A morphological, isoenzymatic and behavioural study of ten populations of Anopheles(Nyssorhynchus) albitarsis Lynch-Arribalzaga, 1878 (Diptera: culicidae) including from the type-locality - Baradero, Argentina

Anopheles (Nyssorhynchus) albitarsis Lynch-Arribalzaga, 1878 shows morphological and behavioural variations which results in it being sometimes considered as a major malaria vector and at other times as playing no important role in epidemiology. With the aim of clarifying the taxonomy of the species, comparative morphological and isoenzymatic studies were made in populations from the type-locality, Baradero, Argentina and from 9 different localities inBrazil. Morphological studies consisted of the observation of eggs in scanning electron microscopy, of complete chaetotaxy of larvae and pupae and of the detailed drawing of male and female adults. Only Guajara-Mirim and Rio Branco populations, described previously as Anopheles deaneorum sp.n., showed morphological differences. Isoenzymes were studied using 4th instar larvae homogenate and agarosegel electrophoresis. Eleven enzymatic loci were analyzed. By calculation of Nei's Genetic Distance (D), the populations could be separated into 5 groups: i)Baradero, ii)Marajo, iii)Boa Vista, iv)Angra, Itaguai and Paraipaba and v)Guajara-Mirim and Rio Branco. These groups belong to 2 major clusters called I and II, separated by D = 0.345. In the I cluster are groups i, ii and iii and in II clusteriv and v. In I, D=0.246 separates i and ii from iii, while i is separated by D =0.181 from ii. In II, D = 0.223 between iv and v. Only the population of group vcould be distinguished morphologically from the others, leading to the description of an independent species An. deaneorum.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Hard instances of algorithms and proof systems

"Vegeu el resum a l'inici del document del fitxer adjunt."

An Investigation on the Ecology of Triatoma vitticeps (Stal, 1859) and its Possible Role in the Transmission of Trypanosoma cruzi, in the Locality of Triunfo, Santa Maria Madalena Municipal District, State of Rio de Janeiro, Brazil

From January 1989 to April 1995, 465 specimens of Triatoma vitticeps were collected in the locality of Triunfo, 2nd District of Santa Maria Madalena municipal district, State of Rio de Janeiro. The bugs were found indoors by local residents with predominance of adults. The flight activity was high in hot months when the incidence in the domicile also increased. Two hundred and two bugs (111 alive and 91 dead) were examined for Trypanosoma cruzi infection. This was detected in 31 of the dead bugs (34%) and 88 (79%) of the live bugs examined. With a view to investigate the possible vertebrate hosts of the T. cruzi isolates, the blood of 122 mammals was examined through Giemsa-stained smears, hemocultures and xenodiagnosis. T. cruzi was detected in three specimens of Didelphis marsupialis and T. (M.) theileri was detected in one specimen of Bos taurus. The parasites were isolated from triatomine feces, xenoculture and hemoculture. No evidence of human infection was detected in 58 inhabitants examined, as evaluated by indirect imunofluorescence technique using T. cruzi epimastigotes as antigens. These results show that T. vitticeps is still a sylvatic species although nymphs have been found inside the domicile. Thus, an epidemiological vigilance is necessary to know the behaviour of this species following the continuous modifications promoted by the presence of man.

Approximation algorithms for two-state anti-ferromagnetic spin systems on bounded degree graphs

In a seminal paper [10], Weitz gave a deterministic fully polynomial approximation scheme for counting exponentially weighted independent sets (which is the same as approximating the partition function of the hard-core model from statistical physics) in graphs of degree at most d, up to the critical activity for the uniqueness of the Gibbs measure on the innite d-regular tree. ore recently Sly [8] (see also [1]) showed that this is optimal in the sense that if here is an FPRAS for the hard-core partition function on graphs of maximum egree d for activities larger than the critical activity on the innite d-regular ree then NP = RP. In this paper we extend Weitz's approach to derive a deterministic fully polynomial approximation scheme for the partition function of general two-state anti-ferromagnetic spin systems on graphs of maximum degree d, up to the corresponding critical point on the d-regular tree. The main ingredient of our result is a proof that for two-state anti-ferromagnetic spin systems on the d-regular tree, weak spatial mixing implies strong spatial mixing. his in turn uses a message-decay argument which extends a similar approach proposed recently for the hard-core model by Restrepo et al [7] to the case of general two-state anti-ferromagnetic spin systems.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

New host and locality records for Tetrameres (Gynaecophila) spirospiculum Pinto & Vicente, 1995 (Nematoda: Tetrameridae), with new morphological data

We report the finding of Tetrameres spirospiculum Pinto & Vicente, 1995 from Theristicus melanopis melanopis (Threskiornithidae) from Patagonia, Argentina. These constitute new host and locality records. We propose the assignation of this species to the subgenus T. (Gynaecophila) Gubanov, 1950, based on the presence of labia and the absence of cuticular flanges at the anterior end. Some new morphological data are provided, such as the arrangement of cuticular spines and the presence of a pair of somatic papillae at beginning of posterior third of body length. T. (G.) spirospiculum may probably be regarded as specific to birds of the genus Theristicus.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Mid Ulster Locality Group

To improve the pharmaceutical care in the community for older people and their carers working towards giving carers support and raising their awareness of the support which is on offer to them.