Species and serovars of enteropathogenic agents associated with acute diarrheal disease in Rosario, Argentina

We report the most frequent species and serovars of enteropathogenic organisms in Rosario from 1985 to 1993. Enteropathogenic Escherichia coli was the most prevalent agent affecting 144/570 (25.2%) children; 0111 represented 41.8%, 055: 13.6%, 0119: 12.7%. Among enterotoxigenic E. coli (ETEC) the most frequent were ETEC-ST 0128:H21 and 0153:H45. Shigella spp were isolated in 8.8%; S.flexneri: 7%, principally type 2 (59.5%); S. sonnei: 1.6%, and S. dysenteriae type 2: 0.2%. Campylobacter spp were found in 6.1% of patients; C.jejuni: 4.6%; C. coli: 1.4% and C. lari: 0.2%; except groups 0 13,50 and 0 4 (2 cases each), no predominant serogroups were found. Salmonella was isolated in 2.8% of cases, being the predominant serovar S. typhimurium until 1986, but a dramatically increase of cases due to S. enteritidis was observed since 1987. There was 1.9% of Aeromonas spp and 2 cases due to Vibrio cholerae non 0-1. No Yersinia was found. In patients with gastroenteritis due to Shigella, Campylobacter, Salmonella, or EPEC as the unique pathogen, leukocytes were observed in the faeces in 70%, 50%, 20%, and 10% of cases respectively.

Aerobic bacteria, Chlamydia trachomatis, Pneumocystis carinii and Cytomegalovirus as agents of severe peneumonia in small infants

The authors studied 58 infants hospitalized for pneumonia in a semi-intensive care unit. Age ranged from 1 complete to 6 incomplete months. The infants were sent from another hospital in 20 cases and from home in a further 38. Pulmonary involvement, which was alveolar in 46 cases and interstitial in 12, was bilateral in 31 children. The investigation was carried out prospectively on the etiological agents associated with respiratory infection to look for evidence of aerobic bacteria (blood cultures), Chlamydia trachomatis and Cytomegalovirus (serology), and Pneumocystis carinii (direct microscopy of tracheal aspirated material). The following infectious agents were diagnosed in 21 children (36.2%): Aerobic bacteria (8), Chlamydia trachomatis (5), Pneumocystis carinii (3), Cytomegalovirus (3), Cytomegalovirus and Chlamydia trachomatis (1), Aerobic bacteria and Cytomegalovirus (1). Seven cases of infection by Chlamydia trachomatis and/or Cytomegalovirus were diagnosed out of the 12 cases with pulmonary interstitial involvement.

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Adaptive Learning in Games: Defining Profiles of Competitor Players

Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.

Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Realistic Multi-Agent Simulation of Competitive Electricity Markets

The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.

PCR in the First Oropharynx Aspirate of the Newborn: A Possible Source for Identification of Congenital Infection Agents

We present a case of prenatal diagnosis of congenital rubella. After birth, in addition to traditional serologic and clinical examinations to confirm the infection, we could identify the virus in the "first fluid aspirated from the oropharynx of the newborn", using polimerase chain reaction (PCR). We propose that this first oropharynx fluid (collected routinely immediately after birth) could be used as a source for identification of various congenital infection agents, which may not always be easily identified by current methods

Streptococcus pneumoniae and Haemophilus influenzae as etiological agents of conjunctivitis outbreaks in the region of Ribeirão Preto, SP, Brazil

In the study of conjunctivitis outbreaks occurring from September 1994 to September 1996 in the region of Ribeirão Preto, conjunctival exudates of 92 patients were cultivated in Instituto Adolfo Lutz Laboratory I, Ribeirão Preto. Most cases occurred in the age range 2-7 years. The etiological agents which were most frequently isolated from the analyzed cases were: Streptococcus pneumoniae and Haemophilus influenzae, in 40.22% and 21.74%, respectively. 51.35% of the S. pneumoniae isolated strains were not typable. The oxacillin-resistant S. pneumoniae strains were submitted to the minimum inhibitory concentration test (MIC) and three of them presented intermediate resistance, whereas only one was highly resistant to penicillin.

Partial chemical characterization of antigenic preparations of chromoblastomycosis agents

Antigenic preparations (saline, methylic, metabolic and exoantigens) of four agents of chromoblastomycosis, Fonsecaea pedrosoi, Phialophora verrucosa, Cladophialophora (Cladosporium) carrionii and Rhinocladiella aquaspersa were obtained. Partial chemical characterization of these antigenic preparations was obtained by determination of the levels of total lipids, protein, and carbohydrates, and identification of the main sterols and carbohydrates. Methylic antigens presented the highest lipid contents, whereas metabolic antigens showed the highest carbohydrate content. Total lipid, protein, and carbohydrate levels were in the range of 2.33 to 2.00mg/ml, 0.04 to 0.02 mg/ml and 0.10 to 0.02 mg/ml, respectively, in the methylic antigens and in the range of 0.53 to 0.18mg/ml, 0.44 to 0.26mg/ml, and 1.82 to 1.02 mg/ml, respectively, in saline antigens. Total lipid, protein, and carbohydrate contents were in the range of 0.55 to 0.20mg/ml, 0.69 to 0.57mg/ml and 10.73 to 5.93mg/ml, respectively, in the metabolic antigens, and in the range of 0.55 to 0.15mg/ml, 0.62 to 0.20mg/ml and 3.55 to 0.42mg/ml, respectively, in the exoantigens. Phospholipids were not detected in the preparations. Saline and metabolic antigens and exoantigens presented hexose and the methylic antigen revealed additional pentose units in their composition. The UV light absorption spectra of the sterols revealed squalene and an ergosterol fraction in the antigens. The characterization of these antigenic preparations may be useful for serological evaluation of patients of chromoblastomycosis.

Evaluation of Opuntia spp. bioactive products as promising natural chemotherapeutical agents- an in vitro approach

Dissertation to obtain a Master Degree in Biotechnology

In vitro susceptibility to antifungal agents of clinical and environmental Cryptococcus neoformans isolated in Southern of Brazil

The purpose of the present study was to compare the susceptibility to four antifungal agents of 69 Cryptococcus neoformans strains isolated from AIDS patients with that of 13 C. neoformans strains isolated from the environment. Based on the NCCLS M27-A methodology the Minimal Inhibitory Concentrations (MICs) obtained for amphotericin B, itraconazole and ketoconazole were very similar for clinical and environmental isolates. Clinical isolates were less susceptible to fluconazole than environmental isolates. The significance of these findings and aspects concerning the importance, role and difficulties of C. neoformans susceptibility testing are also discussed.

Infectious agents in coronary atheromas: a possible role in the pathogenesis of plaque rupture and acute myocardial infarction

In this review we report our recent findings of histopathological features of plaque instability and the association with Mycoplasma pneumoniae (MP) and Chlamydia pneumoniae (CP) infection, studying thrombosed coronary artery segments (CAS) of patients who died due to acute myocardial infarction. Vulnerable plaques are known to be associated with fat atheromas and inflammation of the plaque. Here we demonstrated that vulnerability is also related with focal positive vessel remodeling that maintains relatively well preserved lumen even in the presence of large atheromatous plaques. This phenomena may explain why the cinecoronariography may not detect large and dangerous vulnerable plaques. Greater amount of these bacteria in vulnerable plaques is associated with adventitial inflammation and positive vessel remodeling: the mean numbers of lymphocytes were significantly higher in adventitia than in the plaque, good direct correlation was obtained between numbers of CD20 B cells and numbers of CP infected cells in adventitia, and between % area of MP-DNA in the plaque and cross sectional area of the vessel, suggesting a cause-effect relationship. Mycoplasma is a bacterium that needs cholesterol for proliferation and may increase virulence of other infectious agents. In conclusion, co-infection by Mycoplasma pneumoniae and Chlamydia pneumoniae may represent an important co-factor for plaque instability, leading to coronary plaque thrombosis and acute myocardial infarction, since larger amount of these bacteria strongly correlated with histological signs of more vulnerability of the plaque. The search of CMV and Helicobacter pilori in these tissues resulted negative.