore, low concentration of nZVI suspensions must be used for successful field application.

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INTRODUCTION: Lutzomyia longipalpis is the main vector of Leishmania infantum chagasi in the Americas. METHODS: Phlebotomine captures were conducted during 2008 and 2009 in a rural area endemic for cutaneous leishmaniasis located in the municipality of Saquarema, Rio de Janeiro. RESULTS: Among other species captured, we observed the presence of Lutzomyia longipalpis. CONCLUSIONS: This is the first report of the occurrence of Lutzomyia longipalpis in this region, demonstrating the potential risk of visceral leishmaniasis transmission in the coastal area of the State of Rio de Janeiro. Therefore, active vigilance by all municipalities in the area is necessary.

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Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

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The identification of new and druggable targets in bacteria is a critical endeavour in pharmaceutical research of novel antibiotics to fight infectious agents. The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. A new model that combines the singular value decomposition technique with biological filters comprised of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of E. coli has been developed to predict potential drug targets in the Enterobacteriaceae family [1]. This model identified 99 potential target proteins amongst the studied bacterial family, exhibiting eight different functions that suggest that the disruption of the activities of these proteins is critical for cells. Out of these candidates, one was selected for target confirmation. To find target modulators, receptor-based pharmacophore hypotheses were built and used in the screening of a virtual library of compounds. Postscreening filters were based on physicochemical and topological similarity to known Gram-negative antibiotics and applied to the retrieved compounds. Screening hits passing all filters were docked into the proteins catalytic groove and 15 of the most promising compounds were purchased from their chemical vendors to be experimentally tested in vitro. To the best of our knowledge, this is the first attempt to rationalize the search of compounds to probe the relevance of this candidate as a new pharmacological target.

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Background: The equations predicting maximal oxygen uptake (VO2max or peak) presently in use in cardiopulmonary exercise testing (CPET) softwares in Brazil have not been adequately validated. These equations are very important for the diagnostic capacity of this method. Objective: Build and validate a Brazilian Equation (BE) for prediction of VO2peak in comparison to the equation cited by Jones (JE) and the Wasserman algorithm (WA). Methods: Treadmill evaluation was performed on 3119 individuals with CPET (breath by breath). The construction group (CG) of the equation consisted of 2495 healthy participants. The other 624 individuals were allocated to the external validation group (EVG). At the BE (derived from a multivariate regression model), age, gender, body mass index (BMI) and physical activity level were considered. The same equation was also tested in the EVG. Dispersion graphs and Bland-Altman analyses were built. Results: In the CG, the mean age was 42.6 years, 51.5% were male, the average BMI was 27.2, and the physical activity distribution level was: 51.3% sedentary, 44.4% active and 4.3% athletes. An optimal correlation between the BE and the CPET measured VO2peak was observed (0.807). On the other hand, difference came up between the average VO2peak expected by the JE and WA and the CPET measured VO2peak, as well as the one gotten from the BE (p = 0.001). Conclusion: BE presents VO2peak values close to those directly measured by CPET, while Jones and Wasserman differ significantly from the real VO2peak.

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The adult, pupal and larval morphology of S. argentiscutum sp. nov., a member of the S. amazonicum-group, is compared with S. amazonicum Goeldi and its distribution, biology and medical importance are discussed. S. argentiscutum is one the most important anthropophilic species in the S. amazonicum-group as it is responsible, together with S. amazonicum, for the transmission of Mansonella ozzardi in Brazil.

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In a series of papers (Tang, Chin and Rao, 2008; and Tang, Petrie and Rao 2006 & 2007), we have tried to improve on a mortality-based health status indicator, namely age-at-death (AAD), and its associated health inequality indicators that measure the distribution of AAD. The main contribution of these papers is to propose a frontier method to separate avoidable and unavoidable mortality risks. This has facilitated the development of a new indicator of health status, namely the Realization of Potential Life Years (RePLY). The RePLY measure is based on the concept of a “frontier country” that, by construction, has the lowest mortality risks for each age-sex group amongst all countries. The mortality rates of the frontier country are used as a proxy for the unavoidable mortality rates, and the residual between the observed mortality rates and the unavoidable mortality rates are considered as avoidable morality rates. In this approach, however, countries at different levels of development are benchmarked against the same frontier country without considering their heterogeneity. The main objective of the current paper is to control for national resources in estimating (conditional) unavoidable and avoidable mortality risks for individual countries. This allows us to construct a new indicator of health status – Realization of Conditional Potential Life Years (RCPLY). The paper presents empirical results from a dataset of life tables for 167 countries from the year 2000, compiled and updated by the World Health Organization. Measures of national average health status and health inequality based on RePLY and RCPLY are presented and compared.

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The drivers of species diversification and persistence are of great interest to current biogeography, especially in those global biodiversity hotspots' harbouring most of Earth's animal and plant life. Classical multispecies biogeographical work has yielded fascinating insights into broad-scale patterns of diversification, and DNA-based intraspecific phylogeographical studies have started to complement this picture at much finer temporal and spatial scales. The advent of novel next-generation sequencing (NGS) technologies provides the opportunity to greatly scale up the numbers of individuals, populations and species sampled, potentially merging intraspecific and interspecific approaches to biogeographical inference. Here, we outline these prospects and issues by using the example of an undisputed hotspot, the Cape of southern Africa. We outline the current state of knowledge on the biogeography of species diversification within the Cape, review the literature for phylogeographical evidence of its likely drivers and mechanisms, and suggest possible ways forward based on NGS approaches. We demonstrate the potential of these methods and current bioinformatic issues with the help of restriction-site-associated DNA (RAD) sequencing data for three highly divergent species of the Restionaceae, an important plant radiation in the Cape. A thorough understanding of the mechanisms that facilitate species diversification and persistence in spatially structured, species-rich environments will require the adoption of novel genomic and bioinformatic tools in biogeographical studies.

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BACKGROUND: As embryo selection is not allowed by law in Switzerland, we need a single early scoring system to identify zygotes with high implantation potential and to select zygotes for fresh transfer or cryopreservation. The underlying aim is to maximize the cumulated pregnancy rate while limiting the number of multiple pregnancies. METHODS: In all, 613 fresh and 617 frozen-thawed zygotes were scored for proximity, orientation and centring of the pronuclei, cytoplasmic halo, and number and polarization of the nucleolar precursor bodies. From these individual scores, a cumulated pronuclear score (CPNS) was calculated. Correlation between CPNS and implantation was examined and compared between fresh and frozen-thawed zygotes. The effect of freezing on CPNS was also investigated. RESULTS: CPNS was positively associated with embryo implantation in both fresh and frozen zygotes. With similar CPNS, frozen zygotes presented implantation rates as high as those of fresh zygotes. Nucleolar precursor bodies pattern and cytoplasmic halo appeared as the most important factors predictive of implantation for both types of zygotes, while pronuclei position was specifically relevant for frozen-thawed zygotes. Freezing induced an alteration of most zygote parameters, resulting in a significantly lower CPNS and a lower pregnancy rate. CONCLUSIONS: CPNS may be used as a single prognostic tool for implantation of both fresh and frozen-thawed zygotes. Lower CPNS values of frozen-thawed zygotes may also be indicative of freezing damage to zygotes. Successful implantation of frozen zygotes despite lower CPNS suggests that they may recover after thawing and in vitro culture.

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Anophelines harbour a diverse microbial consortium that may represent an extended gene pool for the host. The proposed effects of the insect microbiota span physiological, metabolic and immune processes. Here we synthesise how current metagenomic tools combined with classical culture-dependent techniques provide new insights in the elucidation of the role of the Anopheles-associated microbiota. Many proposed malaria control strategies have been based upon the immunomodulating effects that the bacterial components of the microbiota appear to exert and their ability to express anti-Plasmodium peptides. The number of identified bacterial taxa has increased in the current “omics” era and the available data are mostly scattered or in “tables” that are difficult to exploit. Published microbiota reports for multiple anopheline species were compiled in an Excel® spreadsheet. We then filtered the microbiota data using a continent-oriented criterion and generated a visual correlation showing the exclusive and shared bacterial genera among four continents. The data suggested the existence of a core group of bacteria associated in a stable manner with their anopheline hosts. However, the lack of data from Neotropical vectors may reduce the possibility of defining the core microbiota and understanding the mosquito-bacteria interactive consortium.

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BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

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An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

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The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, using the standard tools of forensic DNA profiling (i.e., STR markers), the profile of the minor contributor in mixed DNA stains cannot be successfully detected if its quantitative share of DNA is less than 10% of the mixed trace. This is due to the fact that the major contributor's profile "masks" that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP) linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed [1]. These novel markers are called DIP-STR markers. This paper compares, from a statistical and forensic perspective, the potential usefulness of these novel DIP-STR markers (i) with traditional STR markers in cases of moderately unbalanced mixtures, and (ii) with Y-STR markers in cases of female-male mixtures. This is done through a comparison of the distribution of 100,000 likelihood ratio values obtained using each method on simulated mixtures. This procedure is performed assuming, in turn, the prosecution's and the defence's point of view.

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The aim of this work was the use of NIR technology by direct application of a fiber optic probe on back fat to analyze the fatty acid composition of CLA fed boars and gilts. 265 animals were fed 3 different diets and the fatty acid profile of back fat from Gluteus medius was analyzed using gas chromatography and FT-NIR. Spectra were acquired using a Bruker Optics Matrix-F duplex spectrometer equipped with a fiber optic probe (IN-268-2). Oleic and stearic fatty acids were predicted accurately; myristic, vaccenic and linoleic fatty acids were predicted with lower accuracy, while palmitic and α-linolenic fatty acids were poorly predicted. The relative percentage of fatty acids and NIR spectra showed differences in fatty acid composition of back fat from pigs fed CLA which increased the relative percentage of SFA and PUFA while MUFA decreased. Results suggest that a NIR fiber optic probe can be used to predict total saturated and unsaturated fatty acid composition, as well as the percentage of stearic and oleic. NIR showed potential as a rapid and easily implemented method to discriminate carcasses from animals fed different diets.

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There is no doubt about the necessity of protecting digital communication: Citizens are entrusting their most confidential and sensitive data to digital processing and communication, and so do governments, corporations, and armed forces. Digital communication networks are also an integral component of many critical infrastructures we are seriously depending on in our daily lives. Transportation services, financial services, energy grids, food production and distribution networks are only a few examples of such infrastructures. Protecting digital communication means protecting confidentiality and integrity by encrypting and authenticating its contents. But most digital communication is not secure today. Nevertheless, some of the most ardent problems could be solved with a more stringent use of current cryptographic technologies. Quite surprisingly, a new cryptographic primitive emerges from the ap-plication of quantum mechanics to information and communication theory: Quantum Key Distribution. QKD is difficult to understand, it is complex, technically challenging, and costly-yet it enables two parties to share a secret key for use in any subsequent cryptographic task, with an unprecedented long-term security. It is disputed, whether technically and economically fea-sible applications can be found. Our vision is, that despite technical difficulty and inherent limitations, Quantum Key Distribution has a great potential and fits well with other cryptographic primitives, enabling the development of highly secure new applications and services. In this thesis we take a structured approach to analyze the practical applicability of QKD and display several use cases of different complexity, for which it can be a technology of choice, either because of its unique forward security features, or because of its practicability.