Characterisation of probiotic strains for the control and prevention of enteropathogens in the food chain

60 strains (belonging to the genera Lactobacillus, Bifidobacterium, Leuconostoc and Enterococcus) were tested for their capacity to inhibit the growth of 3 strains of Campylobacter jejuni: Lactobacilli and bifidobacteria were left to grow in MRS or TPY broth at 37°C overnight in anaerobic conditions; Campylobacter jejuni was inoculated in blood agar plates at 37°C for 24-48 hours in microaerophilic conditions. The inhibition experiments were carried out in vitro using ”Spot agar test” and “Well diffusion assay” techniques testing both cellular activity and that of the surnatant. 11 strains proved to inhibit the growth of Campylobacter jejuni. These strains were subsequently analised analised in order to evaluate the resistance to particular situations of stress which are found in the gastrointestinal tract and during the industrial transformation processes (Starvation stress, osmotic stress, heat stress, resistance to pH and to bile salts). Resistance to starvation stress: all strains seemed to resist the stress (except one strain). Resistance to osmotic stress: all strains were relatively resistant to the concentrations of 6% w/v of NaCl (except one strain). Resistance to heat stress: only one strain showed little resistance to the 55°C temperature. Resistance to pH: In the presence of a low pH (2.5), many strains rapidly lost their viability after approximately 1 hour. Resistance to bile salts: Except for one strain, all strains seemed to be relatively resistant to the 2% w/v concentration of bile salts. Afterward, strains were identified by using phenotipic and molecular techniques. Phenotipic identification was carried out by using API 50 CHL (bioMérieux) and API 20 STREP identification system (bioMérieux); molecular identification with species-specific PCR: the molecular techniques confirmed the results by phenotipic identification. For testing the antibiotic resistance profile, bacterial strains were subcultured in MRS or TPY broth and incubated for 18 h at 37°C under anaerobic conditions. Antibiotics tested (Tetracycline, Trimethoprim, Cefuroxime, Kanamycin, Chloramphenicol, Vancomycin, Ampycillin, Sterptomycin, Erythromycin) were diluted to the final concentrations of: 2,4,8,16,32,64,128,256 mg/ml. Then, 20 μl fresh bacterial culture (final concentration in the plates approximately 106 cfu/ml) were added to 160 μl MRS or TPY broth and 20 μl antibiotic solution. As positive control the bacterial culture (20 ul) was added to broth (160 ul) and water (20 ul). Test was performed on plates P96, that after the inoculum were incubated for 24 h at 37oC, then the antibiotic resistance was determined by measuring the Optical Density (OD) at 620 nm with Multiscan EX. All strains showed a similar behaviour: resistance to all antibiotic tested. Further studies are needed.

Spatial and temporal characterisation of ground deformation recorded by geodetic techniques

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow the different sources that generate the observed displacements to be discerned and characterised. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows reducing the dimensionality of the data space maintaining most of the variance of the dataset explained. Anyway, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. The Independent Component Analysis (ICA) is a popular technique adopted to approach this problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, I use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here I present the application of the vbICA technique to GPS position time series. First, I use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise) and a volcanic source, and I study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, I apply vbICA to different tectonically active scenarios, such as the 2009 L'Aquila (central Italy) earthquake, the 2012 Emilia (northern Italy) seismic sequence, and the 2006 Guerrero (Mexico) Slow Slip Event (SSE).