A stratified approach for modeling the distribution of a threatened ant species in the Swiss National Park

We present models predicting the potential distribution of a threatened ant species, Formica exsecta Nyl., in the Swiss National Park ( SNP). Data to fit the models have been collected according to a random-stratified design with an equal number of replicates per stratum. The basic aim of such a sampling strategy is to allow the formal testing of biological hypotheses about those factors most likely to account for the distribution of the modeled species. The stratifying factors used in this study were: vegetation, slope angle and slope aspect, the latter two being used as surrogates of solar radiation, considered one of the basic requirements of F. exsecta. Results show that, although the basic stratifying predictors account for more than 50% of the deviance, the incorporation of additional non-spatially explicit predictors into the model, as measured in the field, allows for an increased model performance (up to nearly 75%). However, this was not corroborated by permutation tests. Implementation on a national scale was made for one model only, due to the difficulty of obtaining similar predictors on this scale. The resulting map on the national scale suggests that the species might once have had a broader distribution in Switzerland. Reasons for its particular abundance within the SNP might possibly be related to habitat fragmentation and vegetation transformation outside the SNP boundaries.

Modeling the biophysical impacts of global change in mountain biosphere reserves

Mountains and mountain societies provide a wide range of goods and services to humanity, but they are particularly sensitive to the effects of global environmental change. Thus, the definition of appropriate management regimes that maintain the multiple functions of mountain regions in a time of greatly changing climatic, economic, and societal drivers constitutes a significant challenge. Management decisions must be based on a sound understanding of the future dynamics of these systems. The present article reviews the elements required for an integrated effort to project the impacts of global change on mountain regions, and recommends tools that can be used at 3 scientific levels (essential, improved, and optimum). The proposed strategy is evaluated with respect to UNESCO's network of Mountain Biosphere Reserves (MBRs), with the intention of implementing it in other mountain regions as well. First, methods for generating scenarios of key drivers of global change are reviewed, including land use/land cover and climate change. This is followed by a brief review of the models available for projecting the impacts of these scenarios on (1) cryospheric systems, (2) ecosystem structure and diversity, and (3) ecosystem functions such as carbon and water relations. Finally, the cross-cutting role of remote sensing techniques is evaluated with respect to both monitoring and modeling efforts. We conclude that a broad range of techniques is available for both scenario generation and impact assessments, many of which can be implemented without much capacity building across many or even most MBRs. However, to foster implementation of the proposed strategy, further efforts are required to establish partnerships between scientists and resource managers in mountain areas.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Sex differences: From mental rotation to object location memory, an ERP study

Sex differences in cognition have been largely investigated. The most consistent sex differences favoring females are observed in object location memory involving the left hemisphere whereas the most consistent sex differences favoring males are observed in tasks that require mental rotation involving the right hemisphere. Here we used a task involving these two abilities to see the impact of mental rotation on object location memory. To that end we used a combination of behavioral and event-related potential (ERP) electroencephalography (EEG) measures.A computer screen displayed a square frame of 4 pairs of images (a "teddy" bear, a shoe, an umbrella and a lamp) randomly arranged around a central fixation cross. After a 10-second interval for memorization, images disappeared and were replaced by a test frame with no image but a random pair of two locations marked in black. In addition, this test frame was randomly displayed either in the original orientation (0° rotation) or in the rotated one (90° clockwise - CW - or 90° counterclockwise - CCW). Preceding the test frame, an arrow indicating the presence or the absence of rotation of the frame was displayed on the screen. The task of the participants (15 females and 15 males) was to determine if two marked locations corresponded or not to a pair of identical images. Each response was followed by feedback.Findings showed no significant sex differences in the performance of the original orientation. In comparison with this position, the rotation of the frame produced an equal decrease of male and female performance. In addition, this decrease was significantly higher when the rotation of the frame was in a CCW direction. We further assessed the ERP when the arrow indicated the direction of rotation as stimulus-onset, during four time windows representing major components C1, P1, N1 and N2. Although no sex differences were observed in performance, brain activities differed according to sex. Enhanced amplitudes were found for the CCW compared to CW rotation over the right posterior areas for the P1, N1 and N2 components for men as well as for women. Major topographical differences related to sex were measured for the CW rotation condition as marked lateralized amplitude: left-hemisphere amplitude larger than right one was measured during P1 time range for men. These similar patterns prolonged from P1 to N1 for women. Early distinctions were found in interaction with sex between CCW and CW waveform amplitudes, expressing over anterior electrode sites during C1 time range (0-50 ms post-stimulus).In conclusion (i) women do not outperform men in object location memory in this study (absence of rotation condition); (ii) mental rotation, in particular the direction of rotation, influences performance on object location memory; (iii) CCW rotation is associated with activity in the right parietal hemisphere whereas the CW rotation involves the left parietal hemisphere; (iv) this last effect is less pronounced in males, which could explain why greater involvement of right parietal areas in men and of bilateral posterior areas in women is generally reported in mental rotation tasks; and (v) the early distinctions between both directions of rotation located over anterior sites could be related to sex differences in their respective involvement of control mechanisms.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

The challenge of modeling nuclear receptor regulatory networks in mammalian cells.

Nuclear receptors are a major component of signal transduction in animals. They mediate the regulatory activities of many hormones, nutrients and metabolites on the homeostasis and physiology of cells and tissues. It is of high interest to model the corresponding regulatory networks. While molecular and cell biology studies of individual promoters have provided important mechanistic insight, a more complex picture is emerging from genome-wide studies. The regulatory circuitry of nuclear receptor regulated gene expression networks, and their response to cellular signaling, appear highly dynamic, and involve long as well as short range chromatin interactions. We review how progress in understanding the kinetics and regulation of cofactor recruitment, and the development of new genomic methods, provide opportunities but also a major challenge for modeling nuclear receptor mediated regulatory networks.

Aerodynamic drag modeling of alpine skiers performing giant slalom turns.

PURPOSE: Aerodynamic drag plays an important role in performance for athletes practicing sports that involve high-velocity motions. In giant slalom, the skier is continuously changing his/her body posture, and this affects the energy dissipated in aerodynamic drag. It is therefore important to quantify this energy to understand the dynamic behavior of the skier. The aims of this study were to model the aerodynamic drag of alpine skiers in giant slalom simulated conditions and to apply these models in a field experiment to estimate energy dissipated through aerodynamic drag. METHODS: The aerodynamic characteristics of 15 recreational male and female skiers were measured in a wind tunnel while holding nine different skiing-specific postures. The drag and the frontal area were recorded simultaneously for each posture. Four generalized and two individualized models of the drag coefficient were built, using different sets of parameters. These models were subsequently applied in a field study designed to compare the aerodynamic energy losses between a dynamic and a compact skiing technique. RESULTS: The generalized models estimated aerodynamic drag with an accuracy of between 11.00% and 14.28%, and the individualized models estimated aerodynamic drag with an accuracy between 4.52% and 5.30%. The individualized model used for the field study showed that using a dynamic technique led to 10% more aerodynamic drag energy loss than using a compact technique. DISCUSSION: The individualized models were capable of discriminating different techniques performed by advanced skiers and seemed more accurate than the generalized models. The models presented here offer a simple yet accurate method to estimate the aerodynamic drag acting upon alpine skiers while rapidly moving through the range of positions typical to turning technique.

Stoneley wave modeling in heterogeneous porous media with viscous pore fluids

We implemented Biot-type porous wave equations in a pseudo-spectral numerical modeling algorithm for the simulation of Stoneley waves in porous media. Fourier and Chebyshev methods are used to compute the spatial derivatives along the horizontal and vertical directions, respectively. To prevent from overly short time steps due to the small grid spacing at the top and bottom of the model as a consequence of the Chebyshev operator, the mesh is stretched in the vertical direction. As a large benefit, the Chebyshev operator allows for an explicit treatment of interfaces. Boundary conditions can be implemented with a characteristics approach. The characteristic variables are evaluated at zero viscosity. We use this approach to model seismic wave propagation at the interface between a fluid and a porous medium. Each medium is represented by a different mesh and the two meshes are connected through the above described characteristics domain-decomposition method. We show an experiment for sealed pore boundary conditions, where we first compare the numerical solution to an analytical solution. We then show the influence of heterogeneity and viscosity of the pore fluid on the propagation of the Stoneley wave and surface waves in general.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Sex differences: From mental rotation to object location memory, an ERP study

Sex differences in cognition have been largely investigated. The most consistent sex differences favoring females are observed in object location memory involving the left hemisphere whereas the most consistent sex differences favoring males are observed in tasks that require mental rotation involving the right hemisphere. Here we used a task involving these two abilities to see the impact of mental rotation on object location memory. To that end we used a combination of behavioral and event-related potential (ERP) electroencephalography (EEG) measures.A computer screen displayed a square frame of 4 pairs of images (a "teddy" bear, a shoe, an umbrella and a lamp) randomly arranged around a central fixation cross. After a 10-second interval for memorization, images disappeared and were replaced by a test frame with no image but a random pair of two locations marked in black. In addition, this test frame was randomly displayed either in the original orientation (0° rotation) or in the rotated one (90° clockwise - CW - or 90° counterclockwise - CCW). Preceding the test frame, an arrow indicating the presence or the absence of rotation of the frame was displayed on the screen. The task of the participants (15 females and 15 males) was to determine if two marked locations corresponded or not to a pair of identical images. Each response was followed by feedback.Findings showed no significant sex differences in the performance of the original orientation. In comparison with this position, the rotation of the frame produced an equal decrease of male and female performance. In addition, this decrease was significantly higher when the rotation of the frame was in a CCW direction. We further assessed the ERP when the arrow indicated the direction of rotation as stimulus-onset, during four time windows representing major components C1, P1, N1 and N2. Although no sex differences were observed in performance, brain activities differed according to sex. Enhanced amplitudes were found for the CCW compared to CW rotation over the right posterior areas for the P1, N1 and N2 components for men as well as for women. Major topographical differences related to sex were measured for the CW rotation condition as marked lateralized amplitude: left-hemisphere amplitude larger than right one was measured during P1 time range for men. These similar patterns prolonged from P1 to N1 for women. Early distinctions were found in interaction with sex between CCW and CW waveform amplitudes, expressing over anterior electrode sites during C1 time range (0-50 ms post-stimulus).In conclusion (i) women do not outperform men in object location memory in this study (absence of rotation condition); (ii) mental rotation, in particular the direction of rotation, influences performance on object location memory; (iii) CCW rotation is associated with activity in the right parietal hemisphere whereas the CW rotation involves the left parietal hemisphere; (iv) this last effect is less pronounced in males, which could explain why greater involvement of right parietal areas in men and of bilateral posterior areas in women is generally reported in mental rotation tasks; and (v) the early distinctions between both directions of rotation located over anterior sites could be related to sex differences in their respective involvement of control mechanisms.

Modeling of the TCR-MHC-peptide complex.

The methodology for generating a homology model of the T1 TCR-PbCS-K(d) class I major histocompatibility complex (MHC) class I complex is presented. The resulting model provides a qualitative explanation of the effect of over 50 different mutations in the region of the complementarity determining region (CDR) loops of the T cell receptor (TCR), the peptide and the MHC's alpha(1)/alpha(2) helices. The peptide is modified by an azido benzoic acid photoreactive group, which is part of the epitope recognized by the TCR. The construction of the model makes use of closely related homologs (the A6 TCR-Tax-HLA A2 complex, the 2C TCR, the 14.3.d TCR Vbeta chain, the 1934.4 TCR Valpha chain, and the H-2 K(b)-ovalbumine peptide), ab initio sampling of CDR loops conformations and experimental data to select from the set of possibilities. The model shows a complex arrangement of the CDR3alpha, CDR1beta, CDR2beta and CDR3beta loops that leads to the highly specific recognition of the photoreactive group. The protocol can be applied systematically to a series of related sequences, permitting the analysis at the structural level of the large TCR repertoire specific for a given peptide-MHC complex.

Making sense of AMPA receptor trafficking by modeling molecular mechanisms of synaptic plasticity.

Synaptic plasticity involves a complex molecular machinery with various protein interactions but it is not yet clear how its components give rise to the different aspects of synaptic plasticity. Here we ask whether it is possible to mathematically model synaptic plasticity by making use of known substances only. We present a model of a multistable biochemical reaction system and use it to simulate the plasticity of synaptic transmission in long-term potentiation (LTP) or long-term depression (LTD) after repeated excitation of the synapse. According to our model, we can distinguish between two phases: first, a "viscosity" phase after the first excitation, the effects of which like the activation of NMDA receptors and CaMKII fade out in the absence of further excitations. Second, a "plasticity" phase actuated by an identical subsequent excitation that follows after a short time interval and causes the temporarily altered concentrations of AMPA subunits in the postsynaptic membrane to be stabilized. We show that positive feedback is the crucial element in the core chemical reaction, i.e. the activation of the short-tail AMPA subunit by NEM-sensitive factor, which allows generating multiple stable equilibria. Three stable equilibria are related to LTP, LTD and a third unfixed state called ACTIVE. Our mathematical approach shows that modeling synaptic multistability is possible by making use of known substances like NMDA and AMPA receptors, NEM-sensitive factor, glutamate, CaMKII and brain-derived neurotrophic factor. Furthermore, we could show that the heteromeric combination of short- and long-tail AMPA receptor subunits fulfills the function of a memory tag.

Modeling the forensic two-trace problem with Bayesian networks

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375-381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727-732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this problem.