Aspects related to productivity for four generations of a Lutzomyia longipalpis laboratory colony

A closed colony of Lutzomyia longipalpis was established with specimens collected in the Raposa - Serra do Sol indian reservoir, one of the main foci of visceral leishmaniasis in the State of Roraima, Brazil. Biological observations were made on four generations of a L. longipalpis colony with emphasis on productivity. Aspects studied were the number of laid and retained eggs, and the number of adults (male and female) per generation. During the four generations the percentage of engorged females that laid eggs varied from 64.2% (third generation-F3) to 90.3% (second generation-F2). The mean number of eggs laid per female varied from 23.6 (F3) to 39.9 (first generation-F1). The maximum number of eggs laid per female varied from 84 (F3) to 124 (F1). The mean number of retained eggs per female was 12.7 (parental generation-P and F1) to 22.1 (F2). The number of females exceeded the number of males in all generations. However, significant difference for male/female ratio was found only for F3. Fecundity rates were between 42.1 (F3) and 58.3 (F2). From a total of 439 blood-fed females, 355 females laid 12,257 eggs that yield 5,354 adults (2,525 males and 2,829 females) in four generations. F2 presented maximum productivity and fecundity rates.

Impact of helpers on colony productivity in a primitively eusocial bee

Small societies of totipotent individuals are good systems in which to study the costs and benefits of group living that are central to the origin and maintenance of eusociality. For instance, in eusocial halictid bees, some females remain in their natal nest to help rear the next brood. Why do helpers stay in the nest? Do they really help, and if yes, is their contribution large enough to voluntarily forfeit direct reproduction? Here, we estimate the impact of helpers on colony survival and productivity in the sweat bee Halictus scabiosae. The number of helpers was positively associated with colony survival and productivity. Colonies from which we experimentally removed one helper produced significantly fewer offspring. However, the effect of helper removal was very small, on average. From the removal experiment, we estimated that one helper increased colony productivity by 0.72 additional offspring in colonies with one to three helpers, while the increase was smaller and not statistically significant in larger colonies. We conclude that helpers do actually help in this primitively eusocial bee, particularly in small colonies. However, the resulting increase in colony productivity is low, which suggests that helpers may be constrained in their role or may attempt to reproduce.

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.

Non-nest mate discrimination and clonal colony structure in the parthenogenetic ant Cerapachys biroi

Understanding the interplay between cooperation and conflict in social groups is a major goal of biology. One important factor is genetic relatedness, and animal societies are usually composed of related but genetically different individuals, setting the stage for conflicts over reproductive allocation. Recently, however, it has been found that several ant species reproduce predominantly asexually. Although this can potentially give rise to clonal societies, in the few well-studied cases, colonies are often chimeric assemblies of different genotypes, due to worker drifting or colony fusion. In the ant Cerapachys biroi, queens are absent and all individuals reproduce via thelytokous parthenogenesis, making this species an ideal study system of asexual reproduction and its consequences for social dynamics. Here, we show that colonies in our study population on Okinawa, Japan, recognize and effectively discriminate against foreign workers, especially those from unrelated asexual lineages. In accord with this finding, colonies never contained more than a single asexual lineage and average pairwise genetic relatedness within colonies was extremely high (r = 0.99). This implies that the scope for social conflict in C. biroi is limited, with unusually high potential for cooperation and altruism.

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.

Ant queens (Hymenoptera: Formicidae) are attracted to fungal pathogens during the initial stage of colony founding

Ant queens that attempt to disperse and found new colonies independently face high mortality risks. The exposure of queens to soil entomopathogens during claustral colony founding may be particularly harmful, as founding queens lack the protection conferred by mature colonies. Here, we tested the hypotheses that founding queens (I) detect and avoid nest sites that are contaminated by fungal pathogens, and (II) tend to associate with other queens to benefit from social immunity when nest sites are contaminated. Surprisingly, in nest choice assays, young Formica selysi BONDROIT, 1918 queens had an initial preference for nest sites contaminated by two common soil entomopathogenic fungi, Beauveria bassiana and Metarhizium brunneum. Founding queens showed a similar preference for the related but non-entomopathogenic fungus Fusarium graminearum. In contrast, founding queens had no significant preference for the more distantly related nonentomopathogenic fungus Petromyces alliaceus, nor for heat-killed spores of B. bassiana. Finally, founding queens did not increase the rate of queen association in presence of B. bassiana. The surprising preference of founding queens for nest sites contaminated by live entomopathogenic fungi suggests that parasites manipulate their hosts or that the presence of specific fungi is a cue associated with suitable nesting sites.

UOC Algorithms

Aplicació per a iPad a mode de repositori de continguts relacionats amb l'ensenyament d'assignatures d'informàtica.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

Biology of the first generation of a laboratory colony of Nyssomyia intermedia (Lutz & Neiva, 1912) and Nyssomyia neivai (Pinto, 1926) (Diptera: Psychodidae)

The phlebotomine sand flies Nyssomyia intermedia (Lutz & Neiva, 1912) and Nyssomyia neivai (Pinto, 1926) are very close and may be involved in the transmission of Leishmania spp. Ross, 1903 in Brazil. The biology of the first laboratory-reared generations of these species, descended from insects captured in Além Paraíba (N. intermedia) and Corinto (N. neivai) in the Brazilian state of Minas Gerais, is described here. The captured females were fed on hamsters and maintained individually in rearing pots. Laboratory temperature and relative humidity were maintained at 25-26ºC and 80% respectively. The productivity of the first generation of N. intermedia was greater than that of N. neivai, and its development time clearly shorter, particularly for the second and third larval instars.

Fecundity of ant queens in relation to their age and the mode of colony founding

The change over time in the fecundity and weight of queens was investigated in three monogynous, independent colony founding species,Lasius niger, Camponotus ligniperda andC. herculaneus, and two polygynous dependent colony founding species,Plagiolepis pygmaea andIridomyrmex humilis. Queens of the three species founding independently exhibited a similar pattern with a significant loss of weight between mating and the emergence of the first workers. In contrast, weights of queens of the species employing dependent colony founding remained more stable. Fecundity of queens founding independently increased slowly with time whereas fecundity of queens founding dependently reached the maximum level some weeks after the beginning of the first reproductive season. These results are discussed in relation to some differences in the life history (e.g., life-span) between queens utilizing independent and dependent colony founding.

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption

Enhancing MPLS QoS routing algorithms by using the network protection degree paradigm

IP based networks still do not have the required degree of reliability required by new multimedia services, achieving such reliability will be crucial in the success or failure of the new Internet generation. Most of existing schemes for QoS routing do not take into consideration parameters concerning the quality of the protection, such as packet loss or restoration time. In this paper, we define a new paradigm to develop new protection strategies for building reliable MPLS networks, based on what we have called the network protection degree (NPD). This NPD consists of an a priori evaluation, the failure sensibility degree (FSD), which provides the failure probability and an a posteriori evaluation, the failure impact degree (FID), to determine the impact on the network in case of failure. Having mathematical formulated these components, we point out the most relevant components. Experimental results demonstrate the benefits of the utilization of the NPD, when used to enhance some current QoS routing algorithms to offer a certain degree of protection