David R. C. Hill

Fully 3D Monte Carlo reconstruction in SPECT: a feasibility study

In single photon emission computed tomography (SPECT) with parallel hole collimation, image reconstruction is usually performed as a set of bidimensional (2D) analytical or iterative reconstructions. This approach ignores the tridimensional (3D) nature of scatter and detector response function that affects the detected signal. To deal with the 3D nature of the image formation process, iterative reconstruction can be used by considering a 3D projector modelling the 3D spread of photons. In this paper, we investigate the value of using accurate Monte Carlo simulations to determine the 3D projector used in a fully 3D Monte Carlo (F3DMC) reconstruction approach. Given the 3D projector modelling all physical effects affecting the imaging process, the reconstruction problem is solved using the maximum likelihood expectation maximization (MLEM) algorithm. To validate the concept, three data sets were simulated and F3DMC was compared with two other 3D reconstruction strategies using analytical corrections for attenuation, scatter and camera point spread function. Results suggest that F3DMC improves spatial resolution, relative and absolute quantitation and signal-to-noise ratio. The practical feasibility of the approach on real data sets is discussed.

GoArrays: highly dynamic and efficient microarray probe design

MOTIVATION The use of oligonucleotide microarray technology requires a very detailed attention to the design of specific probes spotted on the solid phase. These problems are far from being commonplace since they refer to complex physicochemical constraints. Whereas there are more and more publicly available programs for microarray oligonucleotide design, most of them use the same algorithm or criteria to design oligos, with only little variation. RESULTS We show that classical approaches used in oligo design software may be inefficient under certain experimental conditions, especially when dealing with complex target mixtures. Indeed, our biological model is a human obligate parasite, the microsporidia Encephalitozoon cuniculi. Targets that are extracted from biological samples are composed of a mixture of pathogen transcripts and host cell transcripts. We propose a new approach to design oligonucleotides which combines good specificity with a potentially high sensitivity. This approach is original in the biological point of view as well as in the algorithmic point of view. We also present an experimental validation of this new strategy by comparing results obtained with standard oligos and with our composite oligos. A specific E.cuniculi microarray will overcome the difficulty to discriminate the parasite mRNAs from the host cell mRNAs demonstrating the power of the microarray approach to elucidate the lifestyle of an intracellular pathogen using mix mRNAs.

Multi-agent simulation of group foraging in sheep: effects of spatial memory, conspecific attraction and plot size

We describe the modelling of sheep spatial memory at pasture using an individual-based approach. As our modelling goal requires specification of stochastic and state-dependent random movements and some social aspects, we used a multi-agent system that can be regarded as a special case of an individual-based model (IBM). We used a three-phase approach to implement the synchronization kernel since this is particularly well adapted to spatial resource competition. One of the main differences between this model and most earlier IBMs is that we were able to use real field data from animal experiments for model validation. We thus compared real system behaviour with model predictions. As the simulation results were consistent with field data, we used the model as an extrapolation tool to investigate conditions that had not been tested, or that are not easily amenable to experimentation. This enabled us to show that conspecific attraction can have disruptive effects on the searching efficiency of foragers in habitats, where patches deplete rapidly. We also show that the advantages of a good spatial memory vary according to the size of the environment to be explored.

PARALLELIZATION OF MONTE CARLO SIMULATIONS AND SUBMISSION TO A GRID ENVIRONMENT

Monte Carlo simulations are increasingly used in medical physics. In scintigraphic imaging these simulations are used to model imaging systems and to develop and assess tomographic reconstruction algorithms and correction methods for improved image quantization. In radiotherapy-brachytherapy the goal is to evaluate accurately the dosimetry in complex phantoms and at interfaces of tissue, where analytic calculations have shown some limits. The main drawback of Monte Carlo simulations is their high computing time. The aim of our research is to reduce the computing time by parallelizing a simulation on geographically distributed processors. The method is based on the parallelization of the Random Number Generator (RNG) used in Monte Carlo simulations. The long serial of numbers used by the sequential simulation is split. Once the partitioning is done, a software application allows the user to generate automatically the files describing each simulation part. Finally, another software executes them on the DataGrid testbed using an API. All these steps have been made transparent for the user by providing a web page asking the user for all the parameters necessary to launch the simulation and retrieve results. Different tests have been done in order to show first, the reliability of the physical results obtained by concatenation of parallelized output data and secondly the time gained for jobs execution.

An algorithmic model for invasive species : Application to Caulerpa taxifolia (Vahl) C. Agardh development in the North-Western Mediterranean Sea

The purpose of the study is to evaluate the propagation of the green alga of tropical origin Caulerpa taxifolia (Vahl) C. Agardh in the north-western Mediterranean sea—introduced in 1984—by means of an algorithmic computer model. In order to take into account spatial interactions and anthropic dispersion or activities such as eradication and

Individual-based modelling of Pinus sylvestris invasion after grazing abandonment in the French Massif Central

In the Chaîne des Puys, a mid-elevation volcanic mountain of the French Massif Central, Scots pine proves to be an ‘invasive’ species colonizing abandoned lawns or heathlands, and forms in a few years monospecific natural forests. Most of the abandonment occurred 30 to 40 years ago and this process has now stopped. Thus, we lack data on the very first phase of tree colonization. We anticipate that a simulation tool could bring an appreciable help in (i) rebuilding the entire colonization process – including the initial phase – of pine settlement and (ii) answering questions about the origin of the narrow and unimodal distributions of age of pine stands we observed. In addition, such a simulator could help managers to forecast extension of Scots pine and to predict growth and evolution of present secondary forests. A spatially explicit individual-based model is presented. The model takes into account both space and time and includes growth of trees, seed production and seed dispersal, death and competition between individuals. The influence of the initial parameters are analyzed and elements of validation given. The model was then used to predict tree settlement and stand establishment using the initial conditions from a natural stand studied in the field whose characteristics before abandonment were known (number and age-distribution of trees, location of mother trees, time of abandonment). Three simulations were achieved by using the same initial conditions but following different scenarios for the recruitment process. The scenario of a fluctuating resistance of the resident vegetation (that controls the susceptibility of the environment to tree establishment) seemed as one of the most probable to explain the actual stand characteristics. We thus concluded that dynamic models could be improved by taking into account the resistance of the vegetation to colonization as a fluctuating parameter instead of a static and permanent attribute.

Modelling and simulation of ecological propagation processes: application to fire spread

An important class of ecological problems concerns propagation processes. In ecological modelling, these phenomena generally occur on large scales and are generally difficult to simulate efficiently because of the number of entities. Studies of this kind of phenomena lack genericity and reusability because they are often presented from the point of view of a single domain expert. Simulations made by domain experts seem to lack genericity for computer science specialists and simulations developed by computer science specialists seem not to grasp the terminology and problems of the domain experts. We propose here a general object-oriented framework for modelling and simulation of propagation processes. Object-oriented techniques help in developing generic and reusable models. From modelling to simulation, the Unified Modelling Language (UML) provides a common means of communication between computer science specialists and domain experts. The Model Driven Architecture (MDA) is used to improve object-oriented methodology. Simulation optimisations are defined for discrete time models of propagation. The approach is applied to the modelling and simulation of fire spread. Starting from wasteland fire problems, specification levels are used to gradually specify a fire spread simulator. Each level of the study is specified in UML and thus can be reused in another wasteland fire problem.

Disentangling coordination among functional traits using an individual-centred model: impact on plant performance at intra- and inter-specific levels.

Background Plant functional traits co-vary along strategy spectra, thereby defining trade-offs for resource acquisition and utilization amongst other processes. A main objective of plant ecology is to quantify the correlations among traits and ask why some of them are sufficiently closely coordinated to form a single axis of functional specialization. However, due to trait co-variations in nature, it is difficult to propose a mechanistic and causal explanation for the origin of trade-offs among traits observed at both intra- and inter-specific level. Methodology/Principal Findings Using the Gemini individual-centered model which coordinates physiological and morphological processes, we investigated with 12 grass species the consequences of deliberately decoupling variation of leaf traits (specific leaf area, leaf lifespan) and plant stature (height and tiller number) on plant growth and phenotypic variability. For all species under both high and low N supplies, simulated trait values maximizing plant growth in monocultures matched observed trait values. Moreover, at the intraspecific level, plastic trait responses to N addition predicted by the model were in close agreement with observed trait responses. In a 4D trait space, our modeling approach highlighted that the unique trait combination maximizing plant growth under a given environmental condition was determined by a coordination of leaf, root and whole plant processes that tended to co-limit the acquisition and use of carbon and of nitrogen. Conclusion/Significance Our study provides a mechanistic explanation for the origin of trade-offs between plant functional traits and further predicts plasticity in plant traits in response to environmental changes. In a multidimensional trait space, regions occupied by current plant species can therefore be viewed as adaptive corridors where trait combinations minimize allometric and physiological constraints from the organ to the whole plant levels. The regions outside this corridor are empty because of inferior plant performance.

HiSpOD: probe design for functional DNA microarrays.

MOTIVATION The use of DNA microarrays allows the monitoring of the extreme microbial diversity encountered in complex samples like environmental ones as well as that of their functional capacities. However, no probe design software currently available is adapted to easily design efficient and explorative probes for functional gene arrays. RESULTS We present a new efficient functional microarray probe design algorithm called HiSpOD (High Specific Oligo Design). This uses individual nucleic sequences or consensus sequences produced by multiple alignments to design highly specific probes. Indeed, to bypass crucial problem of cross-hybridizations, probe specificity is assessed by similarity search against a large formatted database dedicated to microbial communities containing about 10 million coding sequences (CDS). For experimental validation, a microarray targeting genes encoding enzymes involved in chlorinated solvent biodegradation was built. The results obtained from a contaminated environmental sample proved the specificity and the sensitivity of probes designed with the HiSpOD program. AVAILABILITY http://fc.isima.fr/~g2im/hispod/.

Fully 3D Monte Carlo image reconstruction in SPECT using functional regions

Image reconstruction in single photon emission computed tomography is affected by physical effects such as photon attenuation, Compton scatter and detector response. These effects can be compensated for by modeling the corresponding spread of photons in 3D within the system matrix used for tomographic reconstruction. The fully 3D Monte Carlo (F3DMC) reconstruction technique consists in calculating this system matrix using Monte Carlo simulations. The inverse problem of tomographic reconstruction is then solved using conventional iterative algorithms such as maximum likelihood expectation maximization. Although F3DMC has already shown promising results, its use is currently limited by two major issues: huge size of the fully 3D system matrix and long computation time required for calculating a robust and accurate system matrix. To address these two issues, we propose to calculate the F3DMC system matrix using a spatial sampling matching the functional regions to be reconstructed. In this approach, different regions of interest can be reconstructed with different spatial sampling. For instance, a single value is reconstructed for a functional region assumed to contain uniform activity. To assess the value of this approach, Monte Carlo simulations have been performed using GATE. Results suggest that F3DMC reconstruction using functional regions improves quantitative accuracy compared to the F3DMC reconstruction method proposed so far. In addition, it considerably reduces disk space requirement and duration of the simulations needed to estimate the system matrix. The concept of functional regions might therefore make F3DMC reconstruction practically feasible.