Spatial and temporal variability of crop yield and some Rhodic Hapludox properties under no-tillage

Soil properties are closely related with crop production and spite of the measures implemented, spatial variation has been repeatedly observed and described. Identifying and describing spatial variations of soil properties and their effects on crop yield can be a powerful decision-making tool in specific land management systems. The objective of this research was to characterize the spatial and temporal variations in crop yield and chemical and physical properties of a Rhodic Hapludox soil under no-tillage. The studied area of 3.42 ha had been cultivated since 1985 under no-tillage crop rotation in summer and winter. Yield and soil property were sampled in a regular 10 x 10 m grid, with 302 sample points. Yields of several crops were analyzed (soybean, maize, triticale, hyacinth bean and castor bean) as well as soil chemical (pH, Soil Organic Matter (SOM), P, Ca2+, Mg2+, H + Al, B, Fe, Mn, Zn, CEC, sum of bases (SB), and base saturation (V %)) and soil physical properties (saturated hydraulic conductivity, texture, density, total porosity, and mechanical penetration resistance). Data were analyzed using geostatistical analysis procedures and maps based on interpolation by kriging. Great variation in crop yields was observed in the years evaluated. The yield values in the Northern region of the study area were high in some years. Crop yields and some physical and soil chemical properties were spatially correlated.

Visuo-spatial processing in a dynamic and a static working memory paradigm in schizophrenia.

Recent findings suggest that the visuo-spatial sketchpad (VSSP) may be divided into two sub-components processing dynamic or static visual information. This model may be useful to elucidate the confusion of data concerning the functioning of the VSSP in schizophrenia. The present study examined patients with schizophrenia and matched controls in a new working memory paradigm involving dynamic (the Ball Flight Task - BFT) or static (the Static Pattern Task - SPT) visual stimuli. In the BFT, the responses of the patients were apparently based on the retention of the last set of segments of the perceived trajectory, whereas control subjects relied on a more global strategy. We assume that the patients' performances are the result of a reduced capacity in chunking visual information since they relied mainly on the retention of the last set of segments. This assumption is confirmed by the poor performance of the patients in the static task (SPT), which requires a combination of stimulus components into object representations. We assume that the static/dynamic distinction may help us to understand the VSSP deficits in schizophrenia. This distinction also raises questions about the hypothesis that visuo-spatial working memory can simply be dissociated into visual and spatial sub-components.

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.

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.

How single-trial electrical neuroimaging contributes to multisensory research.

This study details a method to statistically determine, on a millisecond scale and for individual subjects, those brain areas whose activity differs between experimental conditions, using single-trial scalp-recorded EEG data. To do this, we non-invasively estimated local field potentials (LFPs) using the ELECTRA distributed inverse solution and applied non-parametric statistical tests at each brain voxel and for each time point. This yields a spatio-temporal activation pattern of differential brain responses. The method is illustrated here in the analysis of auditory-somatosensory (AS) multisensory interactions in four subjects. Differential multisensory responses were temporally and spatially consistent across individuals, with onset at approximately 50 ms and superposition within areas of the posterior superior temporal cortex that have traditionally been considered auditory in their function. The close agreement of these results with previous investigations of AS multisensory interactions suggests that the present approach constitutes a reliable method for studying multisensory processing with the temporal and spatial resolution required to elucidate several existing questions in this field. In particular, the present analyses permit a more direct comparison between human and animal studies of multisensory interactions and can be extended to examine correlation between electrophysiological phenomena and behavior.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks

This paper presents a statistical model for the quantification of the weight of fingerprint evidence. Contrarily to previous models (generative and score-based models), our model proposes to estimate the probability distributions of spatial relationships, directions and types of minutiae observed on fingerprints for any given fingermark. Our model is relying on an AFIS algorithm provided by 3M Cogent and on a dataset of more than 4,000,000 fingerprints to represent a sample from a relevant population of potential sources. The performance of our model was tested using several hundreds of minutiae configurations observed on a set of 565 fingermarks. In particular, the effects of various sub-populations of fingers (i.e., finger number, finger general pattern) on the expected evidential value of our test configurations were investigated. The performance of our model indicates that the spatial relationship between minutiae carries more evidential weight than their type or direction. Our results also indicate that the AFIS component of our model directly enables us to assign weight to fingerprint evidence without the need for the additional layer of complex statistical modeling involved by the estimation of the probability distributions of fingerprint features. In fact, it seems that the AFIS component is more sensitive to the sub-population effects than the other components of the model. Overall, the data generated during this research project contributes to support the idea that fingerprint evidence is a valuable forensic tool for the identification of individuals.

Spatial Exploration of Age Distribution in Catalan Municipalities

This paper takes the shelf and digs into the complex population’s age structure of Catalan municipalities for the year 2009. Catalonia is a very heterogeneous territory, and age pyramids vary considerably across different areas of the territory, existing geographical factors shaping municipalities’ age distributions. By means of spatial statistics methodologies, this piece of research tries to assess which spatial factors determine the location, scale and shape of local distributions. The results show that there exist different distributional patterns across the geography according to specific local determinants. Keywords: Spatial Models. JEL Classification: C21.

Inversion of multiple intersecting high-resolution crosshole GPR profiles for hydrological characterization at the Boise Hydrogeophysical Research Site

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling.

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Postnatal intracerebroventricular administrations of NGF alter spatial memory in adulthood

The present work assessed the effects of intracerebroventricular injections (2x5 mg/2.5 ml) of recombined human nerve growth factor (rhNGF) at postnatal days 2 and 3 upon the development of spatial learning capacities in rats. The treated rats were trained at the age of 22 days to escape onto an invisible platform at a fixed position in space in a Morris navigation task. For half of the subjects, the training position was also cued, a procedure aimed at facilitating escape and reducing attention to the distant spatial cues. At the age of 2 months all the rats were retrained in the same task. Treatment effects were found in both immature and adult rats. The injection of NGF induced a slight alteration of the immature rats' performance. In contrast, a marked impairment of spatial abilities was shown in the 2-month-old rats. The most consistent effects were a significant increase in the escape latency and a decrease bias towards the training platform area during probe trials. The reduction of spatial memory was particularly marked if the subjects had been trained in a cued condition. Taken together, these experiments reveal that an acute pharmacological treatment that leads to transient modifications during early development might induce a behavioural change long after treatment. Thus, the development and the maintenance of an accurate spatial representation are tightly related to the development of brain structures that could be altered by precocious NGF administrations.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

Revisiting spatial distribution and biochemical composition of calcium-containing crystals in human osteoarthritic articular cartilage.

INTRODUCTION: Calcium-containing (CaC) crystals, including basic calcium phosphate (BCP) and calcium pyrophosphate dihydrate (CPP), are associated with destructive forms of osteoarthritis (OA). We assessed their distribution and biochemical and morphologic features in human knee OA cartilage. METHODS: We prospectively included 20 patients who underwent total knee replacement (TKR) for primary OA. CaC crystal characterization and identification involved Fourier-transform infra-red spectrometry and scanning electron microscopy of 8 to 10 cartilage zones of each knee, including medial and lateral femoral condyles and tibial plateaux and the intercondyle zone. Differential expression of genes involved in the mineralization process between cartilage with and without calcification was assessed in samples from 8 different patients by RT-PCR. Immunohistochemistry and histology studies were performed in 6 different patients. RESULTS: Mean (SEM) age and body mass index of patients at the time of TKR was 74.6 (1.7) years and 28.1 (1.6) kg/m², respectively. Preoperative X-rays showed joint calcifications (chondrocalcinosis) in 4 cases only. The medial femoro-tibial compartment was the most severely affected in all cases, and mean (SEM) Kellgren-Lawrence score was 3.8 (0.1). All 20 OA cartilages showed CaC crystals. The mineral content represented 7.7% (8.1%) of the cartilage weight. All patients showed BCP crystals, which were associated with CPP crystals for 8 joints. CaC crystals were present in all knee joint compartments and in a mean of 4.6 (1.7) of the 8 studied areas. Crystal content was similar between superficial and deep layers and between medial and femoral compartments. BCP samples showed spherical structures, typical of biological apatite, and CPP samples showed rod-shaped or cubic structures. The expression of several genes involved in mineralization, including human homolog of progressive ankylosis, plasma-cell-membrane glycoprotein 1 and tissue-nonspecific alkaline phosphatase, was upregulated in OA chondrocytes isolated from CaC crystal-containing cartilages. CONCLUSIONS: CaC crystal deposition is a widespread phenomenon in human OA articular cartilage involving the entire knee cartilage including macroscopically normal and less weight-bearing zones. Cartilage calcification is associated with altered expression of genes involved in the mineralisation process.

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.