Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone.

The use of molecular data to reconstruct the history of divergence and gene flow between populations of closely related taxa represents a challenging problem. It has been proposed that the long-standing debate about the geography of speciation can be resolved by comparing the likelihoods of a model of isolation with migration and a model of secondary contact. However, data are commonly only fit to a model of isolation with migration and rarely tested against the secondary contact alternative. Furthermore, most demographic inference methods have neglected variation in introgression rates and assume that the gene flow parameter (Nm) is similar among loci. Here, we show that neglecting this source of variation can give misleading results. We analysed DNA sequences sampled from populations of the marine mussels, Mytilus edulis and M. galloprovincialis, across a well-studied mosaic hybrid zone in Europe and evaluated various scenarios of speciation, with or without variation in introgression rates, using an Approximate Bayesian Computation (ABC) approach. Models with heterogeneous gene flow across loci always outperformed models assuming equal migration rates irrespective of the history of gene flow being considered. By incorporating this heterogeneity, the best-supported scenario was a long period of allopatric isolation during the first three-quarters of the time since divergence followed by secondary contact and introgression during the last quarter. By contrast, constraining migration to be homogeneous failed to discriminate among any of the different models of gene flow tested. Our simulations thus provide statistical support for the secondary contact scenario in the European Mytilus hybrid zone that the standard coalescent approach failed to confirm. Our results demonstrate that genomic variation in introgression rates can have profound impacts on the biological conclusions drawn from inference methods and needs to be incorporated in future studies.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale: some case studies

Every year, debris flows cause huge damage in mountainous areas. Due to population pressure in hazardous zones, the socio-economic impact is much higher than in the past. Therefore, the development of indicative susceptibility hazard maps is of primary importance, particularly in developing countries. However, the complexity of the phenomenon and the variability of local controlling factors limit the use of processbased models for a first assessment. A debris flow model has been developed for regional susceptibility assessments using digital elevation model (DEM) with a GIS-based approach.. The automatic identification of source areas and the estimation of debris flow spreading, based on GIS tools, provide a substantial basis for a preliminary susceptibility assessment at a regional scale. One of the main advantages of this model is its workability. In fact, everything is open to the user, from the data choice to the selection of the algorithms and their parameters. The Flow-R model was tested in three different contexts: two in Switzerland and one in Pakistan, for indicative susceptibility hazard mapping. It was shown that the quality of the DEM is the most important parameter to obtain reliable results for propagation, but also to identify the potential debris flows sources.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by processbased modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws.We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25m resolution.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

PulseCath iVAC 3LTM hemodynamic performance for simple assisted flow.

The PulseCath iVAC 3L? left ventricular assist device is an option to treat transitory left heart failure or dysfunction post-cardiac surgery. Assisted blood flow should reach up to 3 l/min. In the present in vitro model exact pump flow, depending on various frequencies and afterload was examined. Optimal flow was achieved with inflation/deflation frequencies of about 70-80/min. The maximal flow rate was achieved at about 2.5 l/min with a minimal afterload of 22 mmHg. Handling of the device was easy due to the connection to a standard intra-aortic balloon pump console. With increasing afterload (up to a simulated mean systemic pressure of 66 mmHg) flow rate and cardiac support are in some extent limited.

Quantification of aortic flow by phase-contrast magnetic resonance in patients with bicuspid aortic valve.

AIMS: Bicuspid aortic valve (BAV) causes complex flow patterns in the ascending aorta (AAo), which may compromise the accuracy of flow measurement by phase-contrast magnetic resonance (PC-MR). Therefore, we aimed to assess and compare the accuracy of forward flow measurement in the AAo, where complex flow is more dominant in BAV patients, with flow quantification in the left ventricular outflow tract (LVOT) and the aortic valve orifice (AV), where complex flow is less important, in BAV patients and controls. METHODS AND RESULTS: Flow was measured by PC-MR in 22 BAV patients and 20 controls at the following positions: (i) LVOT, (ii) AV, and (iii) AAo, and compared with the left ventricular stroke volume (LVSV). The correlation between the LVSV and the forward flow in the LVOT, the AV, and the AAo was good in BAV patients (r = 0.97/0.96/0.93; P < 0.01) and controls (r = 0.96/0.93/0.93; P < 0.01). However, in relation with the LVSV, the forward flow in the AAo was mildly underestimated in controls and much more in BAV patients [median (inter-quartile range): 9% (4%/15%) vs. 22% (8%/30%); P < 0.01]. This was not the case in the LVOT and the AV. The severity of flow underestimation in the AAo was associated with flow eccentricity. CONCLUSION: Flow measurement in the AAo leads to an underestimation of the forward flow in BAV patients. Measurement in the LVOT or the AV, where complex flow is less prominent, is an alternative means for quantifying the systolic forward flow in BAV patients.

Atrial natriuretic peptides: reproducibility of renal effects and response of liver blood flow.

To assess the variability of the response to exogenous atrial natriuretic peptide (ANP), it was infused at the rate of 1 microgram/min for 2 h in 6 salt-loaded normal volunteers under controlled conditions on 2 occasions at an interval of 1 week. The effect on solute excretion and the haemodynamic and endocrine actions were highly reproducible. The constant ANP infusion caused a delayed and prolonged excretion of sodium, chloride and calcium, no change in potassium or phosphate excretion or in glomerular filtration rate but a marked decrease in renal plasma flow. Blood pressure, heart rate and the plasma levels of angiotensin II, aldosterone, arginine vasopressin and plasma renin activity were unaltered. The effect of a 2-h infusion of ANP 0.5 microgram/min or its vehicle on apparent hepatic blood flow (HBF) was also studied in 14 normal volunteers by measuring the indocyanine green clearance. A 21% decrease in HBF was observed in subjects who received the ANP infusion (p less than 0.01 vs vehicle). Thus, ANP infused at a dose that did not lower blood pressure decreased both renal and liver blood flow in normotensive volunteers. The renal and endocrine responses to ANP were reproducible over a 1-week interval.

Gene flow among wild and domesticated almond species: insights from chloroplast and nuclear markers

Hybridization has played a central role in the evolutionary history of domesticated plants. Notably, several breeding programs relying on gene introgression from the wild compartment have been performed in fruit tree species within the genus Prunus but few studies investigated spontaneous gene flow among wild and domesticated Prunus species. Consequently, a comprehensive understanding of genetic relationships and levels of gene flow between domesticated and wild Prunus species is needed. Combining nuclear and chloroplastic microsatellites, we investigated the gene flow and hybridization among two key almond tree species, the cultivated Prunus dulcis and one of the most widespread wild relative Prunus orientalis in the Fertile Crescent. We detected high genetic diversity levels in both species along with substantial and symmetric gene flow between the domesticated P. dulcis and the wild P. orientalis. These results were discussed in light of the cultivated species diversity, by outlining the frequent spontaneous genetic contributions of wild species to the domesticated compartment. In addition, crop-to-wild gene flow suggests that ad hoc transgene containment strategies would be required if genetically modified cultivars were introduced in the northwestern Mediterranean.

A control flow prototype for a dose recommending device for chronic myeloid leukemia patients

In this paper we present a prototype of a control flow for an a posteriori drug dose adaptation for Chronic Myelogenous Leukemia (CML) patients. The control flow is modeled using Timed Automata extended with Tasks (TAT) model. The feedback loop of the control flow includes the decision-making process for drug dose adaptation. This is based on the outputs of the body response model represented by the Support Vector Machine (SVM) algorithm for drug concentration prediction. The decision is further checked for conformity with the dose level rules of a medical guideline. We also have developed an automatic code synthesizer for the icycom platform as an extension of the TIMES tool.