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We extend the analysis of the MAXIMA-1 cosmic microwave background (CMB) data to smaller angular scales. MAXIMA, a bolometric balloon experiment, mapped a 124 deg$^2$ region of the sky with 10\arcmin resolution at frequencies of 150, 240 and 410 GHz during its first flight. The original analysis, which covered the multipole range $36 \leq \ell \leq 785$, is extended to $\ell = 1235$ using data from three 150 GHz photometers in the fully cross-linked central 60 deg$^2$ of the map. The main improvement over the original analysis is the use of 3\arcmin square pixels in the calculation of the map. The new analysis is consistent with the original for $\ell < 785$. For $\ell > 785$, where inflationary models predict a third acoustic peak, the new analysis shows power with an amplitude of $56 \pm 7$ \microk at $\ell \simeq 850$ in excess to the average power of $42 \pm 3$ \microk in the range $441 < \ell < 785$.

The Earth Resources Laboratory of the NASA Stennis Space Center is a center of space related technology for Earth observations. It has assumed the task, in a joint effort with Jackson State University, to reach out to the science community and acquire information pertaining to characteristics of spatially oriented data processing.

Spatial correlations of the cubic 3-torus topology are analysed using the Planck 2013 data. The spatial-correlation method for detecting multiply connected spaces is based on the fact that positions on the cosmic microwave background (CMB) sky, that are separated by large angular distances, can be spatially much nearer according to a hypothesized topology. The comparison of the correlations computed with and without the assumed topology can reveal whether a promising topological candidate is found. The level of the expected correlations is estimated by CMB simulations of the cubic 3-torus topology and compared to those obtained from the Planck data. An interesting 3-torus configuration is discovered which possesses topological correlations of the magnitude found in the CMB simulations based on a toroidal universe. Although the spatial-correlation method has a high false-positive rate, it is striking that there exists an orientation of a cubic 3-torus cell, where correlations between points that are separated by large angular distances, mimic those of closely separated points.

This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data is prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

Urban rivers are of the most important reasons for flood inundation in cities. This study aimed to investigate the morphological effects of the Gorganrood River in Aq’ Qala City in a case of flood inundation on March 2019. The images of Sentinel-2 and Landsat-8 were downloaded for full flood monitoring. After preprocessing, the NDWI and MNDWI indices were applied on the images to extract the flood zones. The sinuosity index and the number and mean radius of meanders were calculated from Sallaq Yilqi village to Dogonchi village using Google Earth and AutoCAD. The NDWI index was appropriate to detect the permanent and clear waters, while the MNDWI index was appropriate to detect the muddy waters and high humidity terrains. The area of flooded zones which obtained from the MNDWI index was 88% more than those of NDWI index. The sinuosity index was 3.88 (sever meander) in the district of the Sallaq Yilqi village to the Yampi village. Destruction outer arc meanders and low radius of meanders and erosion of soils in upstream were caused overflow the flood in the Gorganrood channel. On the 20 th  March due to low channel width and very-low gradient of bed river (0.0002) from the Aq Tekeh Khan village to the Dogonchi village were caused slow velocity of flood stream, therefore the water level was raised and the city of Aq’ Qala were flooded. According to the flood maps obtained, the northern and southern of the Aq’ Qala city located at high-risk zone. Canalization of outer meanders curvature, planting the absorbent vegetation around the quantitative buffer zone of the Gorganrood river, widening and dredging the channel and modifying structure of channel on the northern plains of Gorganrood River can be prevented from flooding over the Aq’ Qala city.

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.

Introduction Understanding the neural dynamics underlying early psychosis (EP) is critical for advancing diagnosis and treatment. This study investigates the role of entropy—a measure of unpredictability or disorder—in characterizing neural activity in EP. By examining both temporal entropy—patterns of activity within specific brain regions and networks—and spatial entropy—patterns of inter-regional communication—we aim to identify distinct signatures of neural disorganization in EP patients compared to healthy controls. Entropy is a measure of the uncertainty or unpredictability of a system's state, but it can also reflect physical characteristics, with higher entropy often indicating greater disorder.1 While brain synchronization as measured by entropy may measure psychedelic modulation of consciousness2, less is known about entropy in psychotic disorders. Disruptions in functional connectivity, particularly involving front-limbic circuits and default mode network activity3, provide critical insights into the mechanisms of early psychosis and may inform novel hypotheses and interventions.4 Measures of spatial and temporal brain entropy may capture features of early psychosis, particularly positive symptomatology. We hypothesize that EP patients will exhibit higher temporal and spatial entropy, reflecting more disorganized neural dynamics. Further, we anticipate that the most pronounced entropy disruptions will occur in brain regions and networks associated with cognitive control and emotional processing. This study will contribute to the growing body of research linking neural entropy to psychopathology, providing potential biomarkers for EP and insights into the underlying mechanisms of disease. The results could inform precision medicine approaches, paving the way for more targeted interventions in psychosis. Aims Assess whether early psychosis (EP) patients exhibit different patterns of temporal entropy in specific brain regions and networks compared to healthy controls. Assess differences in spatial entropy of inter-regional communication patterns between EP patients and healthy controls. Test associations between entropy and clinical symptoms and cognitive function. References 1. Carhart-Harris RL, Leech R, Hellyer PJ, et al. The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Front Hum Neurosci. 2014;8. doi:10.3389/fnhum.2014.00020 2. Cruzat J, Perl YS, Escrichs A, et al. Effects of classic psychedelic drugs on turbulent signatures in brain dynamics. Network Neuroscience. 2022;6(4):1104-1124. doi:10.1162/netn_a_00250 3. Luppi AI, Craig MM, Pappas I, et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun. 2019;10(1):4616. doi:10.1038/s41467-019-12658-9 4. Carhart-Harris RL, Leech R, Erritzoe D, et al. Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis. Schizophrenia Bulletin. 2013;39(6):1343-1351. doi:10.1093/schbul/sbs117

Introduction Understanding the neural dynamics underlying early psychosis (EP) is critical for advancing diagnosis and treatment. This study investigates the role of entropy—a measure of unpredictability or disorder—in characterizing neural activity in EP. By examining both temporal entropy—patterns of activity within specific brain regions and networks—and spatial entropy—patterns of inter-regional communication—we aim to identify distinct signatures of neural disorganization in EP patients compared to healthy controls. Entropy is a measure of the uncertainty or unpredictability of a system's state, but it can also reflect physical characteristics, with higher entropy often indicating greater disorder.1 While brain synchronization as measured by entropy may measure psychedelic modulation of consciousness2, less is known about entropy in psychotic disorders. Disruptions in functional connectivity, particularly involving front-limbic circuits and default mode network activity3, provide critical insights into the mechanisms of early psychosis and may inform novel hypotheses and interventions.4 Measures of spatial and temporal brain entropy may capture features of early psychosis, particularly positive symptomatology. We hypothesize that EP patients will exhibit higher temporal and spatial entropy, reflecting more disorganized neural dynamics. Further, we anticipate that the most pronounced entropy disruptions will occur in brain regions and networks associated with cognitive control and emotional processing. This study will contribute to the growing body of research linking neural entropy to psychopathology, providing potential biomarkers for EP and insights into the underlying mechanisms of disease. The results could inform precision medicine approaches, paving the way for more targeted interventions in psychosis. Aims Assess whether early psychosis (EP) patients exhibit different patterns of temporal entropy in specific brain regions and networks compared to healthy controls. Assess differences in spatial entropy of inter-regional communication patterns between EP patients and healthy controls. Test associations between entropy and clinical symptoms and cognitive function. References 1. Carhart-Harris RL, Leech R, Hellyer PJ, et al. The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Front Hum Neurosci. 2014;8. doi:10.3389/fnhum.2014.00020 2. Cruzat J, Perl YS, Escrichs A, et al. Effects of classic psychedelic drugs on turbulent signatures in brain dynamics. Network Neuroscience. 2022;6(4):1104-1124. doi:10.1162/netn_a_00250 3. Luppi AI, Craig MM, Pappas I, et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun. 2019;10(1):4616. doi:10.1038/s41467-019-12658-9 4. Carhart-Harris RL, Leech R, Erritzoe D, et al. Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis. Schizophrenia Bulletin. 2013;39(6):1343-1351. doi:10.1093/schbul/sbs117

Introduction Understanding the neural dynamics underlying early psychosis (EP) is critical for advancing diagnosis and treatment. This study investigates the role of entropy—a measure of unpredictability or disorder—in characterizing neural activity in EP. By examining both temporal entropy—patterns of activity within specific brain regions and networks—and spatial entropy—patterns of inter-regional communication—we aim to identify distinct signatures of neural disorganization in EP patients compared to healthy controls. Entropy is a measure of the uncertainty or unpredictability of a system's state, but it can also reflect physical characteristics, with higher entropy often indicating greater disorder.1 While brain synchronization as measured by entropy may measure psychedelic modulation of consciousness2, less is known about entropy in psychotic disorders. Disruptions in functional connectivity, particularly involving front-limbic circuits and default mode network activity3, provide critical insights into the mechanisms of early psychosis and may inform novel hypotheses and interventions.4 Measures of spatial and temporal brain entropy may capture features of early psychosis, particularly positive symptomatology. We hypothesize that EP patients will exhibit higher temporal and spatial entropy, reflecting more disorganized neural dynamics. Further, we anticipate that the most pronounced entropy disruptions will occur in brain regions and networks associated with cognitive control and emotional processing. This study will contribute to the growing body of research linking neural entropy to psychopathology, providing potential biomarkers for EP and insights into the underlying mechanisms of disease. The results could inform precision medicine approaches, paving the way for more targeted interventions in psychosis. Aims Assess whether early psychosis (EP) patients exhibit different patterns of temporal entropy in specific brain regions and networks compared to healthy controls. Assess differences in spatial entropy of inter-regional communication patterns between EP patients and healthy controls. Test associations between entropy and clinical symptoms and cognitive function. References 1. Carhart-Harris RL, Leech R, Hellyer PJ, et al. The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Front Hum Neurosci. 2014;8. doi:10.3389/fnhum.2014.00020 2. Cruzat J, Perl YS, Escrichs A, et al. Effects of classic psychedelic drugs on turbulent signatures in brain dynamics. Network Neuroscience. 2022;6(4):1104-1124. doi:10.1162/netn_a_00250 3. Luppi AI, Craig MM, Pappas I, et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun. 2019;10(1):4616. doi:10.1038/s41467-019-12658-9 4. Carhart-Harris RL, Leech R, Erritzoe D, et al. Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis. Schizophrenia Bulletin. 2013;39(6):1343-1351. doi:10.1093/schbul/sbs117

Introduction Understanding the neural dynamics underlying early psychosis (EP) is critical for advancing diagnosis and treatment. This study investigates the role of entropy—a measure of unpredictability or disorder—in characterizing neural activity in EP. By examining both temporal entropy—patterns of activity within specific brain regions and networks—and spatial entropy—patterns of inter-regional communication—we aim to identify distinct signatures of neural disorganization in EP patients compared to healthy controls. Entropy is a measure of the uncertainty or unpredictability of a system's state, but it can also reflect physical characteristics, with higher entropy often indicating greater disorder.1 While brain synchronization as measured by entropy may measure psychedelic modulation of consciousness2, less is known about entropy in psychotic disorders. Disruptions in functional connectivity, particularly involving front-limbic circuits and default mode network activity3, provide critical insights into the mechanisms of early psychosis and may inform novel hypotheses and interventions.4 Measures of spatial and temporal brain entropy may capture features of early psychosis, particularly positive symptomatology. We hypothesize that EP patients will exhibit higher temporal and spatial entropy, reflecting more disorganized neural dynamics. Further, we anticipate that the most pronounced entropy disruptions will occur in brain regions and networks associated with cognitive control and emotional processing. This study will contribute to the growing body of research linking neural entropy to psychopathology, providing potential biomarkers for EP and insights into the underlying mechanisms of disease. The results could inform precision medicine approaches, paving the way for more targeted interventions in psychosis. Aims Assess whether early psychosis (EP) patients exhibit different patterns of temporal entropy in specific brain regions and networks compared to healthy controls. Assess differences in spatial entropy of inter-regional communication patterns between EP patients and healthy controls. Test associations between entropy and clinical symptoms and cognitive function. References 1. Carhart-Harris RL, Leech R, Hellyer PJ, et al. The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Front Hum Neurosci. 2014;8. doi:10.3389/fnhum.2014.00020 2. Cruzat J, Perl YS, Escrichs A, et al. Effects of classic psychedelic drugs on turbulent signatures in brain dynamics. Network Neuroscience. 2022;6(4):1104-1124. doi:10.1162/netn_a_00250 3. Luppi AI, Craig MM, Pappas I, et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun. 2019;10(1):4616. doi:10.1038/s41467-019-12658-9 4. Carhart-Harris RL, Leech R, Erritzoe D, et al. Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis. Schizophrenia Bulletin. 2013;39(6):1343-1351. doi:10.1093/schbul/sbs117

Wavelets are scaleable, oscillatory functions that deviate from zero only within a limited spatial regime and have average value zero. In addition to their use as source characterizers, wavelet functions are rapidly gaining currency within the source detection field. Wavelet-based source detection involves the correlation of scaled wavelet functions with binned, two-dimensional image data. If the chosen wavelet function exhibits the property of vanishing moments, significantly non-zero correlation coefficients will be observed only where there are high-order variations in the data; e.g., they will be observed in the vicinity of sources. In this paper, we describe the mission-independent, wavelet-based source detection algorithm WAVDETECT, part of the CIAO software package. Aspects of our algorithm include: (1) the computation of local, exposure-corrected normalized (i.e. flat-fielded) background maps; (2) the correction for exposure variations within the field-of-view; (3) its applicability within the low-counts regime, as it does not require a minimum number of background counts per pixel for the accurate computation of source detection thresholds; (4) the generation of a source list in a manner that does not depend upon a detailed knowledge of the point spread function (PSF) shape; and (5) error analysis. These features make our algorithm considerably more general than previous methods developed for the analysis of X-ray image data, especially in the low count regime. We demonstrate the algorithm's robustness by applying it to various images.

Simulation data used in the paper CC0 Waiver

The goal of this paper is to describe the application of quasi-likelihood estimating equations for spatially correlated binary data. In this paper, a logistic function is used to model the marginal probability of binary responses in terms of parameters of interest. With mild assumptions on the correlations, the Leonov-Shiryaev formula combined with a comparison of characteristic functions can be used to establish asymptotic normality for linear combinations of the binary responses. The consistency and asymptotic normality for quasi-likelihood estimates can then be derived. By modeling spatial correlation with a variogram, we apply these asymptotic results to test independence of two spatially correlated binary outcomes and illustrate the concepts with a well-known example based on data from Lansing Woods. The comparison of generalized estimating equations and the proposed approach is also discussed.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results: MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion: We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results: MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion: We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org.

In this work we analyze chronological data for lunar meteorites with emphasis on the spatial and temporal distribution of lunar mare basalts. The data are mostly from the Lunar Meteorite Compendium (http://www-curator.jsc.nasa.gov/antmet/lmc/contents.cfm cited thereafter as Compendium) compiled by Kevin Righter and from the associated literature.

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-based Meta-analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and a neuroimaging meta-analysis dataset.

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-based Meta-analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and a neuroimaging meta-analysis dataset.

Investigation of highly structured data sets to unveil statistical regularities is of major importance in complex system research. The first step is to choose the scale at which to observe the process, the most informative scale being the one that includes the important features while disregarding noisy details in the data. In the investigation of spatial patterns, the optimal scale defines the optimal bin size of the histogram in which to visualize the empirical density of the pattern. In this paper we investigate a method proposed recently by K.~H.~Knuth to find the optimal bin size of an histogram as a tool for statistical analysis of spatial point processes. We test it through numerical simulations on various spatial processes which are of interest in ecology. We show that Knuth optimal bin size rule reducing noisy fluctuations performs better than standard kernel methods to infer the intensity of the underlying process. Moreover it can be used to highlight relevant spatial characteristics of the underlying distribution such as space anisotropy and clusterization. We apply these findings to analyse cluster-like structures in plants' arrangement of Barro Colorado Island rainforest.

Investigation of highly structured data sets to unveil statistical regularities is of major importance in complex system research. The first step is to choose the scale at which to observe the process, the most informative scale being the one that includes the important features while disregarding noisy details in the data. In the investigation of spatial patterns, the optimal scale defines the optimal bin size of the histogram in which to visualize the empirical density of the pattern. In this paper we investigate a method proposed recently by K.~H.~Knuth to find the optimal bin size of an histogram as a tool for statistical analysis of spatial point processes. We test it through numerical simulations on various spatial processes which are of interest in ecology. We show that Knuth optimal bin size rule reducing noisy fluctuations performs better than standard kernel methods to infer the intensity of the underlying process. Moreover it can be used to highlight relevant spatial characteristics of the underlying distribution such as space anisotropy and clusterization. We apply these findings to analyse cluster-like structures in plants' arrangement of Barro Colorado Island rainforest.

Recurrence plot based methods are highly efficient and widely accepted tools for the investigation of time series or one-dimensional data. We present an extension of the recurrence plots and their quantifications in order to study recurrent structures in higher-dimensional spatial data. The capability of this extension is illustrated on prototypical 2D models. Next, the tested and proved approach is applied to assess the bone structure from CT images of human proximal tibia. We find that the spatial structures in trabecular bone become more self-similar during the bone loss in osteoporosis.

This article is from BMC Genomics , volume 15 . Abstract Background: Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a tool for selection of models for phenotypic data analysis. Results: Using complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models. Conclusions: Making predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed. Electronic supplementary material: The online version of this article (doi:10.1186/1471-2164-15-646) contains supplementary material, which is available to authorized users.

How do problem solvers represent visual-spatial information in complex problem solving tasks? This paper explores the predictions of embodied problem solving and a neurocomputational theory for what factors influence internal representation choices. Data are collected from experts and novices in three different, complex visual-spatial problem-solving domains (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities. Analyses of domain differences, expertise differences, and changes over time with problem solving suggest that neurocomputational constraints play a larger role than the nature of the visual input or the nature of the underlying real world being examined through problem solving, especially for expert problem solvers. The particular neurocomptuational feature that was found to drive internal representation choice is the required spatial precision of the main goals of problem solving.

Developing cultural information into cultural knowledge for military operations is predominantly an intelligence activity that takes place within the Military Decision Making Process. The products of such efforts are routinely classified and unusable by the tactical war fighter. The Actionable Cultural Understanding for Support to Tactical Operations (ACUSTO) research effort was undertaken to provide a product for enhanced cultural understanding that will be accessible to the tactical war fighter. This is done by combining spatial and explicit content analysis of open source news media to provide cultural understanding in the operational environment that can be disseminated down to the lowest tactical level. The development of actionable intelligence for counterinsurgency parallels the study of civilian criminal events (widely covered in open source media) and can exploit the methodological approaches that emphasize spatially explicit information. Crime research is conducted at aggregate levels, which implies the aggregation of a series of points representing events to areas representing higher scales. This work focused on data quality in point pattern analysis, and on the effect that different levels of data accuracy and precision have on policy recommendations.

No observation is ever exact. As a corollary, every observation contains error. These statements are fundamental and accepted universally. It follows logically, therefore, that surveyors, who are measurement specialists, should have a thorough understanding of errors. They must be familiar with the different types of errors, their sources, and their expected magnitudes. Armed with this knowledge, they will be able to (1) adopt procedures for reducing error sizes when making their measurements and (2) account rigorously for the presence of errors as they analyze and adjust their data. This book is devoted to creating a better understanding of these topics.

Cirrus is one of the most poorly quantified clouds. As a part of International Satellite Cloud Climatology Project (ISCCP), intensive observations of cirrus clouds were taken in the autumn of 1986 over Wisconsin. During this First ISCCP Regional Experiment Cirrus Intensive Field Observation (FIRE Cirrus IFO), coordinated measurements from satellite, aircraft, and ground-based platforms were made of cirrus clouds. This paper deals with the verification of cirrus cloud information, both spatial and radiative, obtained for a 1986 FIRE cloud scene using measurements from two independent sensors onboard the NOAA-9 polar orbiting satellite. In addition to the wide variability in properties common for other types of clouds, cirrus clouds have the added complexity of transmissivity t values that span the entire possible domain 0 &lt;= t &gt;= 1. Thus, uncertainties exist in thin cirrus cloud amount, altitude, thickness, and optical properties as retrieved from satellite because the measured cirrus signal is affected additionally by an unknown radiation component from below.

Microarray time course (MTC) gene expression data are commonly collected to study the dynamic nature of biological processes. One important problem is to identify genes that show different expression profiles over time and pathways that are perturbed during a given biological process. While methods are available to identify the genes with differential expression levels over time, there is a lack of methods that can incorporate the pathway information in identifying the pathways being modified/activated during a biological process. In this paper we develop a hidden spatial-temporal Markov random field (hstMRF)-based method for identifying genes and subnetworks that are related to biological processes, where the dependency of the differential expression patterns of genes on the networks are modeled over time and over the network of pathways. Simulation studies indicated that the method is quite effective in identifying genes and modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway structure or time dependency information, with similar false discovery rates. Application to a microarray gene expression study of systemic inflammation in humans identified a core set of genes on the KEGG pathways that show clear differential expression patterns over time. In addition, the method confirmed that the TOLL-like signaling pathway plays an important role in immune response to endotoxins.

ABSTRACT The lack of truly reliable data for climate change analyses and prediction presents challenges in climate modeling. Needed data are required to be hydrologically/statistically reliable to be useful for hydrological, meteorological, climate change, and estimation studies. Thus, data quality and homogeneity screening are preliminary analyses. In this study, the homogeneity of the climatic data used for analyses of climate variability was conducted in the coastal region of Nigeria. Climatic Research Unit (CRU 0.5× 0.5) gridded monthly climatic data for sixty years (1956- 2016) for nine states of the coastal region of Nigeria obtained from internet sources were validated with the Nigerian Meteorological Agency (NiMet) data to assure adequacy for use. The data were tested for normality using the Shapiro-Wilk (S-W) test, D’Agostino-Pearson omnibus test, and skewness test. Four homogeneity test methods were applied to 257 locations in the nine states of the coastal region of Nigeria and they include Pettit’s, Standard Normal Homogeneity Test (SNHT), Buishand’s and Von Neumann Ratio (VNR) tests. The results of the validity analysis indicated that the CRU data are very reliable and thus justified their use for the further analysis carried out in the study. Also, the results obtained indicated that CRU climatic data series were normally distributed and parametric methods could be used in further analysis of the data. Rainfall data homogeneity was detected for Bayelsa, Delta, Edo, Lagos, Ogun, and Ondo states and inhomogeneity for Akwa Ibom, Cross Rivers, and Rivers States. Also, temperature data inhomogeneity was detected for all the states in the study area.

ABSTRACT The lack of truly reliable data for climate change analyses and prediction presents challenges in climate modeling. Needed data are required to be hydrologically/statistically reliable to be useful for hydrological, meteorological, climate change, and estimation studies. Thus, data quality and homogeneity screening are preliminary analyses. In this study, the homogeneity of the climatic data used for analyses of climate variability was conducted in the coastal region of Nigeria. Climatic Research Unit (CRU 0.5× 0.5) gridded monthly climatic data for sixty years (1956- 2016) for nine states of the coastal region of Nigeria obtained from internet sources were validated with the Nigerian Meteorological Agency (NiMet) data to assure adequacy for use. The data were tested for normality using the Shapiro-Wilk (S-W) test, D’Agostino-Pearson omnibus test, and skewness test. Four homogeneity test methods were applied to 257 locations in the nine states of the coastal region of Nigeria and they include Pettit’s, Standard Normal Homogeneity Test (SNHT), Buishand’s and Von Neumann Ratio (VNR) tests. The results of the validity analysis indicated that the CRU data are very reliable and thus justified their use for the further analysis carried out in the study. Also, the results obtained indicated that CRU climatic data series were normally distributed and parametric methods could be used in further analysis of the data. Rainfall data homogeneity was detected for Bayelsa, Delta, Edo, Lagos, Ogun, and Ondo states and inhomogeneity for Akwa Ibom, Cross Rivers, and Rivers States. Also, temperature data inhomogeneity was detected for all the states in the study area.

The author has identified the following significant results. Computer-aided analysis techniques used with aircraft MSS data showed that the spatial resolution was sufficient to recognize each soil mapping unit of the test site. Some difficulties occurred where different soil series were intricately mixed, and this mixture showed as a separate spectral mapping unit, or where the difference between two soils depended on the depth of silty surface material. Analysis of LANDSAT data with computer-aided techniques showed that it was not possible to find spectrally homogeneous soil features of the seven soil series on the 40 ha test site on the digital display or on a picture print map. Cluster techniques could be used on an extended test area to group spectrally similar data points into cluster classes.

We discuss the problem of CMB spectrum corruption during Galactic emission removing. A new technique of spatial--frequency data reduction is proposed. The technique gives us a possibility to avoid a spatial harmonics nonorthogonality. The proposed technique is applied to the two-year COBE DMR sky maps. We exclude the harmonics with l=7, 9,13, 23 and 25 as having anomalous statistics noise behavior. One shows that procedure do not give systematic errors, if the data are statistically regular. The spectral parameter of the power spectrum of primordial perturbation $n=1.84 \pm 0.29$ and quadrupole moment $Q_2=15.22 \pm 3.0$ are estimated. The power spectrum estimation results are inconsistent with the Harrison-Zel`dovich $n=1$ model with the confidence 99%. It is shown a necessity of an increasing a survey sensitivity to reach a more reliable estimation of the cosmological signal.

It was hypothesized that the chronological observation of land use change could be shown to follow a predictable pattern and these patterns could be correlated with other statistical data to develop transition probabilities suitable for modeling purposes. A literature review and preliminary research, however, indicated a totally stochastic approach was not practical for simulating land use change and thus a more deterministic approach was adopted. The approach used assumes the determinants of the land use conversion process are found in the market place, where land transactions among buyers and sellers occur. Only one side of the market transaction process is studied, however, namely, the purchaser's desires in securing an ideal or suitable site. The problem was to identify the ideal qualities, quantities or attributes desired in an industrial site (or housing development), and to formulate a general algorithmic statement capable of identifying potential development sites. Research procedures involved developing a list of variables previously noted in the literature to be related to site selection and streamlining the list to a set suitable for statistical testing. A sample of 157 industries which have located (or relocated) in the 16-county Knoxville metropolitan region since 1950 was selected for industrial location analysis. Using NASA color infrared photography and Tennessee Valley Authority historical aerial photography, data were collected on the spatial characteristics of each industrial location event. These data were then subjected to factor analysis to determine the interrelations of variables.

It was hypothesized that the chronological observation of land use change could be shown to follow a predictable pattern and these patterns could be correlated with other statistical data to develop transition probabilities suitable for modeling purposes. A literature review and preliminary research, however, indicated a totally stochastic approach was not practical for simulating land use change and thus a more deterministic approach was adopted. The approach used assumes the determinants of the land use conversion process are found in the market place, where land transactions among buyers and sellers occur. Only one side of the market transaction process is studied, however, namely, the purchaser's desires in securing an ideal or suitable site. The problem was to identify the ideal qualities, quantities or attributes desired in an industrial site (or housing development), and to formulate a general algorithmic statement capable of identifying potential development sites. Research procedures involved developing a list of variables previously noted in the literature to be related to site selection and streamlining the list to a set suitable for statistical testing. A sample of 157 industries which have located (or relocated) in the 16-county Knoxville metropolitan region since 1950 was selected for industrial location analysis. Using NASA color infrared photography and Tennessee Valley Authority historical aerial photography, data were collected on the spatial characteristics of each industrial location event. These data were then subjected to factor analysis to determine the interrelations of variables.

We conduct a novel comprehensive investigation that seeks to prove the connection between spatial and time scales in surface soil moisture (SM) within the satellite footprint (approx. 50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at time scales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per time-scale basis by comparison to large-spatial scale datasets (the in situ spatial average, SMOS, AMSR2 and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), the percentage of correlated areas (CArea) and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the time scale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings and TC is suitable for week and month scales but not for other scales where datasets cross-correlations are found low. In contrast, WCor and CArea give consistent results at all time-scales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (1 station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.

Geospatial data, information, and technologies are becoming more important and more common tools throughout the world because of their capacity to improve government and private sector decision making. Geospatial information is developed, used, maintained and shared in a range of application areas, including: transportation, environment, natural resources, agriculture, telecommunications, mapping, health, emergency services, research, and national security. Sharing geospatial data in such applications helps improve the management of public infrastructures and natural resources and produces numerous other benefits. Many nations and regions around the world are developing Spatial Data Infrastructures (SDIs) to help facilitate cooperative production, use, and sharing of geospatial information. An SDI usually encompasses policies, standards, technologies and procedures for organizations to cooperatively produce and share geographic data. The Global Spatial Data Infrastructure (GSDI) is a fairly recent international collaboration that promotes the development of SDIs throughout the world. Like other SDIs, GSDI is focusing on the development of policies and processes to enable efficient geospatial data sharing and use. CSDI is being advanced through the leadership of many nations and organizations represented by a GSDI Steering Committee, which includes representatives from all continents, and from government, academia, and the private sector. At this point, GSDI consists mostly of dedicated volunteers, modeled on national SDIs, with small amounts of funding from various governments.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured brain electrical signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski. We decomposed eight fMRI data sets from 4 normal subjects performing various cognitive tasks. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and related fourth-order decomposition technique were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations.

The trauma data set was processed and analyzed by SPIRE. The data set was small and highly structured. Both of these facts were limiting factors in the SPIRE analysis. SPIRE accurately portrayed the information content; however, more research is needed for SPIRE to assess information from a small, structured data set.

During the initial development of micro Analysis & Design's (MA&D's) Spatial Disorientation Analysis Toll (SDAT), we received seven mishap data sets from the Air Force Safety Center (AFSC). AFSC simply told us that five of the data sets were definitely SD related, and two were probably SD related; we received no other details about the data. Our goal was to use SDAT to analyze the data sets, report our results to AFSC, and then compare our findings to the Safety Investigation Board's (SIB's) conclusions. SDAT does not account for other sensory inputs to orientation. Even though it is know that the human visual system is the primary source of orientation information, there is nothing in the research literature that quantifies the contribution of visual inputs. SADT results assume that the pilot is not attending to visual attitude information (which is often the case when SD mishaps occur) or that such information is not available.

Consistent and expedient evaluations of nonstructural flood loss reduction measures for existing and alternatives future land use patterns can be performed using spatial analysis concepts. The techniques developed center about the processing of spatial geographic data into a grid cell data bank and the subsequent accessing and manipulation of pertinent data variables by a computer program. The results are an automatically constructed elevation-damage function at damage reach index locations for selected land use patterns. The modification of the damage function resulting from specific nonstructural alternatives may be performed by inputting into the computer program a target protection level or a specified stage of protection for selected land use categories. These functions may be analyzed conventionally by damage frequency integration methods or used as input into more complex system models. (Author)

Directional data arise in various contexts such as oceanography (wave directions) and meteorology (wind directions), as well as with measurements on a periodic scale (weekdays, hours, etc.). Our contribution is to introduce a model-based approach to handle periodic data in the case of measurements taken at spatial locations, anticipating structured dependence between these measurements. We formulate a wrapped Gaussian spatial process model for this setting, induced from a customary linear Gaussian process. We build a hierarchical model to handle this situation and show that the fitting of such a model is possible using standard Markov chain Monte Carlo methods. Our approach enables spatial interpolation (and can accommodate measurement error). We illustrate with a set of wave direction data from the Adriatic coast of Italy, generated through a complex computer model.

In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at $p$ locations and $n$ time points with the possibility that $p>n$. While principal component analysis (PCA) is commonly applied to find the dominant patterns, the eigenimages produced from PCA may exhibit patterns that are too noisy to be physically meaningful when $p$ is large relative to $n$. To obtain more precise estimates of eigenimages, we propose a regularization approach incorporating smoothness and sparseness of eigenimages, while accounting for their orthogonality. Our method allows data taken at irregularly spaced or sparse locations. In addition, the resulting optimization problem can be solved using the alternating direction method of multipliers, which is easy to implement, and applicable to a large spatial dataset. Furthermore, the estimated eigenfunctions provide a natural basis for representing the underlying spatial process in a spatial random-effects model, from which spatial covariance function estimation and spatial prediction can be efficiently performed using a regularized fixed-rank kriging method. Finally, the effectiveness of the proposed method is demonstrated by several numerical examples

For statistical analysis of functional Magnetic Resonance Imaging (fMRI) data sets, we propose a data-driven approach based on Independent Component Analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attracting exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.

A common analytical problem in neuroscience is the interpretation of neural activity with respect to sensory input or behavioral output. This is typically achieved by regressing measured neural activity against known stimuli or behavioral variables to produce a "tuning function" for each neuron. Unfortunately, because this approach handles neurons individually, it cannot take advantage of simultaneous measurements from spatially adjacent neurons that often have similar tuning properties. On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons. In this paper, we develop a computationally efficient block Gibbs sampler that effectively pools information between neurons to de-noise tuning function estimates while simultaneously preserving sharp discontinuities that might exist in the organization of tuning across space. This method is fully Bayesian and its computational cost per iteration scales sub-quadratically with total parameter dimensionality. We demonstrate the robustness and scalability of this approach by applying it to both real and synthetic datasets. In particular, an application to data from the spinal cord illustrates that the proposed methods can dramatically decrease the experimental time required to accurately estimate tuning functions.

The digital SPOT image data from the study area chosen by TEC within Port Benning comprise three wavebands, near infrared, red and green. The standard vegetation index, NDVI, was computed from the first two channels. The pixel data for the four variables were mapped, and the summary statistics were computed. Wavebands 2 and 3 were strongly skewed and so the values weRe transformed to logarithms. Variograms were computed in two directions, along the rows and down the columns, for the three wavebands using both raw and transformed data, and for NDVI. Several mathematical models were fitted to the variograms. The double exponential model provided the best fit in all cases. The parameters of the models were different for the different directions and for the different variables. Two distinct scales of spatial variation are present in each case, a short range one Of about 9 pixels and a long range one of over a hundred pixelS. The scale of variation is greater in the N-S direction. Based on these results we have recommended a reconnaissance sampling plan comprising a series of transects over the study area for obtaining field data to correlate with the image. (AN)

Standard multivariate analysis methods aim to identify and summarize the main structures in large data sets containing the description of a number of observations by several variables. In many cases, spatial information is also available for each observation, so that a map can be associated to the multivariate data set. Two main objectives are relevant in the analysis of spatial multivariate data: summarizing covariation structures and identifying spatial patterns. In practice, achieving both goals simultaneously is a statistical challenge, and a range of methods have been developed that offer trade-offs between these two objectives. In an applied context, this methodological question has been and remains a major issue in community ecology, where species assemblages (i.e., covariation between species abundances) are often driven by spatial processes (and thus exhibit spatial patterns). In this paper we review a variety of methods developed in community ecology to investigate multivariate spatial patterns. We present different ways of incorporating spatial constraints in multivariate analysis and illustrate these different approaches using the famous data set on moral statistics in France published by Andr\'{e}-Michel Guerry in 1833. We discuss and compare the properties of these different approaches both from a practical and theoretical viewpoint.

Standard multivariate analysis methods aim to identify and summarize the main structures in large data sets containing the description of a number of observations by several variables. In many cases, spatial information is also available for each observation, so that a map can be associated to the multivariate data set. Two main objectives are relevant in the analysis of spatial multivariate data: summarizing covariation structures and identifying spatial patterns. In practice, achieving both goals simultaneously is a statistical challenge, and a range of methods have been developed that offer trade-offs between these two objectives. In an applied context, this methodological question has been and remains a major issue in community ecology, where species assemblages (i.e., covariation between species abundances) are often driven by spatial processes (and thus exhibit spatial patterns). In this paper we review a variety of methods developed in community ecology to investigate multivariate spatial patterns. We present different ways of incorporating spatial constraints in multivariate analysis and illustrate these different approaches using the famous data set on moral statistics in France published by Andr\'{e}-Michel Guerry in 1833. We discuss and compare the properties of these different approaches both from a practical and theoretical viewpoint.

This article is from International Journal of Health Geographics , volume 12 . Abstract Background: Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Methods: Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Results: Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these “hotspots”. Conclusions: Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

A high degree of accuracy was obtained in measuring the spatial frequency spectrum of known samples using an optical data processor based on a microprocessor, which reliably collected intensity versus angle data. Stray light control, system alignment, and angle measurement problems were addressed and solved. The capabilities of the instrument were extended by the addition of appropriate optics to allow the use of different wavelengths of laser radiation and by increasing the travel limits of the rotating arm to + or - 160 degrees. The acquisition, storage, and plotting of data by the computer permits the researcher a free hand in data manipulation such as subtracting background scattering from a diffraction pattern. Tests conducted to verify the operation of the processor using a 25 mm diameter pinhole, a 39.37 line pairs per mm series of multiple slits, and a microscope slide coated with 1.091 mm diameter polystyrene latex spheres are described.