High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

A mechanism for localized lignin deposition in the endodermis.

The precise localization of extracellular matrix and cell wall components is of critical importance for multicellular organisms. Lignin is a major cell wall modification that often forms intricate subcellular patterns that are central to cellular function. Yet the mechanisms of lignin polymerization and the subcellular precision of its formation remain enigmatic. Here, we show that the Casparian strip, a lignin-based, paracellular diffusion barrier in plants, forms as a precise, median ring by the concerted action of a specific, localized NADPH oxidase, brought into proximity of localized peroxidases through the action of Casparian strip domain proteins (CASPs). Our findings in Arabidopsis provide a simple mechanistic model of how plant cells regulate lignin formation with subcellular precision. We speculate that scaffolding of NADPH oxidases to the downstream targets of the reactive oxygen species (ROS) that they produce might be a widespread mechanism to ensure specificity and subcellular precision of ROS action within the extracellular matrix.

Measurements of glial metabolic fluxes with C-11-acetate using positron emission and an adapted NMR-based metabolic modeling approach

Objectives: Acetate brain metabolism has the particularity to occur specifically in glial cells. Labeling studies, using acetate labeled either with 13C (NMR) or 11C (PET), are governed by the same biochemical reactions and thus follow the same mathematical principles. In this study, the objective was to adapt an NMR acetate brain metabolism model to analyse [1-11C]acetate infusion in rats. Methods: Brain acetate infusion experiments were modeled using a two-compartment model approach used in NMR.1-3 The [1-11C]acetate labeling study was done using a beta scintillator.4 The measured radioactive signal represents the time evolution of the sum of all labeled metabolites in the brain. Using a coincidence counter in parallel, an arterial input curve was measured. The 11C at position C-1 of acetate is metabolized in the first turn of the TCA cycle to the position 5 of glutamate (Figure 1A). Through the neurotransmission process, it is further transported to the position 5 of glutamine and the position 5 of neuronal glutamate. After the second turn of the TCA cycle, tracer from [1-11C]acetate (and also a part from glial [5-11C]glutamate) is transferred to glial [1-11C]glutamate and further to [1-11C]glutamine and neuronal glutamate through the neurotransmission cycle. Brain poster session: oxidative mechanisms S460 Journal of Cerebral Blood Flow & Metabolism (2009) 29, S455-S466 Results: The standard acetate two-pool PET model describes the system by a plasma pool and a tissue pool linked by rate constants. Experimental data are not fully described with only one tissue compartment (Figure 1B). The modified NMR model was fitted successfully to tissue time-activity curves from 6 single animals, by varying the glial mitochondrial fluxes and the neurotransmission flux Vnt. A glial composite rate constant Kgtg=Vgtg/[Ace]plasma was extracted. Considering an average acetate concentration in plasma of 1 mmol/g5 and the negligible additional amount injected, we found an average Vgtg = 0.08±0.02 (n = 6), in agreement with previous NMR measurements.1 The tissue time-activity curve is dominated by glial glutamate and later by glutamine (Figure 1B). Labeling of neuronal pools has a low influence, at least for the 20 mins of beta-probe acquisition. Based on the high diffusivity of CO2 across the blood-brain barrier; 11CO2 is not predominant in the total tissue curve, even if the brain CO2 pool is big compared with other metabolites, due to its strong dilution through unlabeled CO2 from neuronal metabolism and diffusion from plasma. Conclusion: The two-compartment model presented here is also able to fit data of positron emission experiments and to extract specific glial metabolic fluxes. 11C-labeled acetate presents an alternative for faster measurements of glial oxidative metabolism compared to NMR, potentially applicable to human PET imaging. However, to quantify the relative value of the TCA cycle flux compared to the transmitochondrial flux, the chemical sensitivity of NMR is required. PET and NMR are thus complementary.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Debris flow modeling for susceptibility mapping at regional to national scale in Norway

Debris flows and related landslide processes occur in many regions all over Norway and pose a significant hazard to inhabited areas. Within the framework of the development of a national debris flows susceptibility map, we are working on a modeling approach suitable for Norway with a nationwide coverage. The discrimination of source areas is based on an index approach, which includes topographic parameters and hydrological settings. For the runout modeling, we use the Flow-R model (IGAR, University of Lausanne), which is based on combined probabilistic and energetic algorithms for the assessment of the spreading of the flow and maximum runout distances. First results for different test areas have shown that runout distances can be modeled reliably. For the selection of source areas, however, additional factors have to be considered, such as the lithological and quaternary geological setting, in order to accommodate the strong variation in debris flow activity in the different geological, geomorphological and climate regions of Norway.

Photopolymerizable hydrogels for implants: Monte-Carlo modeling and experimental in vitro validation.

Photopolymerization is commonly used in a broad range of bioapplications, such as drug delivery, tissue engineering, and surgical implants, where liquid materials are injected and then hardened by means of illumination to create a solid polymer network. However, photopolymerization using a probe, e.g., needle guiding both the liquid and the curing illumination, has not been thoroughly investigated. We present a Monte Carlo model that takes into account the dynamic absorption and scattering parameters as well as solid-liquid boundaries of the photopolymer to yield the shape and volume of minimally invasively injected, photopolymerized hydrogels. In the first part of the article, our model is validated using a set of well-known poly(ethylene glycol) dimethacrylate hydrogels showing an excellent agreement between simulated and experimental volume-growth-rates. In the second part, in situ experimental results and simulations for photopolymerization in tissue cavities are presented. It was found that a cavity with a volume of 152  mm3 can be photopolymerized from the output of a 0.28-mm2 fiber by adding scattering lipid particles while only a volume of 38  mm3 (25%) was achieved without particles. The proposed model provides a simple and robust method to solve complex photopolymerization problems, where the dimension of the light source is much smaller than the volume of the photopolymerizable hydrogel.

Niche, distribution and global change : modeling insights into biogeography and conservation biology Olivier Broennimann

RESUME Les évidences montrant que les changements globaux affectent la biodiversité s'accumulent. Les facteurs les plus influant dans ce processus sont les changements et destructions d'habitat, l'expansion des espèces envahissantes et l'impact des changements climatiques. Une évaluation pertinente de la réponse des espèces face à ces changements est essentielle pour proposer des mesures permettant de réduire le déclin actuel de la biodiversité. La modélisation de la répartition d'espèces basée sur la niche (NBM) est l'un des rares outils permettant cette évaluation. Néanmoins, leur application dans le contexte des changements globaux repose sur des hypothèses restrictives et demande une interprétation critique. Ce travail présente une série d'études de cas investiguant les possibilités et limitations de cette approche pour prédire l'impact des changements globaux. Deux études traitant des menaces sur les espèces rares et en danger d'extinction sont présentées. Les caractéristiques éco-géographiques de 118 plantes avec un haut degré de priorité de conservation sont revues. La prévalence des types de rareté sont analysées en relation avec leur risque d'extinction UICN. La revue souligne l'importance de la conservation à l'échelle régionale. Une évaluation de la rareté à échelle globale peut être trompeuse pour certaine espèces car elle ne tient pas en compte des différents degrés de rareté que présente une espèce à différentes échelles spatiales. La deuxième étude test une approche pour améliorer l'échantillonnage d'espèces rares en incluant des phases itératives de modélisation et d'échantillonnage sur le terrain. L'application de l'approche en biologie de la conservation (illustrée ici par le cas du chardon bleu, Eryngium alpinum), permettrait de réduire le temps et les coûts d'échantillonnage. Deux études sur l'impact des changements climatiques sur la faune et la flore africaine sont présentées. La première étude évalue la sensibilité de 227 mammifères africains face aux climatiques d'ici 2050. Elle montre qu'un nombre important d'espèces pourrait être bientôt en danger d'extinction et que les parcs nationaux africains (principalement ceux situé en milieux xériques) pourraient ne pas remplir leur mandat de protection de la biodiversité dans le futur. La seconde étude modélise l'aire de répartition en 2050 de 975 espèces de plantes endémiques du sud de l'Afrique. L'étude propose l'inclusion de méthodes améliorant la prédiction des risques liés aux changements climatiques. Elle propose également une méthode pour estimer a priori la sensibilité d'une espèce aux changements climatiques à partir de ses propriétés écologiques et des caractéristiques de son aire de répartition. Trois études illustrent l'utilisation des modèles dans l'étude des invasions biologiques. Une première étude relate l'expansion de la laitue sáuvage (Lactuca serriola) vers le nord de l'Europe en lien avec les changements du climat depuis 250 ans. La deuxième étude analyse le potentiel d'invasion de la centaurée tachetée (Centaures maculosa), une mauvaise herbe importée en Amérique du nord vers 1890. L'étude apporte la preuve qu'une espèce envahissante peut occuper une niche climatique différente après introduction sur un autre continent. Les modèles basés sur l'aire native prédisent de manière incorrecte l'entier de l'aire envahie mais permettent de prévoir les aires d'introductions potentielles. Une méthode alternative, incluant la calibration du modèle à partir des deux aires où l'espèce est présente, est proposée pour améliorer les prédictions de l'invasion en Amérique du nord. Je présente finalement une revue de la littérature sur la dynamique de la niche écologique dans le temps et l'espace. Elle synthétise les récents développements théoriques concernant le conservatisme de la niche et propose des solutions pour améliorer la pertinence des prédictions d'impact des changements climatiques et des invasions biologiques. SUMMARY Evidences are accumulating that biodiversity is facing the effects of global change. The most influential drivers of change in ecosystems are land-use change, alien species invasions and climate change impacts. Accurate projections of species' responses to these changes are needed to propose mitigation measures to slow down the on-going erosion of biodiversity. Niche-based models (NBM) currently represent one of the only tools for such projections. However, their application in the context of global changes relies on restrictive assumptions, calling for cautious interpretations. In this thesis I aim to assess the effectiveness and shortcomings of niche-based models for the study of global change impacts on biodiversity through the investigation of specific, unsolved limitations and suggestion of new approaches. Two studies investigating threats to rare and endangered plants are presented. I review the ecogeographic characteristic of 118 endangered plants with high conservation priority in Switzerland. The prevalence of rarity types among plant species is analyzed in relation to IUCN extinction risks. The review underlines the importance of regional vs. global conservation and shows that a global assessment of rarity might be misleading for some species because it can fail to account for different degrees of rarity at a variety of spatial scales. The second study tests a modeling framework including iterative steps of modeling and field surveys to improve the sampling of rare species. The approach is illustrated with a rare alpine plant, Eryngium alpinum and shows promise for complementing conservation practices and reducing sampling costs. Two studies illustrate the impacts of climate change on African taxa. The first one assesses the sensitivity of 277 mammals at African scale to climate change by 2050 in terms of species richness and turnover. It shows that a substantial number of species could be critically endangered in the future. National parks situated in xeric ecosystems are not expected to meet their mandate of protecting current species diversity in the future. The second study model the distribution in 2050 of 975 endemic plant species in southern Africa. The study proposes the inclusion of new methodological insights improving the accuracy and ecological realism of predictions of global changes studies. It also investigates the possibility to estimate a priori the sensitivity of a species to climate change from the geographical distribution and ecological proprieties of the species. Three studies illustrate the application of NBM in the study of biological invasions. The first one investigates the Northwards expansion of Lactuca serriola L. in Europe during the last 250 years in relation with climate changes. In the last two decades, the species could not track climate change due to non climatic influences. A second study analyses the potential invasion extent of spotted knapweed, a European weed first introduced into North America in the 1890s. The study provides one of the first empirical evidence that an invasive species can occupy climatically distinct niche spaces following its introduction into a new area. Models fail to predict the current full extent of the invasion, but correctly predict areas of introduction. An alternative approach, involving the calibration of models with pooled data from both ranges, is proposed to improve predictions of the extent of invasion on models based solely on the native range. I finally present a review on the dynamic nature of ecological niches in space and time. It synthesizes the recent theoretical developments to the niche conservatism issues and proposes solutions to improve confidence in NBM predictions of the impacts of climate change and species invasions on species distributions.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation.

A critical issue in brain energy metabolism is whether lactate produced within the brain by astrocytes is taken up and metabolized by neurons upon activation. Although there is ample evidence that neurons can efficiently use lactate as an energy substrate, at least in vitro, few experimental data exist to indicate that it is indeed the case in vivo. To address this question, we used a modeling approach to determine which mechanisms are necessary to explain typical brain lactate kinetics observed upon activation. On the basis of a previously validated model that takes into account the compartmentalization of energy metabolism, we developed a mathematical model of brain lactate kinetics, which was applied to published data describing the changes in extracellular lactate levels upon activation. Results show that the initial dip in the extracellular lactate concentration observed at the onset of stimulation can only be satisfactorily explained by a rapid uptake within an intraparenchymal cellular compartment. In contrast, neither blood flow increase, nor extracellular pH variation can be major causes of the lactate initial dip, whereas tissue lactate diffusion only tends to reduce its amplitude. The kinetic properties of monocarboxylate transporter isoforms strongly suggest that neurons represent the most likely compartment for activation-induced lactate uptake and that neuronal lactate utilization occurring early after activation onset is responsible for the initial dip in brain lactate levels observed in both animals and humans.