Spatial correlations and cross sections of clusters in the A + B -> 0 reaction

We consider the distribution of cross sections of clusters and the density-density correlation functions for the A+B¿0 reaction. We solve the reaction-diffusion equations numerically for random initial distributions of reactants. When both reactant species have the same diffusion coefficients the distribution of cross sections and the correlation functions scale with the diffusion length and obey superuniversal laws (independent of dimension). For different diffusion coefficients the correlation functions still scale, but the scaling functions depend on the dimension and on the diffusion coefficients. Furthermore, we display explicitly the peculiarities of the cluster-size distribution in one dimension.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Mineralogy of the clay fraction of Alfisols in two slope curvatures: III - spatial variability

A good knowledge of the spatial distribution of clay minerals in the landscape facilitates the understanding of the influence of relief on the content and crystallographic attributes of soil minerals such as goethite, hematite, kaolinite and gibbsite. This study aimed at describing the relationships between the mineral properties of the clay fraction and landscape shapes by determining the mineral properties of goethite, hematite, kaolinite and gibbsite, and assessing their dependence and spatial variability, in two slope curvatures. To this end, two 100 × 100 m grids were used to establish a total of 121 regularly spaced georeferenced sampling nodes 10 m apart. Samples were collected from the layer 0.0-0.2 m and analysed for iron oxides, and kaolinite and gibbsite in the clay fraction. Minerals in the clay fraction were characterized from their X-ray diffraction (XRD) patterns, which were interpreted and used to calculate the width at half height (WHH) and mean crystallite dimension (MCD) of iron oxides, kaolinite, and gibbsite, as well as aluminium substitution and specific surface area (SSA) in hematite and goethite. Additional calculations included the goethite and hematite contents, and the goethite/(goethite+hematite) [Gt/(Gt+Hm)] and kaolinite/(kaolinite+gibbsite) [Kt/(Kt+Gb)] ratios. Mineral properties were established by statistical analysis of the XRD data, and spatial dependence was assessed geostatistically. Mineralogical properties differed significantly between the convex area and concave area. The geostatistical analysis showed a greater number of mineralogical properties with spatial dependence and a higher range in the convex than in the concave area.

Formation of a spatial memory trace : a behavioral, cellular and molecular study

THESIS ABSTRACTThis thesis project was aimed at studying the molecular mechanisms underlying learning and memory formation, in particular as they relate to the metabolic coupling between astrocytes and neurons. For that, changes in the metabolic activity of different mice brain regions after 1 or 9 days of training in an eight-arm radial maze were assessed by (14C) 2-deoxyglucose (2DG) autoradiography. Significant differences in the areas engaged during the behavioral task at day 1 (when animals are confronted for the first time to the learning task) and at day 9 (when animals are highly performing) have been identified. These areas include the hippocampus, the fornix, the parietal cortex, the laterodorsal thalamic nucleus and the mammillary bodies at day 1 ; and the anterior cingulate, the retrosplenial cortex and the dorsal striatum at day 9. Two of these cerebral regions (those presenting the greatest changes at day 1 and day 9: the hippocampus and the retrosplenial cortex, respectively) were microdissected by laser capture microscopy and selected genes related to neuron-glia metabolic coupling, glucose metabolism and synaptic plasticity were analyzed by RT-PCR. 2DG and gene expression analysis were performed at three different times: 1) immediately after the end of the behavioral paradigm, 2) 45 minutes and 3) 6 hours after training. The main goal of this study was the identification of the metabolic adaptations following the learning task. Gene expression results demonstrate that the learning task profoundly modulates the pattern of gene expression in time, meaning that these two cerebral regions with high 2DG signal (hippocampus and retrosplenial cortex) have adapted their metabolic molecular machinery in consequence. Almost all studied genes show a higher expression in the hippocampus at day 1 compared to day 9, while an increased expression was found in the retrosplenial cortex at day 9. We can observe these molecular adaptations with a short delay of 45 minutes after the end of the task. However, 6 hours after training a high gene expression was found at day 9 (compared to day 1) in both regions, suggesting that only one day of training is not sufficient to detect transcriptional modifications several hours after the task. Thus, gene expression data match 2DG results indicating a transfer of information in time (from day 1 to day 9) and in space (from the hippocampus to the retrosplenial cortex), and this at a cellular and a molecular level. Moreover, learning seems to modify the neuron-glia metabolic coupling, since several genes involved in this coupling are induced. These results also suggest a role of glia in neuronal plasticity.RESUME DU TRAVAIL DE THESECe projet de thèse a eu pour but l'étude des mécanismes moléculaires qui sont impliqués dans l'apprentissage et la mémoire et, en particulier, à les mettre en rapport avec le couplage métabolique existant entre les astrocytes et les neurones. Pour cela, des changements de l'activité métabolique dans différentes régions du cerveau des souris après 1 ou 9 jours d'entraînement dans un labyrinthe radial à huit-bras ont été évalués par autoradiographie au 2-désoxyglucose (2DG). Des différences significatives dans les régions engagées pendant la tâche comportementale au jour 1 (quand les animaux sont confrontés pour la première fois à la tâche) et au jour 9 (quand les animaux ont déjà appris) ont été identifiés. Ces régions incluent, au jour 1, l'hippocampe, le fornix, le cortex pariétal, le noyau thalamic laterodorsal et les corps mamillaires; et, au jour 9, le cingulaire antérieur, le cortex retrosplenial et le striatum dorsal. Deux de ces régions cérébrales (celles présentant les plus grands changements à jour 1 et à jour 9: l'hippocampe et le cortex retrosplenial, respectivement) ont été découpées par microdissection au laser et quelques gènes liés au couplage métabolique neurone-glie, au métabolisme du glucose et à la plasticité synaptique ont été analysées par RT-PCR. L'étude 2DG et l'analyse de l'expression de gènes ont été exécutés à trois temps différents: 1) juste après entraînement, 2) 45 minutes et 3) 6 heures après la fin de la tâche. L'objectif principal de cette étude était l'identification des adaptations métaboliques suivant la tâche d'apprentissage. Les résultats de l'expression de gènes démontrent que la tâche d'apprentissage module profondément le profile d'expression des gènes dans le temps, signifiant que ces deux régions cérébrales avec un signal 2DG élevé (l'hippocampe et le cortex retrosplenial) ont adapté leurs « machines moléculaires » en conséquence. Presque tous les gènes étudiés montrent une expression plus élevée dans l'hippocampe au jour 1 comparé au jour 9, alors qu'une expression accrue a été trouvée dans le cortex retrosplenial au jour 9. Nous pouvons observer ces adaptations moléculaires avec un retard court de 45 minutes après la fin de la tâche. Cependant, 6 heures après l'entraînement, une expression de gènes élevée a été trouvée au jour 9 (comparé à jour 1) dans les deux régions, suggérant que seulement un jour d'entraînement ne suffit pas pour détecter des modifications transcriptionelles plusieurs heures après la tâche. Ainsi, les données d'expression de gènes corroborent les résultats 2DG indiquant un transfert d'information dans le temps (de jour 1 à jour 9) et dans l'espace (de l'hippocampe au cortex retrosplenial), et ceci à un niveau cellulaire et moléculaire. D'ailleurs, la tâche d'apprentissage semble modifier le couplage métabolique neurone-glie, puisque de nombreux gènes impliqués dans ce couplage sont induits. Ces observations suggèrent un rôle important de la glie dans les mécanismes de plasticité du système nerveux.

Spatial planning to the test of property rights in Switzerland : an innovative land management approach to coordinate spatial planning goals with property rights interests

In Switzerland, the land management regime is characterized by a liberal attitude towards the institution of property rights, which is guaranteed by the Constitution. Under the present Swiss constitutional arrangement, authorities (municipalities) are required to take into account landowners' interests when implementing their spatial planning policy. In other words, the institution of property rights cannot be restricted easily in order to implement zoning plans and planning projects. This situation causes many problems. One of them is the gap between the way land is really used by the landowners and the way land should be used based on zoning plans. In fact, zoning plans only describe how landowners should use their property. There is no sufficient provision for handling cases where the use is not in accordance with zoning plans. In particular, landowners may not be expropriated for a non-conforming use of the land. This situation often leads to the opening of new building areas in greenfields and urban sprawl, which is in contradiction with the goals set into the Federal Law on Spatial Planning. In order to identify legal strategies of intervention to solve the problem, our paper is structured into three main parts. Firstly, we make a short description of the Swiss land management regime. Then, we focus on an innovative land management approach designed to implement zoning plans in accordance with property rights. Finally, we present a case study that shows the usefulness of the presented land management approach in practice. We develop three main results. Firstly, the land management approach brings a mechanism to involve landowners in planning projects. Coordination principle between spatial planning goals and landowners' interests is the cornerstone of all the process. Secondly, the land use is improved both in terms of space and time. Finally, the institution of property rights is not challenged, since there is no expropriation and the market stays free.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Spatial estimation of foliar phosphorus in different species of the genus Coffea based on soil properties

Information underlying analyses of coffee fertilization systems should consider both the soil and the nutritional status of plants. This study investigated the spatial relationship between phosphorus (P) levels in coffee plant tissues and soil chemical and physical properties. The study was performed using two arabica and one canephora coffee variety. Sampling grids were established in the areas, and the points georeferenced. The assessed properties of the soil were levels of available phosphorus (P-Mehlich), remaining phosphorus (P-rem) and particle size, and of the plant tissue, phosphorus levels (foliar P). The data were subjected to descriptive statistical analysis, correlation analysis, cluster analysis, and probability tests. Geostatistical and trend analyses were only performed for pairs of variables with significant linear correlation. The spatial variability for foliar P content was high for the variety Catuai and medium for the other evaluated plants. Unlike P-Mehlich, the variability in P-rem of the soil indicated the nutritional status of this nutrient in the plant.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

The importance of host spatial distribution for parasite specialization and speciation: a comparative study of bird fleas (Siphonaptera : Ceratophyllidae)

1. The environment of parasites is determined largely by their hosts. Variation in host quality, abundance and spatial distribution affects the balance between selection within hosts and gene flow between hosts, and this should determine the evolution of a parasite's host-range and its propensity to locally adapt and speciate. 2. We investigated the relationship between host spatial distribution and (1) parasite host range, (2) parasite mobility and (3) parasite geographical range, in a comparative study of a major group of avian ectoparasites, the birds fleas belonging to the Ceratophyllidae (Siphonaptera). 3. Flea species parasitizing colonial birds had narrower host ranges than those infesting territorial nesters or birds with an intermediate level of nest aggregation. 4. The potential mobility and geographical ranges of fleas decreased with increasing level of aggregation of their hosts and increased with the fleas' host ranges. 5. Birds with aggregated nest distribution harboured more flea species mainly due to a larger number of specialists than solitarily nesting hosts. 6. These results emphasize the importance of host spatial distribution for the evolution of specialization, and for local adaptation and speciation in Ceratophyllid bird fleas.

Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.