Wolbachia infections of phlebotomine sand flies (Diptera: Psychodidae)

Old and New World phlebotomine sand fly species were screened for infection with Wolbachia, intracellular bacterial endosymbionts found in many arthropods and filarial nematodes. Of 53 samples representing 15 species, nine samples of four species were found positive for Wolbachia by polymerase chain reaction amplification using primers for the Wolbachia surface protein (wsp) gene. Five of the wsp gene fragments from four species were cloned, sequenced, and used for phylogenetic analysis. These wsp sequences were placed in three different clades within the arthropod associated Wolbachia (groups A and B), suggesting that Wolbachia has infected sand flies on more than one occasion. Two distantly related sand fly species, Lutzomyia (Psanthyromyia) shannoni (Dyar) and Lutzomyia (Nyssomyia) whitmani (Antunes & Coutinho), infected with an identical Wolbachia strain suggest a very recent horizontal transmission.

Ultrastructure and small-subunit ribosomal DNA sequence of Henneguya lesteri n. sp (Myxosporea), a parasite of sand whiting Sillago analis (Sillaginidae) from the coast of Queensland, Australia

Henneguya lesteri n. sp, (Myxosporea) is described from sand whiting, Sillago analis, from the southern Queensland coast of Australia. H. lesteri displays a preference for the pseudobranchs and is typically positioned along the afferent blood vessels, displacing the adjoining lamellae and disrupting their normal array, The plasmodia appeared as whitish-hyaline, elliptical cysts (mean dimensions 230 x 410 mum) attached to the oral mucosa lining of the hyoid arch on the inner surface of the operculum. Infections of the gills were also found, in which the plasmodia were spherical, averaged 240 x 240 mum in size and were located on the inner hemibranch margin. The parasites lodged in the gill filament crypts and generated a mild hyperplastic response of the branchial epithelium, In histological sections, the plasmodium wall and adjoining ectoplasm appeared as a finely granulated, weakly eosinophilic layer, Ultrastructurally, this section of the host-parasite interface contained an intricate complex of pinocytotic channels. H. lesteri is polysporic, disporoblastic and pansporoblast forming. Sporogenesis is asynchronous, with the earliest developmental stages aligned predominantly along the plasmodium periphery, and maturing sporoblasts and spores toward the center. Ultrastructural details of sporoblast and spore development are in agreement with previously described myxosporeans. The mature spore is drop-shaped, length (mean) 9.1 mum, width 4.7 mum, thickness 2.5 mum, and comprises 2 polar capsules positioned closely together, a binucleated sporoplasm and a caudal process of 12.6 mum. The polar capsules are elongated, 3.2 x 1.6 mum, with 4 turns of the polar filament. Mean length of the everted filament is 23.2 mum, Few studies have analyzed the 18S gene-of marine Myxosporea. In fact, H. lesteri is the first marine species of Henneguya to be characterized at the molecular level: we determined 1966 bp of the small-subunit (18S) rDNA, The results indicated that differences between this and the hitherto studied freshwater Henneguya species are greater than differences among the freshwater Henneguya species.

Influence of hysteresis on tidal capillary fringe dynamics in a well-sorted sand

Nielsen and Perrochet [Adv. Water Resour. 23 (2000) 503] presented experimental data for cyclic water movement in the vadose zone above an oscillating watertable. The response of the watertable to cyclic forcing was characterised by the ratios of the forcing head to watertable amplitudes and their associated phase lag. They found that their non-hysteretic Richards' equation model failed to represent the observed behaviour of these parameters. This paper explores the effect on the simulated capillary fringe dynamics (in terms of these parameters) of including varying degrees of hysteresis in the moisture retention curve used in a numerical model of their experiment. It is clear that hysteresis can indeed account for observed discrepancies between simulation and experiment and that the effect of hysteresis varies with the frequency of oscillation. The use of a single-valued mean retention curve, as advocated by some authors, fails to provide a match between the simulated and observed behaviour of the Nielsen and Perrochet parameters, but is shown to be adequate for predicting time-averaged soil moisture profiles. (C) 2003 Elsevier Ltd. All rights reserved.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.