A survey of bacterial diversity in ticks, lice and fleas from Australia

We isolated bacteria from ticks, lice and fleas. Partial small subunit rRNA sequences were obtained for each isolate and the closest matches in the FastA database were determined. These bacteria were mostly Gram-positive (Firmicutes), although representatives from the Proteobacteria (alpha, beta, gamma subdivisions) and CFB group were also isolated. Most of the isolates we found were from genera that were present in most of the ectoparasites studied, but a few genera were restricted to one species of ectoparasite. The most commonly isolated genera were Stenotrophomonas, Staphylococcus, Pseudomonas, Acinetobacter and Bacillus. Species of Bacillus and Proteus, which have biopesticide potential, were found in some of these ectoparasites. Overall, the communities of bacteria were similar to those found in other studies of parasitic arthropods.

Direct electrochemistry of a bacterial sulfite dehydrogenase

Sulfite dehydrogenase from Starkeya novella is an alphabeta heterodimer comprising a 40.6 kDa subunit (containing the Mo cofactor) and a smaller 8.8 kDa heme c subunit. The enzyme catalyses the oxidation of sulfite to sulfate with the natural electron acceptor being cytochrome c(550). Its catalytic mechanism is thought to resemble that found in eukaryotic sulfite oxiclases. Using protein film voltammetry and redox potentiometry, we have identified both Mo- and heme-centered redox responses from the enzyme immobilized on a pyrolytic graphite working electrode: E-m,E-8 (Fe-III/II) +177 mV; E-m,E-8 (Mo-VI/V) +211 mV and E(m,)8 (Mo-V/IV) -118 mV vs NHE; Upon addition of sulfite to the electrochemical cell a steady-state voltammogram is observed and an apparent Michaelis constant (K-m) of 26(l) muM was determined for the enzyme immobilized on the working electrode surface, which is comparable with the value obtained from solution assays.

Intramolecular electron transfer in a bacterial sulfite dehydrogenase

Sulfite dehydrogenase (SDH) from Starkeya novella, a sulfite-oxidizing molybdenum-containing enzyme, has a novel tightly bound αβ-heterodimeric structure in which the Mo cofactor and the c-type heme are located on different subunits. Flash photolysis studies of intramolecular electron transfer (IET) in SDH show that the process is first-order, independent of solution viscosity, and not inhibited by sulfate, which strongly indicates that IET in SDH proceeds directly through the protein medium and does not involve substantial movement of the two subunits relative to each other. The IET results for SDH contrast with those for chicken and human sulfite oxidase (SO) in which the molybdenum domain is linked to a b-type heme domain through a flexible loop, and IET shows a remarkable dependence on sulfate concentration and viscosity that has been ascribed to interdomain docking. The results for SDH provide additional support for the interdomain docking hypothesis in animal SO and clearly demonstrate that dependence of IET on viscosity and sulfate is not an inherent property of all sulfite-oxidizing molybdenum enzymes.

A reduced load approximation accounting for link interactions in a loss network

This paper is concerned with evaluating the performance of loss networks. Accurate determination of loss network performance can assist in the design and dimen- sioning of telecommunications networks. However, exact determination can be difficult and generally cannot be done in reasonable time. For these reasons there is much interest in developing fast and accurate approximations. We develop a reduced load approximation that improves on the famous Erlang fixed point approximation (EFPA) in a variety of circumstances. We illustrate our results with reference to a range of networks for which the EFPA may be expected to perform badly.

Epidemiology of bacterial meningitis among children in Brazil, 1997-1998

OBJECTIVE: To document the incidence and the descriptive epidemiology of bacterial meningitis among individuals under age 20 in a geographically defined region in Brazil during the two-year period immediately preceding the introduction of Haemophilus influenzae type b (Hib) vaccines into the national immunization program of Brazil. METHODS: Population-based epidemiological study of all cases of bacterial meningitis reported among residents of Campinas, Brazil, under age 20 (n=316,570) during the period of 1997-98, using comprehensive surveillance records compiled by the Campinas Health Department from cases reported among hospital inpatients, outpatients, emergency room visits, death certificates, and autopsy reports. RESULTS: The incidence of bacterial meningitis (n=274) was 334.9, 115 and 43.5 cases/10(5) person-years (pys) for residents of Campinas under age 1, 5 and 20, respectively. All cases were hospitalized, with an average length of stay of 12 days. Documented prior antibiotic use was 4.0%. The case-fatality rate of bacterial meningitis in individuals under age 20 was 9% (24/274) with 75% of deaths occurring in children under the age of five. The incidence of Hib meningitis (n=26) was 62.8 and 17 cases/10(5) pys in children age <1 and <5, respectively. CONCLUSIONS: The incidence of Hib meningitis in children under the age of 5 in Campinas during 1997-98 was similar to that reported in the US, Western Europe, and Israel prior to widespread Hib vaccine use in those regions. This study provides a baseline for later studies to evaluate changes in the etiology and incidence of bacterial meningitis in children after introduction of routine Hib vaccination in Brazil.

A new cloning system based on the OprI lipoprotein for the production of recombinant bacterial cell wall-derived immunogenic formulations

The conjugation of antigens with ligands of pattern recognition receptors (PRR) is emerging as a promising strategy for the modulation of specific immunity. Here, we describe a new Escherichia coli system for the cloning and expression of heterologous antigens in fusion with the OprI lipoprotein, a TLR ligand from the Pseudomonas aeruginosa outer membrane (OM). Analysis of the OprI expressed by this system reveals a triacylated lipid moiety mainly composed by palmitic acid residues. By offering a tight regulation of expression and allowing for antigen purification by metal affinity chromatography, the new system circumvents the major drawbacks of former versions. In addition, the anchoring of OprI to the OM of the host cell is further explored for the production of novel recombinant bacterial cell wall-derived formulations (OM fragments and OM vesicles) with distinct potential for PRR activation. As an example, the African swine fever virus ORF A104R was cloned and the recombinant antigen was obtained in the three formulations. Overall, our results validate a new system suitable for the production of immunogenic formulations that can be used for the development of experimental vaccines and for studies on the modulation of acquired immunity.

Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using Subsampling

A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Solubility of Ethene in Water and in a Medium for the Cultivation of a Bacterial Strain

The solubility of ethene in water and in the fermentation medium of Xanthobacter Py(2) was determined with a Ben-Naim-Baer type apparatus. The solubility measurements were carried out in the temperature range of (293.15 to 323.15) K and at atmospheric pressure with a precision of about +/- 0.3 %. The Ostwald coefficients, the mole fractions of the dissolved ethene, at the gas partial pressure of 101.325 kPa, and the Henry coefficients, at the water vapor pressure, were calculated using accurate thermodynamic relations. A comparison between the solubility of ethene in water and in the cultivation medium has shown that this gas is about 2.4 % more soluble in pure water. On the other hand, from the solubility temperature dependence, the Gibbs energy, enthalpy, and entropy changes for the process of transferring the solute from the gaseous phase to the liquid solutions were also determined. Moreover, the perturbed-chain statistical associating fluid theory equation of state (PC-SAFT EOS) model was used for the prediction of the solubility of ethene in water. New parameters, k(ij), are proposed for this system, and it was found that using a ky temperature-dependent PC-SAFT EOS describes more accurately the behavior solubilities of ethene in water at 101.325 kPa, improving the deviations to 1 %.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

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.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Intelligent SCADA for load control

A supervisory control and data acquisition (SCADA) system is an integrated platform that incorporates several components and it has been applied in the field of power systems and several engineering applications to monitor, operate and control a lot of processes. In the future electrical networks, SCADA systems are essential for an intelligent management of resources like distributed generation and demand response, implemented in the smart grid context. This paper presents a SCADA system for a typical residential house. The application is implemented on MOVICON™11 software. The main objective is to manage the residential consumption, reducing or curtailing loads to keep the power consumption in or below a specified setpoint, imposed by the costumer and the generation availability.