High-resolution numerical modelling of flow-vegetation interactions

In this paper, we present and apply a new three-dimensional model for the prediction of canopy-flow and turbulence dynamics in open-channel flow. The approach uses a dynamic immersed boundary technique that is coupled in a sequentially staggered manner to a large eddy simulation. Two different biomechanical models are developed depending on whether the vegetation is dominated by bending or tensile forces. For bending plants, a model structured on the Euler-Bernoulli beam equation has been developed, whilst for tensile plants, an N-pendula model has been developed. Validation against flume data shows good agreement and demonstrates that for a given stem density, the models are able to simulate the extraction of energy from the mean flow at the stem-scale which leads to the drag discontinuity and associated mixing layer.

Dynamics of experimental populations of native and introduced blowflies (Diptera: Calliphoridae): mathematical modelling and the transition from asymptotic equilibrium to bounded oscillations

The equilibrium dynamics of native and introduced blowflies is modelled using a density-dependent model of population growth that takes into account important features of the life-history in these flies. A theoretical analysis indicates that the product of maximum fecundity and survival is the primary determinant of the dynamics. Cochliomyia macellaria, a blowfly native to the Americas and the introduced Chrysomya megacephala and Chrysomya putoria, differ in their dynamics in that the first species shows a damping oscillatory behavior leading to a one-point equilibrium, whereas in the last two species population numbers show a two-point limit cycle. Simulations showed that variation in fecundity has a marked effect on the dynamics and indicates the possibility of transitions from one-point equilibrium to bounded oscillations and aperiodic behavior. Variation in survival has much less influence on the dynamics.

The carbohydrate-binding specificity and molecular modelling of Canavalia maritima and Dioclea grandiflora lectins

The carbohydrate-binding specificity of lectins from the seeds of Canavalia maritima and Dioclea grandiflora was studied by hapten-inhibition of haemagglutination using various sugars and sugar derivatives as inhibitors, including N-acetylneuraminic acid and N-acetylmuramic acid. Despite some discrepancies, both lectins exhibited a very similar carbohydrate-binding specificity as previously reported for other lectins from Diocleinae (tribe Phaseoleae, sub-tribe Diocleinae). Accordingly, both lectins exhibited almost identical hydropathic profiles and their three-dimensional models built up from the atomic coordinates of ConA looked very similar. However, docking experiments of glucose and mannose in their monosaccharide-binding sites, by comparison with the ConA-mannose complex used as a model, revealed conformational changes in side chains of the amino acid residues involved in the binding of monosaccharides. These results fully agree with crystallographic data showing that binding of specific ligands to ConA requires conformational chances of its monosaccharide-binding site.

Modelling Interbank Relations during the International Financial Crisis

This paper examines the effects of the current financial crisis on the correlations of four international banking stocks. We find that in the beginning of the crisis banks generally show a transition to a higher correlation followed by a dramatic decline towards the end of 2008. These findings are consistent with both traditional contagion theory and the more recent network theory of contagion. JEL classifications: C51; G15 Keywords: Financial Crises; Contagion; Interbank Markets.

Different approaches to modelling the cost-effectiveness of schistosomiasis control

This paper reviews three different approaches to modelling the cost-effectiveness of schistosomiasis control. Although these approaches vary in their assessment of costs, the major focus of the paper is on the evaluation of effectiveness. The first model presented is a static economic model which assesses effectiveness in terms of the proportion of cases cured. This model is important in highlighting that the optimal choice of chemotherapy regime depends critically on the level of budget constraint, the unit costs of screening and treatment, the rates of compliance with screening and chemotherapy and the prevalence of infection. The limitations of this approach is that it models the cost-effectiveness of only one cycle of treatment, and effectiveness reflects only the immediate impact of treatment. The second model presented is a prevalence-based dynamic model which links prevalence rates from one year to the next, and assesses effectiveness as the proportion of cases prevented. This model was important as it introduced the concept of measuring the long-term impact of control by using a transmission model which can assess reduction in infection through time, but is limited to assessing the impact only on the prevalence of infection. The third approach presented is a theoretical framework which describes the dynamic relationships between infection and morbidity, and which assesses effectiveness in terms of case-years prevented of infection and morbidity. The use of this model in assessing the cost-effectiveness of age-targeted treatment in controlling Schistosoma mansoni is explored in detail, with respect to varying frequencies of treatment and the interaction between drug price and drug efficacy.

Population pharmacokinetic modelling and evaluation of different dosage regimens for darunavir and ritonavir in HIV-infected individuals.

OBJECTIVES: Darunavir is a protease inhibitor that is administered with low-dose ritonavir to enhance its bioavailability. It is prescribed at standard dosage regimens of 600/100 mg twice daily in treatment-experienced patients and 800/100 mg once daily in naive patients. A population pharmacokinetic approach was used to characterize the pharmacokinetics of both drugs and their interaction in a cohort of unselected patients and to compare darunavir exposure expected under alternative dosage regimens. METHODS: The study population included 105 HIV-infected individuals who provided darunavir and ritonavir plasma concentrations. Firstly, a population pharmacokinetic analysis for darunavir and ritonavir was conducted, with inclusion of patients' demographic, clinical and genetic characteristics as potential covariates (NONMEM(®)). Then, the interaction between darunavir and ritonavir was studied while incorporating levels of both drugs into different inhibitory models. Finally, model-based simulations were performed to compare trough concentrations (Cmin) between the recommended dosage regimen and alternative combinations of darunavir and ritonavir. RESULTS: A one-compartment model with first-order absorption adequately characterized darunavir and ritonavir pharmacokinetics. The between-subject variability in both compounds was important [coefficient of variation (CV%) 34% and 47% for darunavir and ritonavir clearance, respectively]. Lopinavir and ritonavir exposure (AUC) affected darunavir clearance, while body weight and darunavir AUC influenced ritonavir elimination. None of the tested genetic variants showed any influence on darunavir or ritonavir pharmacokinetics. The simulations predicted darunavir Cmin much higher than the IC50 thresholds for wild-type and protease inhibitor-resistant HIV-1 strains (55 and 550 ng/mL, respectively) under standard dosing in >98% of experienced and naive patients. Alternative regimens of darunavir/ritonavir 1200/100 or 1200/200 mg once daily also had predicted adequate Cmin (>550 ng/mL) in 84% and 93% of patients, respectively. Reduction of darunavir/ritonavir dosage to 600/50 mg twice daily led to a 23% reduction in average Cmin, still with only 3.8% of patients having concentrations below the IC50 for resistant strains. CONCLUSIONS: The important variability in darunavir and ritonavir pharmacokinetics is poorly explained by clinical covariates and genetic influences. In experienced patients, treatment simplification strategies guided by drug level measurements and adherence monitoring could be proposed.

Constitutive activity and inverse agonism at the α(₁a) and α(₁b) adrenergic receptor subtypes.

The α(1b)-adrenergic receptor (AR) was, after rhodopsin, the first G protein-coupled receptor (GPCR) in which point mutations were shown to trigger constitutive (agonist-independent) activity. Constitutively activating mutations have been found in other AR subtypes as well as in several GPCRs. This chapter briefly summarizes the main findings on constitutively active mutants of the α(1a)- and α(1b)-AR subtypes and the methods used to predict activating mutations, to measure constitutive activity of Gq-coupled receptors and to investigate inverse agonism. In addition, it highlights the implications of studies on constitutively active AR mutants on elucidating the molecular mechanisms of receptor activation and drug action.

Re: Cost effectiveness of strategies to combat cardiovascular disease, diabetes, and tobacco use in sub-Saharan Africa and South East Asia: mathematical modelling study.

Rapid response to: Ortegón M, Lim S, Chisholm D, Mendis S. Cost effectiveness of strategies to combat cardiovascular disease, diabetes, and tobacco use in sub-Saharan Africa and South East Asia: mathematical modelling study. BMJ. 2012 Mar 2;344:e607. doi: 10.1136/bmj.e607. PMID: 22389337.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

A theoretical model of the tridimensional structure of Bacillus thuringiensis subsp. medellin Cry 11Bb toxin deduced by homology modelling

Cry11Bb is an insecticidal crystal protein produced by Bacillus thuringiensis subsp. medellin during its stationary phase; this ¶-endotoxin is active against dipteran insects and has great potential for mosquito borne disease control. Here, we report the first theoretical model of the tridimensional structure of a Cry11 toxin. The tridimensional structure of the Cry11Bb toxin was obtained by homology modelling on the structures of the Cry1Aa and Cry3Aa toxins. In this work we give a brief description of our model and hypothesize the residues of the Cry11Bb toxin that could be important in receptor recognition and pore formation. This model will serve as a starting point for the design of mutagenesis experiments aimed to the improvement of toxicity, and to provide a new tool for the elucidation of the mechanism of action of these mosquitocidal proteins.

Sound Texture Modelling

In the PhD thesis “Sound Texture Modeling” we deal with statistical modelling or textural sounds like water, wind, rain, etc. For synthesis and classification. Our initial model is based on a wavelet tree signal decomposition and the modeling of the resulting sequence by means of a parametric probabilistic model, that can be situated within the family of models trainable via expectation maximization (hidden Markov tree model ). Our model is able to capture key characteristics of the source textures (water, rain, fire, applause, crowd chatter ), and faithfully reproduces some of the sound classes. In terms of a more general taxonomy of natural events proposed by Graver, we worked on models for natural event classification and segmentation. While the event labels comprise physical interactions between materials that do not have textural propierties in their enterity, those segmentation models can help in identifying textural portions of an audio recording useful for analysis and resynthesis. Following our work on concatenative synthesis of musical instruments, we have developed a pattern-based synthesis system, that allows to sonically explore a database of units by means of their representation in a perceptual feature space. Concatenative syntyhesis with “molecules” built from sparse atomic representations also allows capture low-level correlations in perceptual audio features, while facilitating the manipulation of textural sounds based on their physical and perceptual properties. We have approached the problem of sound texture modelling for synthesis from different directions, namely a low-level signal-theoretic point of view through a wavelet transform, and a more high-level point of view driven by perceptual audio features in the concatenative synthesis setting. The developed framework provides unified approach to the high-quality resynthesis of natural texture sounds. Our research is embedded within the Metaverse 1 European project (2008-2011), where our models are contributting as low level building blocks within a semi-automated soundscape generation system.

Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach

Protecting native biodiversity against alien invasive species requires powerful methods to anticipate these invasions and to protect native species assumed to be at risk. Here, we describe how species distribution models (SDMs) can be used to identify areas predicted as suitable for rare native species and also predicted as highly susceptible to invasion by alien species, at present and under future climate and land-use scenarios. To assess the condition and dynamics of such conflicts, we developed a combined predictive modelling (CPM) approach, which predicts species distributions by combining two SDMs fitted using subsets of predictors classified as acting at either regional or local scales. We illustrate the CPM approach for an alien invader and a rare species associated to similar habitats in northwest Portugal. Combined models predict a wider variety of potential species responses, providing more informative projections of species distributions and future dynamics than traditional, non-combined models. They also provide more informative insight regarding current and future rare-invasive conflict areas. For our studied species, conflict areas of highest conservation relevance are predicted to decrease over the next decade, supporting previous reports that some invasive species may contract their geographic range and impact due to climate change. More generally, our results highlight the more informative character of the combined approach to address practical issues in conservation and management programs, especially those aimed at mitigating the impact of invasive plants, land-use and climate changes in sensitive regions