Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

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.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Objective assessment of image quality in conventional and digital mammography taking into account dynamic range.

The goal of this work is to develop a method to objectively compare the performance of a digital and a screen-film mammography system in terms of image quality. The method takes into account the dynamic range of the image detector, the detection of high and low contrast structures, the visualisation of the images and the observer response. A test object, designed to represent a compressed breast, was constructed from various tissue equivalent materials ranging from purely adipose to purely glandular composition. Different areas within the test object permitted the evaluation of low and high contrast detection, spatial resolution and image noise. All the images (digital and conventional) were captured using a CCD camera to include the visualisation process in the image quality assessment. A mathematical model observer (non-prewhitening matched filter), that calculates the detectability of high and low contrast structures using spatial resolution, noise and contrast, was used to compare the two technologies. Our results show that for a given patient dose, the detection of high and low contrast structures is significantly better for the digital system than for the conventional screen-film system studied. The method of using a test object with a large tissue composition range combined with a camera to compare conventional and digital imaging modalities can be applied to other radiological imaging techniques. In particular it could be used to optimise the process of radiographic reading of soft copy images.

Structure and spatial variability of Pinus uncinata Ram. growth forms at two contrasting treeline ecotones in the Spanish Pyrenees.

Describimos la distribución espacial de individuos de Pinus uncinata clasificados según su forma de crecimiento y tamaño en dos ecotonos poco perturbados del límite altitudinal del árbol situados en los Pirineos Centrales españoles (Ordesa, O; Tessó, T). En cada sitio situamos una parcela rectangular (30 140 m), que incluía los límites del árbol y del bosque, y cuyo lado mayor seguía el gradiente altitudinal. En ambos sitios, los individuos vivos eran más grandes y tenían un mayor número de cohortes de acículas pendiente abajo. La distribución de las clases de individuos según su forma de crecimiento y tamaño en el sitio O seguía una secuencia de mayor tamaño pendiente abajo, desde abundantes individuos policórmicos arbustivos (krummholz) con pocas cohortes de acículas (1-3) hasta individuos arbóreos mayores unicórmicos con varias cohortes de acículas (4-12). Por el contrario, los cambios estructurales en el ecotono del sitio T fueron graduales y no siguieron de forma tan clara dicha secuencia

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.

Spatial distribution and biomass of aquatic rooted macrophytes and their relevance in the metabolism of a Mediterranean coastal lagoon

This work aims to characterise the current autotrophic compartment of the Albufera des Grau coastal lagoon (Menorca, Balearic Islands) and to assess the relationship between the submerged macrophytes and the limnological parameters of the lagoon. During the study period the submerged vegetation was dominated by the macrophyte Ruppia cirrhosa, which formed dense extensive meadows covering 79% of the surface. Another macrophyte species, Potamogeton pectinatus, was also observed but only forming small stands near the rushing streams. Macroalgae were only occasionally observed. Macrophyte biomass showed a clear seasonal trend, with maximum values in July. The biomass of R. cirrhosa achieved 1760 g DW m-2, the highest biomass ever reported for this species in the literature. The seasonal production-decomposition cycle of the macrophyte meadows appears to drive the nutrient dynamics and carbon fluxes in the lagoon. Despite the significant biomass accumulation and the absence of a washout of nutrients and organic matter to the sea, the lagoon did not experience a dystrophic collapse. These results indicate that internal metabolism is more important than exchange processes in the lagoon.

Role of prohormone convertases in pro-neuropeptide Y processing: coexpression and in vitro kinetic investigations.

Proneuropeptide Y (ProNPY) undergoes cleavage at a single dibasic site Lys38-Arg39 resulting in the formation of 1-39 amino acid NPY which is further processed successively by carboxypeptidase-like and peptidylglycine alpha-amidating monooxygenase enzymes. To investigate whether prohormone convertases are involved in ProNPY processing, a vaccinia virus derived expression system was used to coexpress recombinant ProNPY with each of the prohormone convertases PC1/3, PC2, furin, and PACE4 in Neuro2A and NIH 3T3 cell lines as regulated neuroendocrine and constitutive prototype cell lines, respectively. The analysis of processed products shows that only PC1/3 generates NPY in NIH 3T3 cells while both PC1/3 and PC2 are able to generate NPY in Neuro2A cells. The convertases furin and PACE4 are unable to process ProNPY in either cell line. Moreover, comparative in vitro cleavage of recombinant NPY precursor by the enzymes PC1/3, PC2 and furin shows that only PC1/3 and PC2 are involved in specific cleavage of the dibasic site. Kinetic studies demonstrate that PC1/3 cleaves ProNPY more efficiently than PC2. The main difference between the cleavage efficiency is observed in the Vmax values whereas no major difference is observed in Km values. In addition the cleavage by PC1/3 and PC2 of two peptides reproducing the dibasic cleavage site with different amino acid sequence lengths namely (20-49)-ProNPY and (28-43)-ProNPY was studied. These shortened ProNPY substrates, when recognized by the enzymes, are more efficiently cleaved than ProNPY itself. The shortest peptide is not cleaved by PC2 while it is by PC1/3. On the basis of these observations it is proposed, first, that the constitutive secreted NPY does not result from the cleavage carried out by ubiquitously expressed enzymes furin and PACE4; second, that PC1/3 and PC2 are not equipotent in the cleavage of ProNPY; and third, substrate peptide length might discriminate PC1/3 and PC2 processing activity.

Building hippocampal circuits to learn and remember: insights into the development of human memory

The hippocampal formation is essential for the processing of episodic memories for autobiographical events that happen in unique spatiotemporal contexts. Interestingly, before 2 years of age, children are unable to form or store episodic memories for recall later in life, a phenomenon known as infantile amnesia. From 2 to 7 years of age, there are fewer memories than predicted based on a forgetting function alone, a phenomenon known as childhood amnesia. Here, we discuss the postnatal maturation of the primate hippocampal formation with the goal of characterizing the development of the neurobiological substrates thought to subserve the emergence of episodic memory. Distinct regions, layers and cells of the hippocampal formation exhibit different profiles of structural and molecular development during early postnatal life. The protracted period of neuronal addition and maturation in the dentate gyrus is accompanied by the late maturation of specific layers in different hippocampal regions that are located downstream from the dentate gyrus, particularly CA3. In contrast, distinct layers in several hippocampal regions, particularly CA1, which receive direct projections from the entorhinal cortex, exhibit an early maturation. In addition, hippocampal regions that are more highly interconnected with subcortical structures, including the subiculum, presubiculum, parasubiculum and CA2, mature even earlier. These findings, together with our studies of the development of human spatial memory, support the hypothesis that the differential maturation of distinct hippocampal circuits might underlie the differential emergence of specific "hippocampus-dependent" memory processes, culminating in the emergence of episodic memory concomitant with the maturation of all hippocampal circuits.

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.