Amplified Fragment Length Polymorphism Analysis of Campylobacter jejuni Strains Isolated from Chickens and from Patients with Gastroenteritis or Guillain-Barré or Miller Fisher Syndrome

The high-resolution genotyping method of amplified fragment length polymorphism (AFLP) analysis was used to study the genetic relationships between Campylobacter jejuni strains infecting chickens (n = 54) and those causing gastroenteritis in humans (n = 53). In addition, C. jejuni strains associated with the development of Guillain-Barré syndrome (GBS) (n = 14) and Miller Fisher syndrome (MFS) (n = 4), two related acute paralytic syndromes in human, were included. Strains were isolated between 1989 and 1998 in The Netherlands. The AFLP banding patterns were analyzed with correlation-based and band-based similarity coefficients and UPGMA (unweighted pair group method using average linkages) cluster analysis. All C. jejuni strains showed highly heterogeneous fingerprints, and no fingerprints exclusive for chicken strains or for human strains were obtained. All strains were separated in two distinct genetic groups. In group A the percentage of human strains was significantly higher and may be an indication that genotypes of this group are more frequently associated with human diseases. We conclude that C. jejuni from chickens cannot be distinguished from human strains and that GBS or MFS related strains do not belong to a distinct genetic group

peace: pulsar evaluation algorithm for candidate extraction – a software package for post-analysis processing of pulsar survey candidates

Modern radio pulsar surveys produce a large volume of prospective candidates, the majority of which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determine if they are real pulsars. This process can be labor intensive. In this paper, we introduce an algorithm called PEACE (Pulsar Evaluation Algorithm for Candidate Extraction) which improves the efficiency of identifying pulsar signals. The algorithm ranks the candidates based on a score function. Unlike popular machine-learning based algorithms, no prior training data sets are required. This algorithm has been applied to data from several large-scale radio pulsar surveys. Using the human-based ranking results generated by students in the Arecibo Remote Command enter programme, the statistical performance of PEACE was evaluated. It was found that PEACE ranked 68% of the student-identified pulsars within the top 0.17% of sorted candidates, 95% within the top 0.34%, and 100% within the top 3.7%. This clearly demonstrates that PEACE significantly increases the pulsar identification rate by a factor of about 50 to 1000. To date, PEACE has been directly responsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars that may be useful for pulsar timing based gravitational-wave detection projects.Comment: 7 pages, 4 figures, accepted by MNRA