Pseudomonas aeruginosa is an opportunistic pathogen and is associated with nosocomial infections. Its ability to thrive in a broad range of environments is due to a large and diverse genome of which its accessory genome is part. The objective of this study was to characterize P. aeruginosa strains isolated from children who developed bacteremia, using pulse-field gel electrophoresis, and in terms of its genomic islands, virulence genes, multilocus sequence type, and antimicrobial susceptibility. Our results showed that P. aeruginosa strains presented the seven virulence genes: toxA, lasB, lecA, algR, plcH, phzA1, and toxR, a type IV pilin alleles (TFP) group I or II. Additionally, we detected a novel pilin and accessory gene, expanding the number of TFP alleles to group VI. All strains presented the PAPI-2 Island and the majority were exoU+ and exoS+ genotype. Ten percent of the strains were multi-drug resistant phenotype, 18% extensively drug-resistant, 68% moderately resistant and only 3% were susceptible to all the antimicrobial tested. The most prevalent acquired β-Lactamase was KPC. We identified a group of ST309 strains, as a potential high risk clone. Our finding also showed that the strains isolated from patients with bacteremia have important virulence factors involved in colonization and dissemination as: a TFP group I or II; the presence of the exoU gene within the PAPI-2 island and the presence of the exoS gene.

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Time-of-flight measurement is a critical step to per-2 form ultrasonic non-destructive testing of standing 3 trees, with direct influence on the precision of de-4 fect detection. Aiming to increase the accuracy on 5 the estimation, the characteristics of the ultrasonic 6 measurement chain should be adapted to the con-7 straints of wood testing in living condition. This 8 study focused on the excitation signal parameters, 9 such as shape, temporal duration, and frequency re-10 sponse, and then the selection of a suitable time-11 of-flight determination technique. A standing plane 12 tree was tested, placing ultrasonic receivers at four 13 different positions, with five different excitation sig-14 nals and three time-of-flight detection methods. The 15 proposed ultrasonic chain of measurement resulted in 16 high signal-to-noise ratios in received signals for all 17 configurations. A time-frequency analysis was used 18 to determine the power distribution in the frequency 19 domain, showing that only chirp signal could concen-20 trate the power around the resonant frequency of the 21 sensor. Threshold and Akaike information criterion 22 method performed similar for impulsive signals with 23 decreasing uncertainty as sensor position approached 24 to the radial direction. Those two methods failed to 25 accurate determine time-of-flight for Gaussian pulse 26 and chirp signals. Cross-correlation was only suitable 27 for the chirp signal, presenting the lower uncertainty 28 values among all configurations.

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& Key message Considering anisotropy in image reconstruction algorithm for ultrasound computed tomography of trees resulted in a more accurate detection of defects compared to common approaches used. & Context Ultrasound computed tomography is a suitable tool for nondestructive evaluation of standing trees. Until now, to simplify the image reconstruction process, the transverse cross-section of trees has been considered as quasi-isotropic and therefore limiting the defect identification capability. & Aims An approach to solve the inverse problem for tree imaging is presented, using an ultrasound-based method (travel-time computed tomography) suited to the anisotropy of wood material and validated experimentally. & Methods The proposed iterative method focused on finding a polynomial approximation of the slowness in each pixel of the image depending on the angle of propagation, modifying the curved trajectories by means of a raytracing method. This method allowed a mapping of specific elastic constants using nonlinear regression. Experimental validation was performed using sections of green wood from a pine tree (Pinus pinea L.), with configurations that include a healthy case, a centered, and an off-centered defect. & Results Images obtained using the proposed method led to a more accurate location of the defects compared to the filtered backprojection algorithm (isotropic hypothesis), considered as reference. & Conclusion The performed experiments demonstrated that considering the wood anisotropy in the imaging process led to a better defect detection compared to the use of a common imaging technique.

To perform a non-destructive evaluation of wood, the Christoffel equation is frequently used to describe the relationship between the ultrasonic wave velocity and the mechanical parameters. In the context of acoustical tomography imaging of standing trees, the key contribution of this numerical study is to determine the influence of mechanical parameters of the wood radial-tangential plane on the wave velocity computation using the Christoffel equation. Mechanical parameters from six species were selected. A sensitivity analysis was carried out by increasing and decreasing every parameter by a given percentage, and then by computing the variation of velocity for a set of wave direction of propagations. The evolution of the wave velocity, according to the direction of propagation, depended on the considered species; there was a difference between the softwoods and the hardwoods. The sensitivity analysis showed a bigger influence of the Young's moduli, followed by the Poisson's ratio, and finally by the shear modulus. However, these last two parameters cannot be neglected when using the Christoffel equation to solve the inverse problem of standing tree tomography. A proposed solution involves determining the propagation paths using the Young's moduli as variables and then inversing the set of equations in accordance with the overall parameters.

In the assessment of standing trees, an acoustic tomographic device is a valuable tool as it permits to acquire data from the inner part of the trees without causing them to fall down unnecessarily. The interpretation of the images produced by these devices is part of the diagnosis process for urban trees management. This paper presents a segmentation methodology to identify defective regions in cross-section tomographic images obtained with an Arbotom® device. Two trunk samples obtained from a Blackwood Acacia tree (Acacia melanoxylon) were tested, simulating defects by drilling holes with known geometry, size and position and using different numbers of sensors. Tomograms from the trunk cross sections were processed to align the propagation velocity data with the corresponding region, either healthy or defective. The segmentation methodology proposed aims to find a velocity threshold value to separate the defective region adjusting a logistic regression model to obtain the value that maximizes a performance criterion, using in this case the geometric mean. Two criteria were used to validate this methodology: the geometric mean and the surface ratio detected. Although an optimal threshold value was found for each experiment, this value was strongly influenced by the defect characteristics and the number of sensors. The correctly segmented area ranging from 54 to 93% demonstrates that the threshold method is not always the most proper way to process this type of images, and thereby further research is required in image processing and analysis. (Résumé d'auteur

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