Individual-based modelling of bacterial cultures to study the lag ohase

Els bacteris són la forma dominant de vida del planeta: poden sobreviure en medis molt adversos, i en alguns casos poden generar substàncies que quan les ingerim ens són tòxiques. La seva presència en els aliments fa que la microbiologia predictiva sigui un camp imprescindible en la microbiologia dels aliments per garantir la seguretat alimentària. Un cultiu bacterià pot passar per quatre fases de creixement: latència, exponencial, estacionària i de mort. En aquest treball s’ha avançat en la comprensió dels fenòmens intrínsecs a la fase de latència, que és de gran interès en l’àmbit de la microbiologia predictiva. Aquest estudi, realitzat al llarg de quatre anys, s’ha abordat des de la metodologia Individual-based Modelling (IbM) amb el simulador INDISIM (INDividual DIScrete SIMulation), que ha estat millorat per poder fer-ho. INDISIM ha permès estudiar dues causes de la fase de latència de forma separada, i abordar l’estudi del comportament del cultiu des d’una perspectiva mesoscòpica. S’ha vist que la fase de latència ha de ser estudiada com un procés dinàmic, i no definida per un paràmetre. L’estudi de l’evolució de variables com la distribució de propietats individuals entre la població (per exemple, la distribució de masses) o la velocitat de creixement, han permès distingir dues etapes en la fase de latència, inicial i de transició, i aprofundir en la comprensió del que passa a nivell cel•lular. S’han observat experimentalment amb citometria de flux diversos resultats previstos per les simulacions. La coincidència entre simulacions i experiments no és trivial ni casual: el sistema estudiat és un sistema complex, i per tant la coincidència del comportament al llarg del temps de diversos paràmetres interrelacionats és un aval a la metodologia emprada en les simulacions. Es pot afirmar, doncs, que s’ha verificat experimentalment la bondat de la metodologia INDISIM.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Virological study of a dengue type 1 epidemic at Rio de Janeiro

A dengue outbreak started in March, 1986 in Rio de Janeiro and spread very rapidly to other parts of the country. The great majority of cases presented classical dengue fever but there was one fatal case, confirmed by virus isolation. Dengue type 1 strains were isolated from patients and vectors (Aedes aegypti) in the area by cultivation in A. albopictus C6/36 cell line. The cytopathic effect (CPE) was studied by electron microscopy. An IgM capture test (MAC-ELISA) was applied with clear and reproducible results for diagnosis and evaluation of virus circulation; IgM antibodies appeared soon after start of clinical disease, and persisted for about 90 days in most patients. The test was type-specific in about 50% of the patients but high levels of heterologous response for type 3 were observed. An overall isolation rate of 46,8% (813 virus strains out of 1734 specimens) was recorded. The IgM test increased the number of confirmed cases to 58,2% (1479 out of 2451 suspected cases). The importance of laboratory diagnosis in all regions where the vectors are present is emphasized.

Trends in lung cancer among young European women: the rising epidemic in France and Spain.

Lung cancer mortality in young women in the European Union (EU) has steadily increased until the mid 1990 s and has levelled off thereafter, but trends have been heterogeneous in various countries. We analyzed therefore age-standardized trends in lung cancer mortality in young women (20-44) for the 6 major European countries, using joinpoint regression. In the early 1970s the highest lung cancer mortality in young women was in the UK (2.1/100,000). UK rates, however, steadily declined and in 2000-2004 they were the lowest of all 6 major EU countries (1.2/100,000). The second lowest rate in 2000-2002 was in Italy, whose rates remained around 1.1/100,000 between 1970 and 1994, and increased to 1.4 thereafter. In Germany and Poland, lung cancer rates in young women rose from 0.8-1.0/100,000 in the early 1970s to 1.7-1.9 in the mid 1990 s and levelled off during the last decade. Major rises over recent years were observed in France (from 0.8/100,000 in 1985-1989 to 2.2 in 2000-2003) and in Spain (from 0.8 in the 1985-1989 to 1.7 in 2000-2004). Thus, France showed both the highest rate observed over the last 3 decades and the largest rise over the last 2 decades. Since recent trends in the young give relevant information to the likely future trends in middle age, the female lung cancer epidemic is likely to expand in southern Europe from the current rates of 5.0/100,000 in Spain and 7.7 in France to approach 20/100,000 within the next 2-3 decades. Urgent interventions for smoking cessation in women are therefore required.

Modelling of bird distribution under complex mediterranean landscape dynamics: open habitat birds and fire (MEDFIRE)

Report for the scientific sojourn at the Simon Fraser University, Canada, from July to September 2007. General context: landscape change during the last years is having significant impacts on biodiversity in many Mediterranean areas. Land abandonment, urbanisation and specially fire are profoundly transforming large areas in the Western Mediterranean basin and we know little on how these changes influence species distribution and in particular how these species will respond to further change in a context of global change including climate. General objectives: integrate landscape and population dynamics models in a platform allowing capturing species distribution responses to landscape changes and assessing impact on species distribution of different scenarios of further change. Specific objective 1: develop a landscape dynamic model capturing fire and forest succession dynamics in Catalonia and linked to a stochastic landscape occupancy (SLOM) (or spatially explicit population, SEPM) model for the Ortolan bunting, a species strongly linked to fire related habitat in the region. Predictions from the occupancy or spatially explicit population Ortolan bunting model (SEPM) should be evaluated using data from the DINDIS database. This database tracks bird colonisation of recently burnt big areas (&50 ha). Through a number of different SEPM scenarios with different values for a number of parameter, we should be able to assess different hypothesis in factors driving bird colonisation in new burnt patches. These factors to be mainly, landscape context (i.e. difficulty to reach the patch, and potential presence of coloniser sources), dispersal constraints, type of regenerating vegetation after fire, and species characteristics (niche breadth, etc).

Analog Modelling of N-S anticlines of the External Sierras (Jaca basin, Southern Pyrenees)

Report for the scientific sojourn carried out at the Uppsala Universitet, Sweden, from April to July the 2007. Two series of analogue models are used to explore ductile-frictional contrasts of the basal décollement in the development of oblique and transverse structures simultaneously to thin-skinned shortening. These models simulate the evolution of the Central External Sierras (Southern Pyrenees, Spain), which constitute the frontal emerging part of the southernmost Pyrenean thrust sheet. They are characterized by the presence of transverse N-S to NW-SE anticlines, which are perpendicular to the Pyrenean structural trend and developed in the hangingwall of the Santo Domingo thrust system, detaching on an unevenly distributed Triassic materials (evaporitic-dolomitic interfingerings). Model setup performs a décollement made by three patches of silicone neighbouring pure brittle sand. Model series A test the thickness ratio between overburden and décollement. Model series B test the width of frictional detachment areas. Model results show how deformation reaches further in areas detached on ductile layer whereas frictional décollement areas assimilate the strain by means of an additional uplift. This replicates the structural style of Central External Sierras: higher structural relief of N-S anticlines with regard to orogen-parallel structures, absence of a representative ductile décollement in the core, tilting towards the orogen and foreland-side closure not thrusted by the frontal emerging South-Pyrenean thrust.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.

Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.

The Signalling Channel of Central Bank Interventions: Modelling the Yen/US Dollar Exchange Rate

This paper presents a theoretical framework analysing the signalling channel of exchange rate interventions as an informational trigger. We develop an implicit target zone framework with learning in order to model the signalling channel. The theoretical premise of the model is that interventions convey signals that communicate information about the exchange rate objectives of central bank. The model is used to analyse the impact of Japanese FX interventions during the period 1999 -2011 on the yen/US dollar dynamics.

The regional employment impacts of renewable energy expenditures: The case for modelling

One aspect of the case for policy support for renewable energy developments is the wider economic benefits that are expected to be generated. Within Scotland, as with other regions of the UK, there is a focus on encouraging domestically‐based renewable technologies. In this paper, we use a regional computable general equilibrium framework to model the impact on the Scottish economy of expenditures relating to marine energy installations. The results illustrate the potential for (considerable) ‘legacy’ effects after expenditures cease. In identifying the specific sectoral expenditures with the largest impact on (lifetime) regional employment, this approach offers important policy guidance.

The regional employment impacts of renewable energy expenditures: The case for modelling

One aspect of the case for policy support for renewable energy developments is the wider economic benefits that are expected to be generated. Within Scotland, as with other regions of the UK, there is a focus on encouraging domestically‐based renewable technologies. In this paper, we use a regional computable general equilibrium framework to model the impact on the Scottish economy of expenditures relating to marine energy installations. The results illustrate the potential for (considerable) ‘legacy’ effects after expenditures cease. In identifying the specific sectoral expenditures with the largest impact on (lifetime) regional employment, this approach offers important policy guidance.