Spin and spatial dynamics in electron-impact scattering off S-wave He using R-matrix with Time-Dependence theory

R-matrix with time-dependence theory is applied to electron-impact ionisation processes for He in the S-wave model. Cross sections for electron-impact excitation, ionisation and ionisation with excitation for impact energies between 25 and 225 eV are in excellent agreement with benchmark cross sections. Ultra-fast dynamics induced by a scattering event is observed through time-dependent signatures associated with autoionisation from doubly excited states. Further insight into dynamics can be obtained through examination of the spin components of the time-dependent wavefunction.

Hierarchical spatial segregation of two Mediterranean vole species: the role of patch‑network structure and matrix composition

According to ecological theory, the coexistence of competitors in patchy environments may be facilitated by hierarchical spatial segregation along axes of environmental variation, but empirical evidence is limited. Cabrera and water voles show a metapopulation-like structure in Mediterranean farmland, where they are known to segregate along space, habitat, and time axes within habitat patches. Here, we assess whether segregation also occurs among and within landscapes, and how this is influenced by patch-network and matrix composition. We surveyed 75 landscapes, each covering 78 ha, where we mapped all habitat patches potentially suitable for Cabrera and water voles, and the area effectively occupied by each species (extent of occupancy). The relatively large water vole tended to be the sole occupant of landscapes with high habitat amount but relatively low patch density (i.e., with a few large patches), and with a predominantly agricultural matrix, whereas landscapes with high patch density (i.e.,many small patches) and low agricultural cover, tended to be occupied exclusively by the small Cabrera vole. The two species tended to co-occur in landscapes with intermediate patch-network and matrix characteristics, though their extents of occurrence were negatively correlated after controlling for environmental effects. In combination with our previous studies on the Cabrera-water vole system, these findings illustrated empirically the occurrence of hierarchical spatial segregation, ranging from withinpatches to among-landscapes. Overall, our study suggests that recognizing the hierarchical nature of spatial segregation patterns and their major environmental drivers should enhance our understanding of species coexistence in patchy environments.

Expression of matrix metalloproteinases-2 and-9 and RECK during alveolar bone regeneration in rat

MMPs are endopeptidases that play a pivotal role in ECM turnover. RECK is a single membrane-anchored MMP-regulator. Here, we evaluated the temporal and spatial expression of MMP-2, MMP-9, and RECK during alveolar bone regeneration. The maxillary central incisor of Wistar rats was extracted and the animals were killed at 1, 3, 7, 10, 14, 21, 28, and 42 days post-operatively (n = 3/period). The hemimaxillae were collected, demineralized and embedded in paraffin. Immunohistochemical analysis was performed by the immunoperoxidase technique with polyclonal antibodies. On day 1, polymorphonuclear cells in the blood clot presented mild immunolabeling for MMPs. During bone remodeling, osteoblasts facing new bone showed positive staining for gelatinases and RECK in all experimental periods. MMPs were also found in the connective tissue and endothelial cells. Our results show for the first time that inactive and/or active forms of MMP-2, MMP-9 and RECK are differentially expressed by osteogenic and connective cells during several events of alveolar bone regeneration. This may be important for the replacement of the blood clot by connective tissue, and in the formation, maturation and remodeling of new bone.

SAKWeb – Spatial autocorrelation and kriging web, a W3 geocomputation perspective

A thesis submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Information Systems

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Spatial competition between two candidates of different quality: the effects of candidate ideology and private information

This paper examines competition in a spatial model of two-candidate elections, where one candidate enjoys a quality advantage over the other candidate. The candidates care about winning and also have policy preferences. There is two-dimensional private information. Candidate ideal points as well as their tradeoffs between policy preferences and winning are private information. The distribution of this two-dimensional type is common knowledge. The location of the median voter's ideal point is uncertain, with a distribution that is commonly known by both candidates. Pure strategy equilibria always exist in this model. We characterize the effects of increased uncertainty about the median voter, the effect of candidate policy preferences, and the effects of changes in the distribution of private information. We prove that the distribution of candidate policies approaches the mixed equilibrium of Aragones and Palfrey (2002a), when both candidates' weights on policy preferences go to zero.

Temporal and spatial appearance of the membrane cytoskeleton and perineuronal nets in the rat neocortex.

Parvalbumin-immunoreactive interneurons are surrounded by perineuronal nets, containing molecules of the extracellular matrix (e.g. tenascin-R). Furthermore, they seem to have a special cytoskeleton composed of, among others, ankyrinR and beta Rspectrin. In the present developmental study we showed that the intracellular markers parvalbumin, ankyrinR and beta Rspectrin as well as Vicia Villosa agglutinin, an extracellular marker for perineuronal nets, appeared in the second postnatal week. In the third postnatal week, ankyrinR and beta R spectrin were present in the parvalbumin-positive interneurons. Tenascin-R appeared in a similar topographic distribution as the intracellular markers. The adult pattern was established upon the end of the fourth postnatal week. Our results indicate that cytoskeletal maturity maybe a prerequisite for the organization of perineuronal nets of extracellular matrix.

Where is the economics in spatial econometrics?

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

The nuclear matrix: a critical appraisal.

It is becoming increasingly clear that the cell nucleus is a highly structurized organelle. Because of its tight compartmentalization, it is generally believed that a framework must exist, responsible for maintaining such a spatial organization. Over the last twenty years many investigations have been devoted to identifying the nuclear framework. Structures isolated by different techniques have been obtained in vitro and are variously referred to as nuclear matrix, nucleoskeleton or nuclear scaffold. Many different functions, such as DNA replication and repair, mRNA transcription, processing and transport have been described to occur in close association with these structures. However, there is still much debate as to whether or not any of these preparations corresponds to a nuclear framework that exists in vivo. In this article we summarize the most commonly-used methods for obtaining preparations of nuclear frameworks and we also stress the possible artifacts that can be created in vitro during the isolation procedures. Emphasis is placed also on the protein composition of the frameworks as well as on some possible signalling functions that have been recently described to occur in tight association with the nuclear matrix.

Optimization of spatial resolution for peripheral magnetic resonance angiography.

RATIONALE AND OBJECTIVES: To determine optimum spatial resolution when imaging peripheral arteries with magnetic resonance angiography (MRA). MATERIALS AND METHODS: Eight vessel diameters ranging from 1.0 to 8.0 mm were simulated in a vascular phantom. A total of 40 three-dimensional flash MRA sequences were acquired with incremental variations of fields of view, matrix size, and slice thickness. The accurately known eight diameters were combined pairwise to generate 22 "exact" degrees of stenosis ranging from 42% to 87%. Then, the diameters were measured in the MRA images by three independent observers and with quantitative angiography (QA) software and used to compute the degrees of stenosis corresponding to the 22 "exact" ones. The accuracy and reproducibility of vessel diameter measurements and stenosis calculations were assessed for vessel size ranging from 6 to 8 mm (iliac artery), 4 to 5 mm (femoro-popliteal arteries), and 1 to 3 mm (infrapopliteal arteries). Maximum pixel dimension and slice thickness to obtain a mean error in stenosis evaluation of less than 10% were determined by linear regression analysis. RESULTS: Mean errors on stenosis quantification were 8.8% +/- 6.3% for 6- to 8-mm vessels, 15.5% +/- 8.2% for 4- to 5-mm vessels, and 18.9% +/- 7.5% for 1- to 3-mm vessels. Mean errors on stenosis calculation were 12.3% +/- 8.2% for observers and 11.4% +/- 15.1% for QA software (P = .0342). To evaluate stenosis with a mean error of less than 10%, maximum pixel surface, the pixel size in the phase direction, and the slice thickness should be less than 1.56 mm2, 1.34 mm, 1.70 mm, respectively (voxel size 2.65 mm3) for 6- to 8-mm vessels; 1.31 mm2, 1.10 mm, 1.34 mm (voxel size 1.76 mm3), for 4- to 5-mm vessels; and 1.17 mm2, 0.90 mm, 0.9 mm (voxel size 1.05 mm3) for 1- to 3-mm vessels. CONCLUSION: Higher spatial resolution than currently used should be selected for imaging peripheral vessels.

Lutzomyia longipalpis abundance in the city of Posadas, northeastern Argentina: variations at different spatial scales

The distribution of Lutzomyia longipalpis is heterogeneous with a pattern of high abundance areas (HAAs) embedded in a matrix of low abundance areas (LAAs). The objective of this study was to describe the variability in the abundance of Lu. longipalpis at two different spatial levels and to analyse the relationship between the abundance and multiple environmental variables. Of the environmental variables analysed in each household, the condition that best explained the differences in vector abundance between HAA-LAA was the variable "land_grass", with greater average values in the peridomestic environments within the LAA, and the variables "#sp tree", "#pots" and "dist_water" that were higher in the HAA. Of the environmental variables analysed in the patches, the variable "unpaved_streets" was higher in the LAAs and the variable "prop_inf_dogs" was higher in the HAAs. An understanding of the main environmental variables that influence the vector distribution could contribute to the development of strategies for the prevention and control of visceral leishmaniasis (VL). This is the first work in which environmental variables are analysed at the micro-scale in urban areas at the southern edge of the current range of Lu. longipalpis. Our results represent a significant contribution to the understanding of the abundance of the vector in the peridomestic habitats of the region.

In vitro heat exposure induces a redistribution of nuclear matrix proteins in human K562 erythroleukemia cells.

By using both conventional and confocal laser scanning microscopy with three monoclonal antibodies recognizing nuclear matrix proteins we have investigated by means of indirect fluorescence whether an incubation of isolated nuclei at the physiological temperature of 37 degrees C induces a redistribution of nuclear components in human K562 erythroleukemia cells. Upon incubation of isolated nuclei for 45 min at 37 degrees C, we have found that two of the antibodies, directed against proteins of the inner matrix network (M(r) 125 and 160 kDa), gave a fluorescent pattern different from that observed in permeabilized cells. By contrast, the fluorescent pattern did not change if nuclei were kept at 0 degrees C. The difference was more marked in case of the 160-kDa polypeptide. The fluorescent pattern detected by the third antibody, which recognizes the 180-kDa nucleolar isoform of DNA topoisomerase II, was unaffected by heat exposure of isolated nuclei. When isolated nuclear matrices prepared from heat-stabilized nuclei were stained by means of the same three antibodies, it was possible to see that the distribution of the 160-kDa matrix protein no longer corresponded to that observable in permeabilized cells, whereas the fluorescent pattern given by the antibody to the 125-kDa polypeptide resembled that detectable in permeabilized cells. The 180-kDa isoform of topoisomerase II was still present in the matrix nucleolar remnants. We conclude that a 37 degrees C incubation of isolated nuclei induces a redistribution of some nuclear matrix antigens and cannot prevent the rearrangement in the spatial organization of one of these antigens that takes place during matrix isolation in human erythroleukemia cells. The practical relevance of these findings is discussed.

Spatial analysis of the relationship between mortality from cardiovascular and cerebrovascular disease and drinking water hardness.

Previously published scientific papers have reported a negative correlation between drinking water hardness and cardiovascular mortality. Some ecologic and case-control studies suggest the protective effect of calcium and magnesium concentration in drinking water. In this article we present an analysis of this protective relationship in 538 municipalities of Comunidad Valenciana (Spain) from 1991-1998. We used the Spanish version of the Rapid Inquiry Facility (RIF) developed under the European Environment and Health Information System (EUROHEIS) research project. The strategy of analysis used in our study conforms to the exploratory nature of the RIF that is used as a tool to obtain quick and flexible insight into epidemiologic surveillance problems. This article describes the use of the RIF to explore possible associations between disease indicators and environmental factors. We used exposure analysis to assess the effect of both protective factors--calcium and magnesium--on mortality from cerebrovascular (ICD-9 430-438) and ischemic heart (ICD-9 410-414) diseases. This study provides statistical evidence of the relationship between mortality from cardiovascular diseases and hardness of drinking water. This relationship is stronger in cerebrovascular disease than in ischemic heart disease, is more pronounced for women than for men, and is more apparent with magnesium than with calcium concentration levels. Nevertheless, the protective nature of these two factors is not clearly established. Our results suggest the possibility of protectiveness but cannot be claimed as conclusive. The weak effects of these covariates make it difficult to separate them from the influence of socioeconomic and environmental factors. We have also performed disease mapping of standardized mortality ratios to detect clusters of municipalities with high risk. Further standardization by levels of calcium and magnesium in drinking water shows changes in the maps when we remove the effect of these covariates.

The effect of sodium tetrathionate stabilization on the distribution of three nuclear matrix proteins in human K562 erythroleukemia cells.

Using both conventional fluorescence and confocal laser scanning microscopy we have investigated whether or not stabilization of isolated human erythroleukemic nuclei with sodium tetrathionate can maintain in the nuclear matrix the same spatial distribution of three polypeptides (M(r) 160 kDa and 125 kDa, previously shown to be components of the internal nuclear matrix plus the 180-kDa nucleolar isoform of DNA topoisomerase II) as seen in permeabilized cells. The incubation of isolated nuclei in the presence of 2 mM sodium tetrathionate was performed at 0 degrees C or 37 degrees C. The matrix fraction retained 20-40% of nuclear protein, depending on the temperature at which the chemical stabilization was executed. Western blot analysis revealed that the proteins studied were completely retained in the high-salt resistant matrix. Indirect immunofluorescence experiments showed that the distribution of the three antigens in the final matrix closely resembled that detected in permeabilized cells, particularly when the stabilization was performed at 37 degrees C. This conclusion was also strengthened by analysis of cells, isolated nuclei and the nuclear matrix by means of confocal laser scanning microscopy. We conclude that sodium tetrathionate stabilization of isolated nuclei does not alter the spatial distribution of some nuclear matrix proteins.

Testing Spatial Autocorrelation in Weighted Networks : the Modes Permutation Test

In a weighted spatial network, as specified by an exchange matrix, the variances of the spatial values are inversely proportional to the size of the regions. Spatial values are no more exchangeable under independence, thus weakening the rationale for ordinary permutation and bootstrap tests of spatial autocorrelation. We propose an alternative permutation test for spatial autocorrelation, based upon exchangeable spatial modes, constructed as linear orthogonal combinations of spatial values. The coefficients obtain as eigenvectors of the standardised exchange matrix appearing in spectral clustering, and generalise to the weighted case the concept of spatial filtering for connectivity matrices. Also, two proposals aimed at transforming an acessibility matrix into a exchange matrix with with a priori fixed margins are presented. Two examples (inter-regional migratory flows and binary adjacency networks) illustrate the formalism, rooted in the theory of spectral decomposition for reversible Markov chains.