Assessment of the ozone sensitivity of 22 native plant species from mediterranean annual pastures based on visible injury

Ozone (O3) phytototoxicity has been reported on a wide range of plantspecies, inducing the appearance of specific foliar injury or increasing leaf senescence. No information regarding the sensitivity of plantspecies from dehesa Mediterranean grasslands has been provided in spite of their great biological diversity. A screening study was carried out in open-top chambers (OTCs) to assess the O3-sensitivity of 22 representative therophytes of these ecosystems based on the appearance and extent of foliar injury. A distinction was made between specific O3injury and non-specific discolorations. Three O3 treatments (charcoal-filtered air, non-filtered air and non-filtered air supplemented with 40 nl l−1 O3 during 5 days per week) and three OTCs per treatment were used. The Papilionaceae species were more sensitive to O3 than the Poaceae species involved in the experiment since ambient levels induced foliar symptoms in 67% and 27%, respectively, of both plant families. An O3-sensitivity ranking of the species involved in the assessment is provided, which could be useful for bioindication programmes in Mediterranean areas. The assessed Trifoliumspecies were particularly sensitive since foliar symptoms were apparent in association with O3 accumulated exposures well below the current critical level for the prevention of this kind of effect. The exposure indices involving lower cut-off values (i.e. 30 nl l−1) were best related with the extent of O3-induced injury on these species.

Probabilistic Atlas Based Segmentation Using Affine Moment Descriptors and Graph-Cuts

We show a procedure for constructing a probabilistic atlas based on affine moment descriptors. It uses a normalization procedure over the labeled atlas. The proposed linear registration is defined by closed-form expressions involving only geometric moments. This procedure applies both to atlas construction as atlas-based segmentation. We model the likelihood term for each voxel and each label using parametric or nonparametric distributions and the prior term is determined by applying the vote-rule. The probabilistic atlas is built with the variability of our linear registration. We have two segmentation strategy: a) it applies the proposed affine registration to bring the target image into the coordinate frame of the atlas or b) the probabilistic atlas is non-rigidly aligning with the target image, where the probabilistic atlas is previously aligned to the target image with our affine registration. Finally, we adopt a graph cut - Bayesian framework for implementing the atlas-based segmentation.

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

Purpose: A fully three-dimensional (3D) massively parallelizable list-mode ordered-subsets expectation-maximization (LM-OSEM) reconstruction algorithm has been developed for high-resolution PET cameras. System response probabilities are calculated online from a set of parameters derived from Monte Carlo simulations. The shape of a system response for a given line of response (LOR) has been shown to be asymmetrical around the LOR. This work has been focused on the development of efficient region-search techniques to sample the system response probabilities, which are suitable for asymmetric kernel models, including elliptical Gaussian models that allow for high accuracy and high parallelization efficiency. The novel region-search scheme using variable kernel models is applied in the proposed PET reconstruction algorithm. Methods: A novel region-search technique has been used to sample the probability density function in correspondence with a small dynamic subset of the field of view that constitutes the region of response (ROR). The ROR is identified around the LOR by searching for any voxel within a dynamically calculated contour. The contour condition is currently defined as a fixed threshold over the posterior probability, and arbitrary kernel models can be applied using a numerical approach. The processing of the LORs is distributed in batches among the available computing devices, then, individual LORs are processed within different processing units. In this way, both multicore and multiple many-core processing units can be efficiently exploited. Tests have been conducted with probability models that take into account the noncolinearity, positron range, and crystal penetration effects, that produced tubes of response with varying elliptical sections whose axes were a function of the crystal's thickness and angle of incidence of the given LOR. The algorithm treats the probability model as a 3D scalar field defined within a reference system aligned with the ideal LOR. Results: This new technique provides superior image quality in terms of signal-to-noise ratio as compared with the histogram-mode method based on precomputed system matrices available for a commercial small animal scanner. Reconstruction times can be kept low with the use of multicore, many-core architectures, including multiple graphic processing units. Conclusions: A highly parallelizable LM reconstruction method has been proposed based on Monte Carlo simulations and new parallelization techniques aimed at improving the reconstruction speed and the image signal-to-noise of a given OSEM algorithm. The method has been validated using simulated and real phantoms. A special advantage of the new method is the possibility of defining dynamically the cut-off threshold over the calculated probabilities thus allowing for a direct control on the trade-off between speed and quality during the reconstruction.