Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

A model for intra-familial distribution of an infectious disease (Chagas' disease)

A probabilistic model for intra-familial distribution of infectous disease is proposed and applied to the prevalence of positive serology for Trypanosoma cruzi infection in Northeastern Brazilian sample. This double with one tail excess model fits satisfactorily to the data and its interpretation says that around 51% of these 982 families are free of infection risk; among those that are at risk, 3% have a high risk (0.66), probably due to high domestic infestation of the vector bug; while 97% show a small risk (0.11), probably due to accidental, non-domestic transmission.

Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment

1. Landscape modification is often considered the principal cause of population decline in many bat species. Thus, schemes for bat conservation rely heavily on knowledge about species-landscape relationships. So far, however, few studies have quantified the possible influence of landscape structure on large-scale spatial patterns in bat communities. 2. This study presents quantitative models that use landscape structure to predict (i) spatial patterns in overall community composition and (ii) individual species' distributions through canonical correspondence analysis and generalized linear models, respectively. A geographical information system (GIS) was then used to draw up maps of (i) overall community patterns and (ii) distribution of potential species' habitats. These models relied on field data from the Swiss Jura mountains. 3. Fight descriptors of landscape structure accounted for 30% of the variation in bat community composition. For some species, more than 60% of the variance in distribution could be explained by landscape structure. Elevation, forest or woodland cover, lakes and suburbs, were the most frequent predictors. 4. This study shows that community composition in bats is related to landscape structure through species-specific relationships to resources. Due to their nocturnal activities and the difficulties of remote identification, a comprehensive bat census is rarely possible, and we suggest that predictive modelling of the type described here provides an indispensable conservation tool.

Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data

The sensitivity of altitudinal and latitudinal tree-line ecotones to climate change, particularly that of temperature, has received much attention. To improve our understanding of the factors affecting tree-line position, we used the spatially explicit dynamic forest model TreeMig. Although well-suited because of its landscape dynamics functions, TreeMig features a parabolic temperature growth response curve, which has recently been questioned. and the species parameters are not specifically calibrated for cold temperatures. Our main goals were to improve the theoretical basis of the temperature growth response curve in the model and develop a method for deriving that curve's parameters from tree-ring data. We replaced the parabola with an asymptotic curve, calibrated for the main species at the subalpine (Swiss Alps: Pinus cembra, Larix decidua, Picea abies) and boreal (Fennoscandia: Pinus sylvestris, Betula pubescens, P. abies) tree-lines. After fitting new parameters, the growth curve matched observed tree-ring widths better. For the subalpine species, the minimum degree-day sum allowing, growth (kDDMin) was lowered by around 100 degree-days; in the case of Larix, the maximum potential ring-width was increased to 5.19 mm. At the boreal tree-line, the kDDMin for P. sylvestris was lowered by 210 degree-days and its maximum ring-width increased to 2.943 mm; for Betula (new in the model) kDDMin was set to 325 degree-days and the maximum ring-width to 2.51 mm; the values from the only boreal sample site for Picea were similar to the subalpine ones, so the same parameters were used. However, adjusting the growth response alone did not improve the model's output concerning species' distributions and their relative importance at tree-line. Minimum winter temperature (MinWiT, mean of the coldest winter month), which controls seedling establishment in TreeMig, proved more important for determining distribution. Picea, P. sylvestris and Betula did not previously have minimum winter temperature limits, so these values were set to the 95th percentile of each species' coldest MinWiT site (respectively -7, -11, -13). In a case study for the Alps, the original and newly calibrated versions of TreeMig were compared with biomass data from the National Forest Inventor), (NFI). Both models gave similar, reasonably realistic results. In conclusion, this method of deriving temperature responses from tree-rings works well. However, regeneration and its underlying factors seem more important for controlling species' distributions than previously thought. More research on regeneration ecology, especially at the upper limit of forests. is needed to improve predictions of tree-line responses to climate change further.

Spatial distribution of economic activities: an empirical approach usingself-organizing maps

The aim of this paper is to analyse the colocation patterns of industries and firms. We study the spatial distribution of firms from different industries at a microgeographic level and from this identify the main reasons for this locational behaviour. The empirical application uses data from Mercantile Registers of Spanish firms (manufacturers and services). Inter-sectorial linkages are shown using self-organizing maps. Key words: clusters, microgeographic data, self-organizing maps, firm location JEL classification: R10, R12, R34

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Ocular distribution, spectrum of activity, and in vivo viral neutralization of a fully humanized anti-herpes simplex virus IgG Fab fragment following topical application.

Herpes simplex ocular infection is a major cause of corneal blindness. Local antiviral treatments exist but are associated with corneal toxicity, and resistance has become an issue. We evaluated the biodistribution and efficacy of a humanized anti-herpes simplex virus (anti-HSV) IgG FAb fragment (AC-8; 53 kDa) following repeated topical administration. AC-8 was found in the corneal epithelium, anterior stroma, subepithelial stromal cells, and retinal glial cells, with preferential entry through the ocular limbus. AC-8 was active against 13 different strains of HSV-1, with 50% and 90% mean effective concentrations (MEC(50) and MEC(90), respectively) ranging from 0.03 to 0.13 μg/ml, indicating broad-spectrum activity. The in vivo efficacy of AC-8 was evaluated in a mouse model of herpes-induced ocular disease. Treatment with low-dose AC-8 (1 mg/ml) slightly reduced the ocular disease scores. A greater reduction of the disease scores was observed in the 10-mg/ml AC-8-treated group, but not as much as with trifluridine (TFT). AC-8 treatment reduced viral titers but less than trifluridine. AC-8 did not display any toxicity to the cornea or other structures in the eye. In summary, topical instillation of an anti-HSV FAb can be used on both intact and ulcerated corneas. It is well tolerated and does not alter reepithelialization. Further studies to improve the antiviral effect are needed for AC-8 to be considered for therapeutic use.

Can Remotely Sensed Meteorological Data Significantly Contribute to Reduce Costs of Tsetse Surveys?

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.

Distribution and genesis of the RFRP-producing neurons in the rat brain: comparison with melanin-concentrating hormone- and hypocretin-containing neurons.

Prepro-RFRP-containing neurons have recently been described in the mammalian brain. These neurons are only found in the tuberal hypothalamus. In this work, we have provided a detailed analysis of the distribution of cells expressing the RFRP mRNA, and found them in seven anatomical structures of the tuberal hypothalamus. No co-expression with melanin-concentrating hormone (MCH) or hypocretin (Hcrt), that are also described in neurons of the tuberal hypothalamus, was observed. Using the BrdU method, we found that all RFRP cell bodies are generated between E13 and E14. Thus, RFRP neurons form a specific cell population with a complex distribution pattern in the tuberal hypothalamus. However, they are generated in one peak. These observations are discussed with data concerning the distribution and genesis of the MCH and Hcrt cell populations that are also distributed in the tuberal hypothalamus.

A review of the ticks (Acari, Ixodida) of Brazil, their hosts and geographic distribution - 1. The State of Rio Grande do Sul, Southern Brazil

A review of the ticks (Acari, Ixodida) of the State of Rio Grande do Sul, southern Brazil, was completed as a step towards a definitive list (currently indicated as 12) of such species, their hosts and distribution. The ticks: Argas miniatus (poultry), Ixodes loricatus (opossums), Amblyomma aureolatum (dogs), A. calcaratum (anteaters), A. cooperi (capybaras), A. nodosum (anteaters), A. tigrinum (dogs) (Neotropical) and Rhipicephalus sanguineus (dogs) (introduced, cosmopolitan, Afrotropical) were confirmed as present, in addition to the predominant, Boophilus microplus (cattle) (introduced, pan-tropical, Oriental). Of the further 18 species thus far reported in the literature as present in the state, but unavailable for examination: only Ornithodoros brasiliensis (humans and their habitations) (Neotropical), Ixodes affinis (deer) (Nearctic/Neotropical) and I. auritulus (birds) (Nearctic/Neotropical/Afrotropical/ Australasian) are considered likely; 13 species would benefit from corroborative local data but the majority appear unlikely; reports of A. maculatum (Nearctic/Neotropical, but circum-Caribbean) are considered erroneous; the validity of A. fuscum is in doubt. The very recent, first known report of the tropical Anocentor nitens (horses)(Nearctic/Neotropical), but still apparent absence of the tropical A. cajennense (catholic) (Nearctic/Neotropical) and the sub-tropical/temperate Ixodes pararicinus (cattle) (Neotropical) in Rio Grande do Sul are important for considerations on their current biogeographical distribution and its dynamics in South America. The state has relatively long established, introduced ("exotic"), Old World tick species (B. microplus, R. sanguineus) that continue to represent significant pests and disease vectors to their traditional, introduced domestic animal hosts, cattle and urban dogs. There are also indigenous, New World ticks (A. miniatus, O. brasiliensis, A. aureolatum, A. nitens), as both long established and possibly newly locally introduced species in the state, that should be considered as potential and emergent pests and pathogen vectors to humans and their more recently acquired, introduced domestic animal hosts; rural poultry, dogs and horses.

Preliminary regional rockfall hazard mapping using LIDAR-based slope frequency distribution and CONEFALL modeling

A factor limiting preliminary rockfall hazard mapping at regional scale is often the lack of knowledge of potential source areas. Nowadays, high resolution topographic data (LiDAR) can account for realistic landscape details even at large scale. With such fine-scale morphological variability, quantitative geomorphometric analyses become a relevant approach for delineating potential rockfall instabilities. Using digital elevation model (DEM)-based ?slope families? concept over areas of similar lithology and cliffs and screes zones available from the 1:25,000 topographic map, a susceptibility rockfall hazard map was drawn up in the canton of Vaud, Switzerland, in order to provide a relevant hazard overview. Slope surfaces over morphometrically-defined thresholds angles were considered as rockfall source zones. 3D modelling (CONEFALL) was then applied on each of the estimated source zones in order to assess the maximum runout length. Comparison with known events and other rockfall hazard assessments are in good agreement, showing that it is possible to assess rockfall activities over large areas from DEM-based parameters and topographical elements.

Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.

Distribution of the monocarboxylate transporter MCT2 in human cerebral cortex: an immunohistochemical study

The monocarboxylate transporter MCT2 belongs to a large family of membrane proteins involved in the transport of lactate, pyruvate and ketone bodies. Although its expression in rodent brain has been well documented, the presence of MCT2 in the human brain has been questioned on the basis of low mRNA abundance. In this study, the distribution of the monocarboxylate transporter MCT2 has been investigated in the cortex of normal adult human brain using an immunohistochemical approach. Widespread neuropil staining in all cortical layers was observed by light microscopy. Such a distribution was very similar in three different cortical areas investigated. At the cellular level, the expression of MCT2 could be observed in a large number of neurons, in fibers both in grey and white matter, as well as in some astrocytes, mostly localized in layer I and in the white matter. Double staining experiments combined with confocal microscopy confirmed the neuronal expression but also suggested a preferential postsynaptic localization of synaptic MCT2 expression. A few astrocytes in the grey matter appeared to exhibit MCT2 labelling but at low levels. Electron microscopy revealed strong MCT2 expression at asymmetric synapses in the postsynaptic density and also within the spine head but not in the presynaptic terminal. These data not only demonstrate neuronal MCT2 expression in human, but since a portion of it exhibits a distinct synaptic localization, it further supports a putative role for MCT2 in adjustment of energy supply to levels of activity.

The distribution of two major malaria vectors, Anopheles gambiae and Anopheles arabiensis, in Nigeria

The distribution of Anopheles gambiae and An. arabiensis across the ecological zones of Nigeria (arid savanna in the north gradually turns into humid forest in the south) was investigated. Results of the present study were compared to the distributions determined from samples of indoor-resting females reported by an earlier study over 20 years ago. Larvae were sampled in the rainy seasons of 1997 and 1999 from 24 localities, 10 of which were sampled in both years. Specimens were identified by the polymerase chain reaction method. Results showed that species composition changed significantly among the 10 localities in both years (chi2=13.62, P = 0.0002), but this change was significant in only four of the 10 localities. The identity of the prevalent (more abundant) species changed between 1997 and 1999 in only three of 10 localities. An. arabiensis was prevalent in several localities in the southern Guinea savanna, an area where it was virtually absent over 20 years ago. The data suggest that An. arabiensis has extend its range, although differences in sampling technique (larval sampling versus adult collection) can not be ruled out as a possible explanation.

Recent studies on Schistosoma intercalatum: taxonomic status, puzzling distribution and transmission foci revisited

Schistosoma intercalatum, which causes human rectal schistosomiasis in Africa, still presents a great interest for its imprecise taxonomic status and its puzzling distribution in Africa. Two geographically isolated strains of S. intercalatum are recognized, the Lower Guinea strain and the Congo strain, which differ from each other in a number of morphological, biological and biochemical characteristics. Recent molecular data using RAPD markers indicate high divergence between the two strains, with values of Nei and Li's similarity indice allowing recognition of two genetically distinct taxa: experiments on pre- and post-isolating mechanisms are in progress in order to re-evaluate the taxonomic status of this polytypic species. With regard to its geographical distribution, S. intercalatum is characterized by the existence of two stable endemic areas (localized in Lower Guinea and North East of Democratic Republic of Congo) which correspond to the historical areas of species discovery, and the emergence during the last 15 years of new foci of the Lower Guinea strain outside previously known endemic areas. The absence of local adaptation of the Lower Guinea strain to its intermediate host, supported by experimental studies, may help to facilitate the spread of this strain. Nevertheless, the present restricted distribution of this species remains puzzling, because its potential snail hosts (bulinids) are widely distributed throughout much of Africa. Recent experimental and epidemiological studies suggest that interspecific sexual interactions between human schistosomes could have a role in limiting the distribution of S. intercalatum: the competitive sexual processes acting among human schistosomes show that S. haematobium and S. mansoni are always competitively dominant over S. intercalatum. These epidemiological observations lead the authors to distinguish three kinds of transmission foci for S. intercalatum.