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.

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.

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.

Purification and ligand binding of a soluble class I major histocompatibility complex molecule consisting of the first three domains of H-2Kd fused to beta 2-microglobulin expressed in the baculovirus-insect cell system.

A recombinant baculovirus encoding a single-chain murine major histocompatibility complex class I molecule in which the first three domains of H-2Kd are fused to beta 2-microglobulin (beta 2-m) via a 15-amino acid linker has been isolated and used to infect lepidopteran cells. A soluble, 391-amino acid single-chain H-2Kd (SC-Kd) molecule of 48 kDa was synthesized and glycosylated in insect cells and could be purified in the absence of detergents by affinity chromatography using the anti-H-2Kd monoclonal antibody SF1.1.1.1. We tested the ability of SC-Kd to bind antigenic peptides using a direct binding assay based on photoaffinity labeling. The photoreactive derivative was prepared from the H-2Kd-restricted Plasmodium berghei circumsporozoite protein (P.b. CS) peptide 253-260 (YIPSAEKI), a probe that we had previously shown to be unable to bind to the H-2Kd heavy chain in infected cells in the absence of co-expressed beta 2-microglobulin. SC-Kd expressed in insect cells did not require additional mouse beta 2-m to bind the photoprobe, indicating that the covalently attached beta 2-m could substitute for the free molecule. Similarly, binding of the P.b. CS photoaffinity probe to the purified SC-Kd molecule was unaffected by the addition of exogenous beta 2-m. This is in contrast to H-2KdQ10, a soluble H-2Kd molecule in which beta 2-m is noncovalently bound to the soluble heavy chain, whose ability to bind the photoaffinity probe is greatly enhanced in the presence of an excess of exogenous beta 2-m. The binding of the probe to SC-Kd was allele-specific, since labeling was selectively inhibited only by antigenic peptides known to be presented by the H-2Kd molecule.

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.

Fate of TLR-1/TLR-2 agonist functionalised pDNA nanoparticles upon deposition at the human bronchial epithelium in vitro.

BACKGROUND: Plasmid DNA vaccination is a promising approach, but studies in non-human primates and humans failed to achieve protective immunity. To optimise this technology further with focus on pulmonary administration, we developed and evaluated an adjuvant-equipped DNA carrier system based on the biopolymer chitosan. In more detail, the uptake and accompanying immune response of adjuvant Pam3Cys (Toll-like receptor-1/2 agonist) decorated chitosan DNA nanoparticles (NP) were explored by using a three-dimensional (3D) cell culture model of the human epithelial barrier. Pam3Cys functionalised and non-functionalised chitosan DNA NP were sprayed by a microsprayer onto the surface of 3D cell cultures and uptake of NP by epithelial and immune cells (blood monocyte-derived dendritic cells (MDDC) and macrophages (MDM)) was visualised by confocal laser scanning microscopy. In addition, immune activation by TLR pathway was monitored by analysis of interleukin-8 and tumor necrosis factor-α secretions (ELISA). RESULTS: At first, a high uptake rate into antigen-presenting cells (MDDC: 16-17%; MDM: 68-75%) was obtained. Although no significant difference in uptake patterns was observed for Pam3Cys adjuvant functionalised and non-functionalised DNA NP, ELISA of interleukin-8 and tumor necrosis factor-α demonstrated clearly that Pam3Cys functionalisation elicited an overall higher immune response with the ranking of Pam3Cys chitosan DNA NPâeuro0/00>âeuro0/00chitosan DNA NPâeuro0/00=âeuro0/00DNA unloaded chitosan NPâeuro0/00>âeuro0/00control (culture medium). CONCLUSIONS: Chitosan-based DNA delivery enables uptake into abluminal MDDC, which are the most immune competent cells in the human lung for the induction of antigen-specific immunity. In addition, Pam3Cys adjuvant functionalisation of chitosan DNA NP enhances significantly an environment favoring recruitment of immune cells together with a Th1 associated (cellular) immune response due to elevated IL-8 and TNF-α levels. The latter renders this DNA delivery approach attractive for potential DNA vaccination against intracellular pathogens in the lung (e.g., Mycobacterium tuberculosis or influenza virus).

Geochemistry and stable isotope composition of fresh alkaline porphyry copper tailings: Implications on sources and mobility of elements during transport and early stages of deposition

Prevention of acid mine drainage (AMD) in sulfide-containing tailings requires the identification of the geochemical processes and element pathways in the early stages of tailing deposition. However, analyses of recently deposited tailings in active tailings impoundments are scarce because mineralogical changes occur near the detection limits of many assays. This study shows that a detailed geochemical study which includes stable isotopes of water (delta H-2, delta O-18), dissolved sulfates (delta S-34, delta O-18) and hydrochernical parameter (pH, Eh, DOC, major and trace elements) from tailings samples taken at different depths in rainy and dry seasons allows the understanding of weathering (oxidation, dissolution, sorption, and desorption), water and element pathways, and mixing processes in active tailings impoundments. Fresh alkaline tailings (pH 9.2-10.2) from the Cu-Mo porphyry deposit in El Teniente, Chile had low carbonate (0.8-1.1 Wt-% CaCO3 equivalent) and sulfide concentrations (0.8-1.3 wt.%, mainly as pyrite). In the alkaline tailings water, Mo and Cu (up to 3.9 mg/L Mo and 0.016 mg/L Cu) were mobile as MoO42- and Cu (OH)(2)(0). During the flotation, tailings water reached equilibrium with gypsum (up to 738 mg/L Ca and 1765 mg/ L SO4). The delta S-34 VS. delta O-18 covariations of dissolved sulfate (2.3 to 4.5% delta S-34 and 4.1 to 6.0 % delta O-18) revealed the sulfate sources: the dissolution of primary sulfates (12.0 to 13.2%. delta S-34, 7.4 to 10.9%.delta O-18) and oxidation of primary sulfides (-6.7 to 1.7%. delta S-34). Sedimented tailings in the tailings impoundment can be divided into three layers with different water sources, element pathways, and geochemical processes. The deeper sediments (> 1 m depth) were infiltrated by catchment water, which partly replaced the original tailings water, especially during the winter season. This may have resulted in the change from alkaline to near-neutral pH and towards lower concentrations of most dissolved elements. The neutral pH and high DOC (up to 99.4 mg/L C) of the catchment water mobilized Cu (up to 0.25 mg/L) due to formation of organic Cu complexes; and Zn (up to 130 mg/L) due to dissolution of Zn oxides and desorption). At I m depth, tailings pore water obtained during the winter season was chemically and isotopically similar to fresh tailings water (pH 9.8-10.6, 26.7-35.5 mg/L Cl, 2.3-6.0 mg/L Mo). During the summer, a vadose zone evolved locally and temporarily up to 1.2 m depth. resulting in a higher concentration of dissolved solids in the pore water due to evaporation. During periodical new deposition of fresh tailings, the geochemistry of the surface layer was geochemically similar to fresh tailings. In periods without deposition, sulfide oxidation was suggested by decreasing pH (7.7-9.5), enrichment of MoO42- and SO42-, and changes in the isotopic composition of dissolved sulfates. Further enrichment for Na, K, Cl, SO4, Mg, Cu, and Mo (up to 23.8 mg/L Mo) resulted from capillary transport towards the surface followed by evaporation and the precipitation of highly soluble efflorescent salts (e.g., mirabilite, syngenite) at the tailing surface during summer. (C) 2008 Elsevier B.V. All rights reserved.

Single-metal deposition: optimization of this fingermark enhancement technique

Following the introduction of single-metal deposition (SMD), a simplified fingermark detection technique based on multimetal deposition, optimization studies were conducted. The different parameters of the original formula were tested and the results were evaluated based on the contrast and overall aspect of the enhanced fingermarks. The new formula for SMD was found based on the most optimized parameters. Interestingly, it was found that important variations from the base parameters did not significantly affect the outcome of the enhancement, thus demonstrating that SMD is a very robust technique. Finally, a comparison of the optimized SMD with multi-metal deposition (MMD) was carried out on different surfaces. It was demonstrated that SMD produces comparable results to MMD, thus validating the technique.