Deformable templates for tracking and analysis of intravascular ultrasound sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

Endophytic colonization of barley (Hordeum vulgare) roots by the nematophagous fungus Pochonia chlamydosporia reveals plant growth promotion and a general defense and stress transcriptomic response

Plant crop yields are negatively conditioned by a large set of biotic and abiotic factors. An alternative to mitigate these adverse effects is the use of fungal biological control agents and endophytes. The egg-parasitic fungus Pochonia chlamydosporia has been traditionally studied because of its potential as a biological control agent of plant-parasitic nematodes. This fungus can also act as an endophyte in monocot and dicot plants, and has been shown to promote plant growth in different agronomic crops. An Affymetrix 22K Barley GeneChip was used in this work to analyze the barley root transcriptomic response to P. chlamydosporia root colonization. Functional gene ontology (GO) and gene set enrichment analyses showed that genes involved in stress response were enriched in the barley transcriptome under endophytism. An 87.5 % of the probesets identified within the abiotic stress response group encoded heat shock proteins. Additionally, we found in our transcriptomic analysis an up-regulation of genes implicated in the biosynthesis of plant hormones, such as auxin, ethylene and jasmonic acid. Along with these, we detected induction of brassinosteroid insensitive 1-associated receptor kinase 1 (BR1) and other genes related to effector-triggered immunity (ETI) and pattern-triggered immunity (PTI). Our study supports at the molecular level the growth-promoting effect observed in plants endophytically colonized by P. chlamydosporia, which opens the door to further studies addressing the capacity of this fungus to mitigate the negative effects of biotic and abiotic factors on plant crops.

Hydrogenated graphene nanoribbons for spintronics

We show how hydrogenation of graphene nanoribbons at small concentrations can open venues toward carbon-based spintronics applications regardless of any specific edge termination or passivation of the nanoribbons. Density-functional theory calculations show that an adsorbed H atom induces a spin density on the surrounding π orbitals whose symmetry and degree of localization depends on the distance to the edges of the nanoribbon. As expected for graphene-based systems, these induced magnetic moments interact ferromagnetically or antiferromagnetically depending on the relative adsorption graphene sublattice, but the magnitude of the interactions are found to strongly vary with the position of the H atoms relative to the edges. We also calculate, with the help of the Hubbard model, the transport properties of hydrogenated armchair semiconducting graphene nanoribbons in the diluted regime and show how the exchange coupling between H atoms can be exploited in the design of novel magnetoresistive devices.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.