Detection of virulence-associated genes in pathogenic and commensal avian Escherichia coli isolates

Poultry colibacillosis due to Avian Pathogenic Escherichia coli (APEC) is responsible for several extra-intestinal pathological conditions, leading to serious economic damage in poultry production. The most commonly associated pathologies are airsacculitis, colisepticemia, and cellulitis in broiler chickens, and salpingitis and peritonitis in broiler breeders. In this work a total of 66 strains isolated from dead broiler breeders affected with colibacillosis and 61 strains from healthy broilers were studied. Strains from broiler breeders were typified with serogroups O2, O18, and O78, which are mainly associated with disease. The serogroup O78 was the most prevalent (58%). All the strains were checked for the presence of 11 virulence genes: 1) arginine succinyltransferase A (astA); ii) E. coli hemeutilization protein A (chuA); iii) colicin V A/B (cvaA/B); iv) fimbriae mannose-binding type 1 (fimC); v) ferric yersiniabactin uptake A (fyuA); vi) iron-repressible high-molecular-weight proteins 2 (irp2); vii) increased serum survival (iss); viii) iron-uptake systems of E. coli D (iucD); ix) pielonefritis associated to pili C (papC); x) temperature sensitive haemaglutinin (tsh), and xi) vacuolating autotransporter toxin (vat), by Multiplex-PCR. The results showed that all genes are present in both commensal and pathogenic E. coli strains. The iron uptake-related genes and the serum survival gene were more prevalent among APEC. The adhesin genes, except tsh, and the toxin genes, except astA, were also more prevalent among APEC isolates. Except for astA and tsh, APEC strains harbored the majority of the virulence-associated genes studied and fimC was the most prevalent gene, detected in 96.97 and 88.52% of APEC and AFEC strains, respectively. Possession of more than one iron transport system seems to play an important role on APEC survival.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality fig-ure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge rep-resentation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases re-trieval one. On the other hand, and aiming at an improvement of the CBR theo-retical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were re-written, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of one`s confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.