Chemical composition of flavonoids and styrylpyrones and the genetic variability of isozymes in natural populations of Cryptocarya mandioccana Meisner (Lauraceae)

The chemical composition of leaves of 57 trees of Cryptocarya mandioccana from three populations of southeastern Brazil was investigated through HPLC, assaying six flavonoids, seven styrylpyrones, and seven unidentified compounds. From 51 of the former trees, genotypes were obtained from 40 polymorphic loci of 19 isozymes. Cluster analyses of the phytochemical and genetical variation revealed that trees exhibited four chemotypes and five clusters from isozymes, respectively. Discriminant analyses from selected variables of the isozymic and chemical data sets were performed, respectively, in relation to the four chemotypes and the five isozyme clusters. The classification of individuals presented respective error estimates of 9.16% and 13.57%, indicating that the genetic data could explain the clusters from chernotypes and vice versa at acceptable error levels. Linear regressions with Dummy variable showed significant association of locus Skdh-2 with quercetin-3-O-beta-D-glucopyranoside and cryptofolione, indicating that its alleles would be responsible for the chemotype variation between individuals. (c) 2006 Elsevier Ltd. All rights reserved.

Internal genetic structure and outcrossing rate in a natural population of Araucaria angustifolia (Bert.) O. Kuntze

The internal genetic structure and outcrossing rate of a population of Araucaria angustifolia (Bert.) O. Kuntze were investigated using 16 allozyme loci. Estimates of the mean number of alleles per loci (1.6), percentage of polymorphic loci (43.8%), and expected genetic diversity (0.170) were similar to those obtained for other gymnosperms. The analysis of spatial autocorrelation demonstrated the presence of internal structure in the first distance classes (up to 70 m), suggesting the presence of family structure. The outcrossing rate was high (0.956), as expected for a dioecious species. However, it was different from unity, indicating outcrossings between related individuals and corroborating the presence of internal genetic structure. The results of this study have implications for the methodologies used in conservation collections and for the use or analysis of this forest species. © The American Genetic Association. 2006. All rights reserved.

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Local classification of singular hexagonal 3-webs with holomorphic Chern connection form and infinitesimal symmetries

Implicit ODE, cubic in derivative, generically has no infinitesimal symmetries even at regular points with distinct roots. Cartan showed that at regular points, ODEs with hexagonal 3-web of solutions have symmetry algebras of the maximal possible dimension 3. At singular points such a web can lose all its symmetries. In this paper we study hexagonal 3-webs having at least one infinitesimal symmetry at singular points. In particular, we establish sufficient conditions for the existence of non-trivial symmetries and show that under natural assumptions such a symmetry is semi-simple, i.e. is a scaling in some coordinates. Using the obtained results, we provide a complete classification of hexagonal singular 3-web germs in the complex plane, satisfying the following two conditions: 1) the Chern connection form is holomorphic at the singular point, 2) the web admits at least one infinitesimal symmetry at this point. As a by-product, a classification of hexagonal weighted homogeneous 3-webs is obtained.

Estimativa da recarga natural do Aquífero Rio Claro: uma abordagem da regionalização hidrológica através da vazão de permanência

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo do meio físico e caracterização da capacidade de suporte natural da região de Pirassununga/SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Patients With Endometriosis of the Rectosigmoid Have a Higher Percentage of Natural Killer Cells in Peripheral Blood

Study Objective: To estimate the concentration of natural killer (NK) cells in the peripheral blood in patients with and without endometriosis. Design: Case-control study (Canadian Task Force classification II-2). Setting: Tertiary referral hospital. Patients: One hundred fifty-five patients who had undergone videolaparoscopy were divided into 2 groups: those with endometriosis (n = 100) and those without endometriosis (n = 55). Interventions: The percentage of NK cells relative to peripheral lymphocytes was quantified at flow cytometry in 155 patients who had undergone laparoscopy. In addition to verifying the presence of endometriosis, stage of disease and the sites affected were also evaluated. Measurements and Main Results: The mean (SD) percentage of NK cells was higher (15.3% [9.8%]) in patients with endometriosis than in the group without the disease (10.6% [5.8%]) (p < .001). The percentage of NK cells was highest (19.8 [10.3%]) in patients with advanced stages of endometriosis and in those in whom the rectosigmoid colon was affected. In a statistical model of probability, the association of this marker (NK cells >= 11%) with the presence of symptoms such as pain and intestinal bleeding during menstruation and the absence of previous pregnancy yielded a 78% likelihood of the rectosigmoid colon being affected. Conclusion: Compared with patients without endometriosis, those with endometriosis demonstrate a higher concentration of peripheral NK cells. The percentage of NK cells is greater, primarily in patients with advanced stages of endometriosis involving the rectosigmoid colon. Therefore, it may serve as a diagnostic marker for this type of severe endometriosis, in particular if considered in conjunction with the symptoms. Journal of Minimally Invasive Gynecology (2012) 19, 317-324 (C) 2012 AAGL. All rights reserved.

The Ecological Basis for Biogeographic Classification: an Example in Orchid Bees (Apidae: Euglossini)

Biogeography has been difficult to apply as a methodological approach because organismic biology is incomplete at levels where the process of formulating comparisons and analogies is complex. The study of insect biogeography became necessary because insects possess numerous evolutionary traits and play an important role as pollinators. Among insects, the euglossine bees, or orchid bees, attract interest because the study of their biology allows us to explain important steps in the evolution of social behavior and many other adaptive tradeoffs. We analyzed the distribution of morphological characteristics in Colombian orchid bees from an ecological perspective. The aim of this study was to observe the distribution of these attributes on a regional basis. Data corresponding to Colombian euglossine species were ordered with a correspondence analysis and with subsequent hierarchical clustering. Later, and based on community proprieties, we compared the resulting hierarchical model with the collection localities to seek to identify a biogeographic classification pattern. From this analysis, we derived a model that classifies the territory of Colombia into 11 biogeographic units or natural clusters. Ecological assumptions in concordance with the derived classification levels suggest that species characteristics associated with flight performance, nectar uptake, and social behavior are the factors that served to produce the current geographical structure.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Development of a new galaxy classification technique and its application to the zCOSMOS survey

The purpose of this Thesis is to develop a robust and powerful method to classify galaxies from large surveys, in order to establish and confirm the connections between the principal observational parameters of the galaxies (spectral features, colours, morphological indices), and help unveil the evolution of these parameters from $z \sim 1$ to the local Universe. Within the framework of zCOSMOS-bright survey, and making use of its large database of objects ($\sim 10\,000$ galaxies in the redshift range $0 < z \lesssim 1.2$) and its great reliability in redshift and spectral properties determinations, first we adopt and extend the \emph{classification cube method}, as developed by Mignoli et al. (2009), to exploit the bimodal properties of galaxies (spectral, photometric and morphologic) separately, and then combining together these three subclassifications. We use this classification method as a test for a newly devised statistical classification, based on Principal Component Analysis and Unsupervised Fuzzy Partition clustering method (PCA+UFP), which is able to define the galaxy population exploiting their natural global bimodality, considering simultaneously up to 8 different properties. The PCA+UFP analysis is a very powerful and robust tool to probe the nature and the evolution of galaxies in a survey. It allows to define with less uncertainties the classification of galaxies, adding the flexibility to be adapted to different parameters: being a fuzzy classification it avoids the problems due to a hard classification, such as the classification cube presented in the first part of the article. The PCA+UFP method can be easily applied to different datasets: it does not rely on the nature of the data and for this reason it can be successfully employed with others observables (magnitudes, colours) or derived properties (masses, luminosities, SFRs, etc.). The agreement between the two classification cluster definitions is very high. ``Early'' and ``late'' type galaxies are well defined by the spectral, photometric and morphological properties, both considering them in a separate way and then combining the classifications (classification cube) and treating them as a whole (PCA+UFP cluster analysis). Differences arise in the definition of outliers: the classification cube is much more sensitive to single measurement errors or misclassifications in one property than the PCA+UFP cluster analysis, in which errors are ``averaged out'' during the process. This method allowed us to behold the \emph{downsizing} effect taking place in the PC spaces: the migration between the blue cloud towards the red clump happens at higher redshifts for galaxies of larger mass. The determination of $M_{\mathrm{cross}}$ the transition mass is in significant agreement with others values in literature.

Topologically correct intersection curves of tori and natural quadrics

This thesis provides efficient and robust algorithms for the computation of the intersection curve between a torus and a simple surface (e.g. a plane, a natural quadric or another torus), based on algebraic and numeric methods. The algebraic part includes the classification of the topological type of the intersection curve and the detection of degenerate situations like embedded conic sections and singularities. Moreover, reference points for each connected intersection curve component are determined. The required computations are realised efficiently by solving quartic polynomials at most and exactly by using exact arithmetic. The numeric part includes algorithms for the tracing of each intersection curve component, starting from the previously computed reference points. Using interval arithmetic, accidental incorrectness like jumping between branches or the skipping of parts are prevented. Furthermore, the environments of singularities are correctly treated. Our algorithms are complete in the sense that any kind of input can be handled including degenerate and singular configurations. They are verified, since the results are topologically correct and approximate the real intersection curve up to any arbitrary given error bound. The algorithms are robust, since no human intervention is required and they are efficient in the way that the treatment of algebraic equations of high degree is avoided.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

GIS based Watershed Classification in Lao PDR

Every inclined land surface has a potential for soil and water degradation, the seriousness depends on a multitude of parameters such as slope, soil type, geomorphology, rainfall, land use and natural vegetation cover. In Laos this intensified land use leads to reduced vegetation cover, to increased soil erosion, decreasing yield, and finally is likely to influence the hydrological regime. Against this background the Mekong River Commission (MRC) elaborated a spatial explicit Watershed Classification (WSC) for the Lower Mekong Basin. Based on topographic factors derived from a high-resolution Digital Terrain Model, five watershed classes are calculated, giving indication about the sensitivity to resource degradation by soil erosion. The WSC allows spatial priority setting for watershed management and generally supports informed decision making on reconnaissance level. In the conclusions focus is laid on general considerations when GIS techniques are used for spatial decision support in a development context.

Classification and pharmacological treatment of preschool wheezing: changes since 2008.

Since the publication of the European Respiratory Society Task Force report in 2008, significant new evidence has become available on the classification and management of preschool wheezing disorders. In this report, an international consensus group reviews this new evidence and proposes some modifications to the recommendations made in 2008. Specifically, the consensus group acknowledges that wheeze patterns in young children vary over time and with treatment, rendering the distinction between episodic viral wheeze and multiple-trigger wheeze unclear in many patients. Inhaled corticosteroids remain first-line treatment for multiple-trigger wheeze, but may also be considered in patients with episodic viral wheeze with frequent or severe episodes, or when the clinician suspects that interval symptoms are being under reported. Any controller therapy should be viewed as a treatment trial, with scheduled close follow-up to monitor treatment effect. The group recommends discontinuing treatment if there is no benefit and taking favourable natural history into account when making decisions about long-term therapy. Oral corticosteroids are not indicated in mild-to-moderate acute wheeze episodes and should be reserved for severe exacerbations in hospitalised patients. Future research should focus on better clinical and genetic markers, as well as biomarkers, of disease severity.

Watershed classification in the Lower Mekong Basin: Rapid economic growth - a source of increasing potential for conflict over resource use

In the Lower Mekon Basin the extraordinary pace of economic development and growth contradicts with environmental protection. On base of the Watershed Classification Project (WSCP) and the inclusion of a DTM for the entire LMB the potential degradation risk was derived for each land unit. The risks were grouped into five classes, where classes one and two are considered critical with regard to soil erosion when the land is cleared of natural resources. For practical use the database has an enormous potential for further spatial analysis in combination with other datasets, as for example the NCCR North-South uses the WSCP within two research projects.