Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Combining digital elevation model analysis and run-out modeling to characterize hazard posed by a potentially unstable rock slope at Turtle Mountain, Alberta, Canada

Turtle Mountain in Alberta, Canada has become an important field laboratory for testing different techniques related to the characterization and monitoring of large slope mass movements as the stability of large portions of the eastern face of the mountain is still questionable. In order to better quantify the volumes potentially unstable and the most probable failure mechanisms and potential consequences, structural analysis and runout modeling were preformed. The structural features of the eastern face were investigated using a high resolution digital elevation model (HRDEM). According to displacement datasets and structural observations, potential failure mechanisms affecting different portions of the mountain have been assessed. The volumes of the different potentially unstable blocks have been calculated using the Sloping Local Base Level (SLBL) method. Based on the volume estimation, two and three dimensional dynamic runout analyses have been performed. Calibration of this analysis is based on the experience from the adjacent Frank Slide and other similar rock avalanches. The results will be used to improve the contingency plans within the hazard area.

Modeling morphogen gradient formation from arbitrary realistically shaped sources.

Much of the analytical modeling of morphogen profiles is based on simplistic scenarios, where the source is abstracted to be point-like and fixed in time, and where only the steady state solution of the morphogen gradient in one dimension is considered. Here we develop a general formalism allowing to model diffusive gradient formation from an arbitrary source. This mathematical framework, based on the Green's function method, applies to various diffusion problems. In this paper, we illustrate our theory with the explicit example of the Bicoid gradient establishment in Drosophila embryos. The gradient formation arises by protein translation from a mRNA distribution followed by morphogen diffusion with linear degradation. We investigate quantitatively the influence of spatial extension and time evolution of the source on the morphogen profile. For different biologically meaningful cases, we obtain explicit analytical expressions for both the steady state and time-dependent 1D problems. We show that extended sources, whether of finite size or normally distributed, give rise to more realistic gradients compared to a single point-source at the origin. Furthermore, the steady state solutions are fully compatible with a decreasing exponential behavior of the profile. We also consider the case of a dynamic source (e.g. bicoid mRNA diffusion) for which a protein profile similar to the ones obtained from static sources can be achieved.

A connectome-based comparison of diffusion MRI schemes.

Diffusion MRI has evolved towards an important clinical diagnostic and research tool. Though clinical routine is using mainly diffusion weighted and tensor imaging approaches, Q-ball imaging and diffusion spectrum imaging techniques have become more widely available. They are frequently used in research-oriented investigations in particular those aiming at measuring brain network connectivity. In this work, we aim at assessing the dependency of connectivity measurements on various diffusion encoding schemes in combination with appropriate data modeling. We process and compare the structural connection matrices computed from several diffusion encoding schemes, including diffusion tensor imaging, q-ball imaging and high angular resolution schemes, such as diffusion spectrum imaging with a publically available processing pipeline for data reconstruction, tracking and visualization of diffusion MR imaging. The results indicate that the high angular resolution schemes maximize the number of obtained connections when applying identical processing strategies to the different diffusion schemes. Compared to the conventional diffusion tensor imaging, the added connectivity is mainly found for pathways in the 50-100mm range, corresponding to neighboring association fibers and long-range associative, striatal and commissural fiber pathways. The analysis of the major associative fiber tracts of the brain reveals striking differences between the applied diffusion schemes. More complex data modeling techniques (beyond tensor model) are recommended 1) if the tracts of interest run through large fiber crossings such as the centrum semi-ovale, or 2) if non-dominant fiber populations, e.g. the neighboring association fibers are the subject of investigation. An important finding of the study is that since the ground truth sensitivity and specificity is not known, the comparability between results arising from different strategies in data reconstruction and/or tracking becomes implausible to understand.

Implicit methods for qualitative modeling of gene regulatory networks.

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

How a haemosporidian parasite of bats gets around: the genetic structure of a parasite, vector and host compared.

Parasite population structure is often thought to be largely shaped by that of its host. In the case of a parasite with a complex life cycle, two host species, each with their own patterns of demography and migration, spread the parasite. However, the population structure of the parasite is predicted to resemble only that of the most vagile host species. In this study, we tested this prediction in the context of a vector-transmitted parasite. We sampled the haemosporidian parasite Polychromophilus melanipherus across its European range, together with its bat fly vector Nycteribia schmidlii and its host, the bent-winged bat Miniopterus schreibersii. Based on microsatellite analyses, the wingless vector, and not the bat host, was identified as the least structured population and should therefore be considered the most vagile host. Genetic distance matrices were compared for all three species based on a mitochondrial DNA fragment. Both host and vector populations followed an isolation-by-distance pattern across the Mediterranean, but not the parasite. Mantel tests found no correlation between the parasite and either the host or vector populations. We therefore found no support for our hypothesis; the parasite population structure matched neither vector nor host. Instead, we propose a model where the parasite's gene flow is represented by the added effects of host and vector dispersal patterns.

The Hydrophobic interaction: modeling hydrophobic interactions and aggregation of non-polar particles in aqueous solutions

Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. L'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Récemment, l?eau liquide a été décrite comme une structure formée d?un réseau aléatoire de liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure à basse température, certaines liaisons hydrogènes sont détruites ce qui est énergétiquement défavorable. Les molécules d?eau s?arrangent alors autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l?eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise les liaisons hydrogènes. Maintenant, la dissolution des particules devient énergétiquement défavorable, et les particules se séparent de l?eau en formant des agrégats qui minimisent leur surface exposée à l?eau. Pourtant, à très haute température, les effets entropiques deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d?eau. En utilisant un modèle basé sur ces changements de structure formée par des liaisons hydrogènes j?ai pu reproduire les phénomènes principaux liés à l?hydrophobicité. J?ai trouvé une région de coexistence de deux phases entre les températures critiques inférieure et supérieure de solubilité, dans laquelle les particules hydrophobes s?agrègent. En dehors de cette région, les particules sont dissoutes dans l?eau. J?ai démontré que l?interaction hydrophobe est décrite par un modèle qui prend uniquement en compte les changements de structure de l?eau liquide en présence d?une particule hydrophobe, plutôt que les interactions directes entre les particules. Encouragée par ces résultats prometteurs, j?ai étudié des solutions aqueuses de particules hydrophobes en présence de co-solvants cosmotropiques et chaotropiques. Ce sont des substances qui stabilisent ou déstabilisent les agrégats de particules hydrophobes. La présence de ces substances peut être incluse dans le modèle en décrivant leur effet sur la structure de l?eau. J?ai pu reproduire la concentration élevée de co-solvants chaotropiques dans le voisinage immédiat de la particule, et l?effet inverse dans le cas de co-solvants cosmotropiques. Ce changement de concentration du co-solvant à proximité de particules hydrophobes est la cause principale de son effet sur la solubilité des particules hydrophobes. J?ai démontré que le modèle adapté prédit correctement les effets implicites des co-solvants sur les interactions de plusieurs corps entre les particules hydrophobes. En outre, j?ai étendu le modèle à la description de particules amphiphiles comme des lipides. J?ai trouvé la formation de différents types de micelles en fonction de la distribution des regions hydrophobes à la surface des particules. L?hydrophobicité reste également un sujet controversé en science des protéines. J?ai défini une nouvelle échelle d?hydrophobicité pour les acides aminés qui forment des protéines, basée sur leurs surfaces exposées à l?eau dans des protéines natives. Cette échelle permet une comparaison meilleure entre les expériences et les résultats théoriques. Ainsi, le modèle développé dans mon travail contribue à mieux comprendre les solutions aqueuses de particules hydrophobes. Je pense que les résultats analytiques et numériques obtenus éclaircissent en partie les processus physiques qui sont à la base de l?interaction hydrophobe.<br/><br/>Despite the importance of water in our daily lives, some of its properties remain unexplained. Indeed, the interactions of water with organic particles are investigated in research groups all over the world, but controversy still surrounds many aspects of their description. In my work I have tried to understand these interactions on a molecular level using both analytical and numerical methods. Recent investigations describe liquid water as random network formed by hydrogen bonds. The insertion of a hydrophobic particle at low temperature breaks some of the hydrogen bonds, which is energetically unfavorable. The water molecules, however, rearrange in a cage-like structure around the solute particle. Even stronger hydrogen bonds are formed between water molecules, and thus the solute particles are soluble. At higher temperatures, this strict ordering is disrupted by thermal movements, and the solution of particles becomes unfavorable. They minimize their exposed surface to water by aggregating. At even higher temperatures, entropy effects become dominant and water and solute particles mix again. Using a model based on these changes in water structure I have reproduced the essential phenomena connected to hydrophobicity. These include an upper and a lower critical solution temperature, which define temperature and density ranges in which aggregation occurs. Outside of this region the solute particles are soluble in water. Because I was able to demonstrate that the simple mixture model contains implicitly many-body interactions between the solute molecules, I feel that the study contributes to an important advance in the qualitative understanding of the hydrophobic effect. I have also studied the aggregation of hydrophobic particles in aqueous solutions in the presence of cosolvents. Here I have demonstrated that the important features of the destabilizing effect of chaotropic cosolvents on hydrophobic aggregates may be described within the same two-state model, with adaptations to focus on the ability of such substances to alter the structure of water. The relevant phenomena include a significant enhancement of the solubility of non-polar solute particles and preferential binding of chaotropic substances to solute molecules. In a similar fashion, I have analyzed the stabilizing effect of kosmotropic cosolvents in these solutions. Including the ability of kosmotropic substances to enhance the structure of liquid water, leads to reduced solubility, larger aggregation regime and the preferential exclusion of the cosolvent from the hydration shell of hydrophobic solute particles. I have further adapted the MLG model to include the solvation of amphiphilic solute particles in water, by allowing different distributions of hydrophobic regions at the molecular surface, I have found aggregation of the amphiphiles, and formation of various types of micelle as a function of the hydrophobicity pattern. I have demonstrated that certain features of micelle formation may be reproduced by the adapted model to describe alterations of water structure near different surface regions of the dissolved amphiphiles. Hydrophobicity remains a controversial quantity also in protein science. Based on the surface exposure of the 20 amino-acids in native proteins I have defined the a new hydrophobicity scale, which may lead to an improvement in the comparison of experimental data with the results from theoretical HP models. Overall, I have shown that the primary features of the hydrophobic interaction in aqueous solutions may be captured within a model which focuses on alterations in water structure around non-polar solute particles. The results obtained within this model may illuminate the processes underlying the hydrophobic interaction.<br/><br/>La vie sur notre planète a commencé dans l'eau et ne pourrait pas exister en son absence : les cellules des animaux et des plantes contiennent jusqu'à 95% d'eau. Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. En particulier, l'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Bien que l?eau soit généralement un bon solvant, un grand groupe de molécules, appelées molécules hydrophobes (du grecque "hydro"="eau" et "phobia"="peur"), n'est pas facilement soluble dans l'eau. Ces particules hydrophobes essayent d'éviter le contact avec l'eau, et forment donc un agrégat pour minimiser leur surface exposée à l'eau. Cette force entre les particules est appelée interaction hydrophobe, et les mécanismes physiques qui conduisent à ces interactions ne sont pas bien compris à l'heure actuelle. Dans mon étude j'ai décrit l'effet des particules hydrophobes sur l'eau liquide. L'objectif était d'éclaircir le mécanisme de l'interaction hydrophobe qui est fondamentale pour la formation des membranes et le fonctionnement des processus biologiques dans notre corps. Récemment, l'eau liquide a été décrite comme un réseau aléatoire formé par des liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure, certaines liaisons hydrogènes sont détruites tandis que les molécules d'eau s'arrangent autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l'eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise la structure de cage autour des particules hydrophobes. Maintenant, la dissolution des particules devient défavorable, et les particules se séparent de l'eau en formant deux phases. A très haute température, les mouvements thermiques dans le système deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d'eau. A l'aide d'un modèle qui décrit le système en termes de restructuration dans l'eau liquide, j'ai réussi à reproduire les phénomènes physiques liés à l?hydrophobicité. J'ai démontré que les interactions hydrophobes entre plusieurs particules peuvent être exprimées dans un modèle qui prend uniquement en compte les liaisons hydrogènes entre les molécules d'eau. Encouragée par ces résultats prometteurs, j'ai inclus dans mon modèle des substances fréquemment utilisées pour stabiliser ou déstabiliser des solutions aqueuses de particules hydrophobes. J'ai réussi à reproduire les effets dûs à la présence de ces substances. De plus, j'ai pu décrire la formation de micelles par des particules amphiphiles comme des lipides dont la surface est partiellement hydrophobe et partiellement hydrophile ("hydro-phile"="aime l'eau"), ainsi que le repliement des protéines dû à l'hydrophobicité, qui garantit le fonctionnement correct des processus biologiques de notre corps. Dans mes études futures je poursuivrai l'étude des solutions aqueuses de différentes particules en utilisant les techniques acquises pendant mon travail de thèse, et en essayant de comprendre les propriétés physiques du liquide le plus important pour notre vie : l'eau.

Elevated hypertension risk for African-origin populations in biracial societies : Modeling the Epidemiologic Transition Study.

OBJECTIVES: Blood pressures in persons of African descent exceed those of other racial/ethnic groups in the United States. Whether this trait is attributable to the genetic factors in African-origin populations, or a result of inadequately measured environmental exposures, such as racial discrimination, is not known. To study this question, we conducted a multisite comparative study of communities in the African diaspora, drawn from metropolitan Chicago, Kingston, Jamaica, rural Ghana, Cape Town, South Africa, and the Seychelles. METHODS: At each site, 500 participants between the age of 25 and 49 years, with approximately equal sex balance, were enrolled for a longitudinal study of energy expenditure and weight gain. In this study, we describe the patterns of blood pressure and hypertension observed at baseline among the sites. RESULTS: Mean SBP and DBP were very similar in the United States and South Africa in both men and women, although among women, the prevalence of hypertension was higher in the United States (24 vs. 17%, respectively). After adjustment for multiple covariates, relative to participants in the United States, SBP was significantly higher among the South Africans by 9.7 mmHg (P < 0.05) and significantly lower for each of the other sites: for example, Jamaica: -7.9 mmHg (P = 0.06), Ghana: -12.8 mmHg (P < 0.01) and Seychelles: -11.1 mmHg (P = 0.01). CONCLUSION: These data are consistent with prior findings of a blood pressure gradient in societies of the African diaspora and confirm that African-origin populations with lower social status in multiracial societies, such as the United States and South Africa, experience more hypertension than anticipated based on anthropometric and measurable socioeconomic risk factors.

Multigenic lentiviral vectors for combined and tissue-specific expression of miRNA- and protein-based antiangiogenic factors.

Lentivirus-based gene delivery vectors carrying multiple gene cassettes are powerful tools in gene transfer studies and gene therapy, allowing coexpression of multiple therapeutic factors and, if desired, fluorescent reporters. Current strategies to express transgenes and microRNA (miRNA) clusters from a single vector have certain limitations that affect transgene expression levels and/or vector titers. In this study, we describe a novel vector design that facilitates combined expression of therapeutic RNA- and protein-based antiangiogenic factors as well as a fluorescent reporter from back-to-back RNApolII-driven expression cassettes. This configuration allows effective production of intron-embedded miRNAs that are released upon transduction of target cells. Exploiting such multigenic lentiviral vectors, we demonstrate robust miRNA-directed downregulation of vascular endothelial growth factor (VEGF) expression, leading to reduced angiogenesis, and parallel impairment of angiogenic pathways by codelivering the gene encoding pigment epithelium-derived factor (PEDF). Notably, subretinal injections of lentiviral vectors reveal efficient retinal pigment epithelium-specific gene expression driven by the VMD2 promoter, verifying that multigenic lentiviral vectors can be produced with high titers sufficient for in vivo applications. Altogether, our results suggest the potential applicability of combined miRNA- and protein-encoding lentiviral vectors in antiangiogenic gene therapy, including new combination therapies for amelioration of age-related macular degeneration.

Business model design: an evaluation of paper-based and computer-aided canvases

In recent years, Business Model Canvas design has evolved from being a paper-based activity to one that involves the use of dedicated computer-aided business model design tools. We propose a set of guidelines to help design more coherent business models. When combined with functionalities offered by CAD tools, they show great potential to improve business model design as an ongoing activity. However, in order to create complex solutions, it is necessary to compare basic business model design tasks, using a CAD system over its paper-based counterpart. To this end, we carried out an experiment to measure user perceptions of both solutions. Performance was evaluated by applying our guidelines to both solutions and then carrying out a comparison of business model designs. Although CAD did not outperform paper-based design, the results are very encouraging for the future of computer-aided business model design.

Quantifying the time for accurate EEG decoding of single value-based decisions.

BACKGROUND: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. NEW METHOD: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. RESULTS: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500ms for 'easy' vs. ∼700ms for 'hard' decisions, well before subjects' response (∼340ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. COMPARISON WITH EXISTING METHOD(S): We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. CONCLUSIONS: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

A statistical shape model of the human second cervical vertebra.

PURPOSE: Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. METHODS: The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. RESULTS: The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. CONCLUSION: The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Signs of a vector's adaptive choice: on the evasion of infectious hosts and parasite-induced mortality

Laboratory and field experiments have demonstrated in many cases that malaria vectors do not feed randomly, but show important preferences either for infected or non-infected hosts. These preferences are likely in part shaped by the costs imposed by the parasites on both their vertebrate and dipteran hosts. However, the effect of changes in vector behaviour on actual parasite transmission remains a debated issue. We used the natural associations between a malaria-like parasite Polychromophilus murinus, the bat fly Nycteribia kolenatii and a vertebrate host the Daubenton's bat Myotis daubentonii to test the vector's feeding preference based on the host's infection status using two different approaches: 1) controlled behavioural assays in the laboratory where bat flies could choose between a pair of hosts; 2) natural bat fly abundance data from wild-caught bats, serving as an approximation of realised feeding preference of the bat flies. Hosts with the fewest infectious stages of the parasite were most attractive to the bat flies that did switch in the behavioural assay. In line with the hypothesis of costs imposed by parasites on their vectors, bat flies carrying parasites had higher mortality. However, in wild populations, bat flies were found feeding more based on the bat's body condition, rather than its infection level. Though the absolute frequency of host switches performed by the bat flies during the assays was low, in the context of potential parasite transmission they were extremely high. The decreased survival of infected bat flies suggests that the preference for less infected hosts is an adaptive trait. Nonetheless, other ecological processes ultimately determine the vector's biting rate and thus transmission. Inherent vector preferences therefore play only a marginal role in parasite transmission in the field. The ecological processes rather than preferences per se need to be identified for successful epidemiological predictions.

Association of car ownership and physical activity across the spectrum of human development : Modeling the Epidemiologic Transition Study (METS).

BACKGROUND: Variations in physical activity (PA) across nations may be driven by socioeconomic position. As national incomes increase, car ownership becomes within reach of more individuals. This report characterizes associations between car ownership and PA in African-origin populations across 5 sites at different levels of economic development and with different transportation infrastructures: US, Seychelles, Jamaica, South Africa, and Ghana. METHODS: Twenty-five hundred adults, ages 25-45, were enrolled in the study. A total of 2,101 subjects had valid accelerometer-based PA measures (reported as average daily duration of moderate to vigorous PA, MVPA) and complete socioeconomic information. Our primary exposure of interest was whether the household owned a car. We adjusted for socioeconomic position using household income and ownership of common goods. RESULTS: Overall, PA levels did not vary largely between sites, with highest levels in South Africa, lowest in the US. Across all sites, greater PA was consistently associated with male gender, fewer years of education, manual occupations, lower income, and owning fewer material goods. We found heterogeneity across sites in car ownership: after adjustment for confounders, car owners in the US had 24.3 fewer minutes of MVPA compared to non-car owners in the US (20.7 vs. 45.1 minutes/day of MVPA); in the non-US sites, car-owners had an average of 9.7 fewer minutes of MVPA than non-car owners (24.9 vs. 34.6 minutes/day of MVPA). CONCLUSIONS: PA levels are similar across all study sites except Jamaica, despite very different levels of socioeconomic development. Not owning a car in the US is associated with especially high levels of MVPA. As car ownership becomes prevalent in the developing world, strategies to promote alternative forms of active transit may become important.