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Jejunocolic anastomosis (JCA) is a surgical procedure that can result in various complications. Depending on how much of the jejunum remains, these patients are often defined as having intestinal failure (IF) because of short bowel syndrome (SBS). One of the significant complications among these patients is the formation of calcium oxalate renal stones. It has been estimated that 24% of patients with a JCA develop symptomatic renal stones. This narrative review aims to explore the nursing and dietary management strategies for individuals with oxalate stones following JCA. This includes providing adequate hydration, limiting dietary intakes of fat and oxalate, and optimising dietary intake of fluid and calcium whilst discussing the evidence for dietary magnesium, vitamin C and citric acid.

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence–ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.

This study develops a spatial additive mixed modeling (AMM) approach estimating spatial and non-spatial effects from large samples, such as millions of observations. Although fast AMM approaches are already well established, they are restrictive in that they assume a known spatial dependence structure. To overcome this limitation, this study develops a fast AMM with the estimation of spatial structure in residuals and regression coefficients together with non-spatial effects. We rely on a Moran coefficient-based approach to estimate the spatial structure. The proposed approach pre-compresses large matrices whose size grows with respect to the sample size Nbefore the model estimation; thus, the computational complexity for the estimation is independent of the sample size. Furthermore, the pre-compression is done through a block-wise procedure that makes the memory consumption independent of N. Eventually, the spatial AMM is memory free and fast even for millions of observations. The developed approach is compared to alternatives through Monte Carlo simulation experiments. The result confirms the estimation accuracy of the spatially varying coefficients and group coefficients, and computational efficiency of the developed approach. Finally, we apply our approach to an income analysis using United States (US) data in 2015.

Griffith, D. A. (2013.) -Better Articulating Normal Curve Theory for Introductory Mathematical Statistics Students: Power Transformations and Their Back-Transformations,' The American Statistician, 67, 157-169: Comment by Mukhopadhyay and Reply In [...]

In immature oocytes, Balbiani bodies are conserved membraneless condensates implicated in oocyte polarization, the organization of mitochondria, and long-term organelle and RNA storage. In Xenopus laevis, Balbiani body assembly is mediated by the protein Velo1. Velo1 contains an N-terminal prion-like domain (PLD) that is essential for Balbiani body formation. PLDs have emerged as a class of intrinsically disordered regions that can undergo various different types of intracellular phase transitions and are often associated with dynamic, liquid-like condensates. Intriguingly, the Velo1 PLD forms solid-like assemblies. Here we sought to understand why Velo1 phase behavior appears to be biophysically distinct from that of other PLD-containing proteins. Through bioinformatic analysis and coarse-grained simulations, we predict that the clustering of aromatic residues and the amino acid composition of residues between aromatics can influence condensate material properties, organization, and the driving forces for assembly. To test our predictions, we redesigned the Velo1 PLD to test the impact of targeted sequence changes in vivo.We found that the Velo1 design with evenly spaced aromatic residues shows rapid internal dynamics, as probed by fluorescent recovery after photobleaching, even when recruited into Balbiani bodies. Our results suggest that Velo1 might have been selected in evolution for distinctly clustered aromatic residues to maintain the structure of Balbiani bodies in long-lived oocytes. In general, our work identifies several tunable parameters that can be used to augment the condensate material state, offering a road map for the design of synthetic condensates.

HELD: Only the contemporaneous refusal of treatment by a fully informed, competent adult patient is sufficient to override evidence of medical necessity, and no action lies for battery for treating [...]

HELD: A person with an incurable brain disease, which will result in a persistent vegetative state and ultimately death, does not have a constitutional right to either premortem cryogenic suspension [...]

HELD: The right of a patient in a persistent unconscious condition to refuse life-sustaining treatment such as tube-feeding can be effectuated by court order if the court is presented with [...]

Sue Ann Lawrance was a forty-two year old, never-competent woman diagnosed as being in a persistent vegetative state as the result of a full in 1987. In re Sue Ann [...]

The Best Interests Standard: A Comparison of the State's Parens Patriae Authority and Judicial Oversight in Best Interests Determinations for Children and Incompetent Patients The "best interests of the child" [...]

The most common measure of statistical correlation is between two variables for the same set of observations. A variant of this concept is serial correlation, which references the correlation among […]

A synthesis of the 2-azatricyclo[4.3.2.04,9]undecane ring system—a hitherto unreported bridged azatricyclic ring system—beginning from tricarbonyl(tropone)iron and allylamine was accomplished in three steps: (1) aza-Michael addition of allylamine to tricarbonyl(tropone)iron; (2) Boc-protection of the resulting secondary amine; and (3) oxidative demetallation leading to a spontaneous intramolecular Diels–Alder reaction. The effect of a variety of parameters on the intramolecular Diels–Alder reaction was investigated, including diene and dienophile substitution patterns and dienophile tether length.

ABSTRACTSpace-time kernel density estimation (STKDE) commonly is used for space-time cluster detection. But, this technique might be limited because it does not take into account an underlying population at risk for observed events. A space-time relative risk function (STRRF) can help overcome this limitation by allowing a comparison of each kernel density of observations with that of controls. This paper proposes a cross-STRRF to identify spatio-temporal locations that experience statistically significant changes in their density of events. With events organized in a space-time voxel structure, the cross-STRRF evaluates space-time patterns by comparing event occurrences at a spatial location in a previous time period with ones in its future as well as with its spatial neighbors in its contemporaneous time period. The test statistics of the cross-STRRF values in each voxel are obtained with a permutation test in which cases and controls are shuffled within each time period to maintain the space-time envelope of events. An application to assault crime incidents in the city of Plano, Texas between 2008 and 2012 illustrates that the cross-STRRF and its significance test results emphasize spatio-temporal changes in event density rather than constantly focusing on high density regions, which STKDE does.

Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers. Understanding and identifying changes in plant diversity along broad environmental gradients requires scalable and reliable data. Spectroscopy has been shown to provide data across scales with the ability to measure plant reflectance at various extents (e.g., leaf, plot, and landscapes) with high spectral resolution and broad coverage of the electromagnetic spectrum (350–2,500 nm). In grasses, evolutionary lineage captures major axes in plant biodiversity and functional variation. We show that identifying grass evolutionary lineages from spectroscopy is possible based on common characteristics in their leaf‐level spectra. We classified 43 grass species from four sites in North America into their respective evolutionary lineages with very high accuracy (>90%) based on similarities in their leaf spectra. Classifying grass species into lineages using coarser spectral resolution data, similar to existing multispectral satellites, Sentinel 2 and Landsat 8, resulted in lower accuracy due to a loss of information from decreasing the spectral resolution. Grass lineages likely have similar spectra because of common leaf traits that evolved under similar ecological contexts. The importance of these distinctions found in the spectral reflectance of dominant grass lineages, should help our efforts in mapping and understanding grassland ecosystem function and patterns of biodiversity. Globally dominant grass lineages are identifiable using hyperspectral leaf signaturesKey wavelengths for separating grass lineages were visible, red‐edge and shortwave infrared regions, rarely measured by multispectral sensorsHyperspectral sensors have the potential to improve remote sensing identification of grass functional types over multispectral sensors Globally dominant grass lineages are identifiable using hyperspectral leaf signatures Key wavelengths for separating grass lineages were visible, red‐edge and shortwave infrared regions, rarely measured by multispectral sensors Hyperspectral sensors have the potential to improve remote sensing identification of grass functional types over multispectral sensors

The dual achievements of this paper are: establishing that the Getis spatial filtering technique can uncover latent PSA–NSA mixtures, and uncovering this very mixture property in geospatial agricultural datasets, acknowledging omitted variable complications attributable to its presence. This methodological extension derives from published comments by Getis himself, whereas this agricultural data category augments the existing set comprising georeferenced socio-economic/demographic and disease data. Puerto Rico space–time datasets—for milk, plantain, and sugarcane production—constitute the analyzed empirical specimens, adding consistency across sequential periods in time to the current repertoire of already recognized focal data features that include geographic resolution and scale as well as geographic landscape diversity. This paper also presents comparisons between the proposed novel Getis spatial filtering decomposition with both spatial autoregressive and Moran eigenvector spatial filtering specifications, credibly concluding that, to some degree, all are capable of identifying PSA–NSA mixtures in geotagged data. Its other prominent general conclusion is that PSA–NSA mixtures tend to be latent in geospatial agricultural datasets.

ABSTRACTWith advances in spatial data management technologies, accurate geographic information about individual patients increasingly has become available. Researchers should protect the privacy of patients, which includes their locational information, in public health data analyses. Protecting privacy involves a trade-off between information loss and disclosure risk. Estimation of a kernel density surface commonly has been used to mask confidential point locations. However, the literature lacks an extensive discussion of reverse transformations from a kernel density estimation surface to points, and evaluations of recovered points compared to their original point counterparts. This paper presents a method to recover relatively precise point locations from a kernel density estimation surface using geometric centres of clusters, and evaluates recovered points in terms of protecting locational privacy and maintaining locational accuracy. An application illustrates this method utilizing late-stage colorectal cancer points in the Pensacola metropolitan statistical area, Florida that examines various kernel density estimation surfaces with different bandwidths and cell sizes.

Thiamine plays a pivotal role in energy metabolism. The aim of the study was to determine serial whole blood TPP concentrations in critically ill patients receiving chronic diuretic treatment before ICU admission and to correlate TPP levels with clinically determined serum phosphorus concentrations.

Background: Chemotherapy and radiation therapy result in increased free radical formation and depletion of tissue antioxidants. It is not known whether parenteral nutrition (PN) administered during bone marrow transplantation (BMT) supports systemic antioxidant status. Objective: The aims of the study were to determine 1) whether high-dose chemotherapy decreases concentrations of major circulating antioxidants in patients undergoing BMT and 2) whether administration of standard PN maintains systemic antioxidant concentrations compared with PN containing micronutrients and minimal lipids alone. Design: Twenty-four BMT patients were randomly assigned to receive either standard PN containing conventional amounts of dextrose, amino acids, micronutrients, and lipid (120 kJ/d) or a solution containing only micronutrients (identical to those in standard PN) and a small amount of lipid (12 kJ/d). Plasma antioxidant status was measured before conditioning therapy and serially at days 1, 3, 7, 10, and 14 after BMT. Results: Plasma glutathione (GSH) and [Alpha]- and [Gamma]-tocopherol concentrations decreased and the GSH redox state became more oxidized after conditioning chemotherapy. Plasma cysteine concentrations were unchanged, whereas cystine concentrations increased. Plasma vitamin C and zinc concentrations and GSH peroxidase activity increased over time. Plasma [Alpha]-tocopherol concentrations were lower in patients given standard PN. There were no differences in other plasma antioxidants between groups. Conclusions: A significant decline in GSH-glutathione disulfide, cysteine-cystine, and vitamin E status occurs after chemotherapy and BMT. Standard PN does not improve antioxidant status compared with administration of micronutrients alone. Further evaluation of PN formulations to support patients undergoing high-dose chemotherapy and BMT are needed. Am J Clin Nutr 2000;72:181-9. KEY WORDS Parenteral nutrition, antioxidants, glutathione, bone marrow transplantation, cysteine

This article describes how modeling positional uncertainty helps to understand potential factors of uncertainty, and to identify impacts of uncertainty on spatial analysis results. However, modeling geocoding positional uncertainty still is limited in providing a comprehensive explanation about these impacts, and requires further investigation of potential factors to enhance understanding of uncertainty. Furthermore, spatial autocorrelation among geocoded points has been barely considered in this type of modeling, although the presence of spatial autocorrelation is recognized in the literature. The purpose of this article is to extend the discussion about modeling geocoding positional uncertainty by investigating potential factors with regression, whose model is appropriately specified to account for spatial autocorrelation. The analysis results for residential addresses in Volusia County, Florida reveal covariates that are significantly associated with uncertainty in geocoded points. In addition, these results confirm that spatial autocorrelation needs to be accounted for when modeling positional uncertainty.

Evolutionary relatedness underlies patterns of functional diversity in the natural world. Hyperspectral remote sensing has the potential to detect these patterns in plants through inherited patterns of leaf reflectance spectra. We collected leaf reflectance data across the California flora from plants grown in a common garden. Regions of the reflectance spectra vary in the depth and strength of phylogenetic signal. We also show that these differences are much greater than variation due to the geographic origin of the plant. At the phylogenetic extent of the California flora, spectral variation explained by the combination of ecotypic variation (divergent evolution) and convergent evolution of disparate lineages was minimal (3%–7%) but statistically significant. Interestingly, at the extent of a single genus (Arctostaphylos) no unique variation could be attributed to geographic origin. However, up to 18% of the spectral variation among Arctostaphylosindividuals was shared between phylogeny and intraspecific variation stemming from ecotypic differences (i.e., geographic origin). Future studies could conduct more structured experiments (e.g., transplants or observations along environmental gradients) to disentangle these sources of variation and include other intraspecific variation (e.g., plasticity). We constrain broad‐scale spectral variability due to ecotypic sources, providing further support for the idea that phylogenetic clusters of species might be detectable through remote sensing. Phylogenetic clusters could represent a valuable dimension of biodiversity monitoring and detection. Related plant species often share inherited features, and we show that this is the case for the way the plant leaves reflect certain wavelengths of light. We collected leaf reflectance data from plants in a common garden where plants from across California are grown. Evolution explained more of the reflectance qualities of leaves than geographic origin of the plant species. These results provide support for the idea that remote sensing could help monitor biodiversity change by detecting groups of related plants. We collected leaf reflectance data from across the California flora from plants grown in a common gardenDifferent parts of the spectra capture deeper evolutionary differences among plants and are more important than geographic originThis provides support for the idea that phylogenetic clusters of species might be detectable through remote sensing We collected leaf reflectance data from across the California flora from plants grown in a common garden Different parts of the spectra capture deeper evolutionary differences among plants and are more important than geographic origin This provides support for the idea that phylogenetic clusters of species might be detectable through remote sensing

Lay rescuers play a pivotal role in the recognition and initial management of out-of-hospital cardiac arrest. The provision of timely pre-arrival care by lay responders, including CPR and use of an AED prior to EMS arrival, are important links in the chain of survival and have been shown to improve outcomes from cardiac arrest. While physicians are not directly involved in bystander response to cardiac arrest, they play a key role in emphasizing the importance of bystander interventions. It is critical for the health care community to advocate for OHCA systems of care, support bystander resuscitation efforts, and encourage education and training for lay providers. Furthermore, improving outcomes for this high-risk population requires a commitment to ongoing research efforts.

Popular spatial autocorrelation (SA) indices employed in spatial econometrics include the Moran Coefficient (MC), the Geary Ratio, (GR) and the join count statistics (JCS). Properties of these first two quantities rely on spatial weights matrix definitions [e.g., binary 0–1 (rook or queen adjacencies), nearest neighbors, inverse inter-point distance, row standardized], which may cause confusion about output from different software packages; to date, JCS calculations have been using only binary 0–1 definitions. The MC and GR expected values for linear regression residuals also merit closer examination; although the mean and other details of the sampling distribution for the MC are well-known, at least the details of those for the GR are not. The (MC + GR) sum furnishes a potential diagnostic for georeferenced data normality, one that warrants much further explication and scrutiny. The Moran scatterplot is a widely used graphic tool for visualizing SA; this paper formally introduces its Geary scatterplot counterpart (first appearing informally in 2019), together with some comparisons of the two. Meanwhile, established relationships between the JCS and the MC and the GR need additional inspection, too, especially in terms of their sampling variances. Preliminary analyses summarized in this paper also address derived asymptotic properties as well as links with the single spatial autoregressive parameter of the simultaneous autoregressive (SAR; spatial error) and autoregressive response (AR; spatial lag) model specifications. This paper describes selected little-known features of these standard SA indices, furthering a better understanding of, and a more complete set of details about, them. Results from a myriad of empirical spatial economics landscapes [e.g., Puerto Rico, Jiangsu Province, Texas, Houston (Harris County), and the Dallas-Fort Worth (DFW) metroplex] and a variety of planar surface partitionings (including the square and hexagonal tessellations, and randomly generated graphs) illustrate highlighted theoretical and conceptual traits. These include a corroboration of the contention in the literature that the MC more closely aligns with spatial autoregression, and the GR more closely aligns with geostatistics.

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This paper describes the previously unknown statistical distribution of adjacency matrix spectra for planar graphs, also known as spatial weights matrices, in terms of the following three readily available eigenvalue properties: extremes, rank orderings, and sums of powers. This distribution is governed by at most six parameters that, once known, allow accurate approximations of eigenvalues to be computed without resorting to numerical matrix methods applied on a case-by-case basis. Parameter estimates for illustrative real-world examples are obtained using nonlinear least squares regression techniques. Three conjectures are proposed, and graphical and trend results are reported for a diverse set of planar graph-based matrices.

Malabsorptive bariatric surgery is rapidly becoming a major cause of copper deficiency given the increasing prevalence of these procedures for morbid obesity. Acquired copper deficiency can present with clinically significant hematologic and neurological manifestations. Although hematologic manifestations of copper deficiency are rapidly reversible, significant neurological improvement after copper supplementation therapy is unusual and many patients remain debilitated and may only experience, at best, stabilization of the neurological manifestations. Here we present a case of an undiagnosed copper deficiency several years after bariatric gastric bypass surgery, in a patient who concomitantly used zinc-containing denture cream for several years, associated with anemia, neutropenia, myelopathy, respiratory failure, and bilateral optic neuropathy, which caused major vision loss. This patient was also a heterozygote carrier of the 5,10-methylenetetrahydrofolate reductase A1298C gene polymorphism, which may affect copper metabolism. Intravenous copper repletion resulted in rapid correction of hematologic indices. However, neurological manifestations, including vision loss responded only modestly to copper supplementation, despite achieving normal blood copper concentrations. Clinicians should consider copper deficiency in patients at risk, as in this case, as a delayed diagnosis can lead to irreversible disability due to neurological manifestations.

Urban Compression Patterns: Fractals and Non-Euclidean Geometries - Inventory and ProspectUrban growth and fractality is a topic that opens an entrance for a range of radical ideas: from the theoretical to the practical, and back again. We begin with a brief inventory of related ideas from the past, and proceed to one specific application of fractals in the non-Euclidean geometry of Manhattan space. We initialize our discussion by inventorying selected existing knowledge about fractals and urban areas, and then presenting empirical evidence about the geometry of and movement in physical urban space.Selected empirical analyses of minimum path distances between places in urban space indicate that its metric is best described by a general Minkowskian one whose parameters are between those for Manhattan and Euclidean space. Separate analyses relate these results to the fractal dimensions of the underlying physical spaces. One principal implication is that theoretical, as well as applied, ideas based upon fractals and the Manhattan distance metric should be illuminating in a variety of contexts. These specific analyses are the focus of this paper, leading a reader through analytical approaches to fractal metrics in Manhattan geometry. Consequently, they suggest metrics for evaluating urban network densities as these represent compression of human activity. Because geodesics are not unique in Manhattan geometry, that geometry offers a better fit to human activity than do Euclidean tools with their unique geodesic activities: human activity often moves along different paths to get from one place to another.Real-world evidence motivates our specific application, although an interested reader may find the subsequent "prospect" section of value in suggesting a variety of future research topics that are currently in progress. Does "network science" embrace tools such as these for network compression as it might link to urban function and form? Stay tuned for forthcoming work in Geographical Analysis.

The topic of research ethics is assuming an increasingly more central role when preparing research proposals, with Institutional Review Boards being instructed to oversee more and more research activities. Graduate students, especially, should be educated about ethical and unethical research practices, for they will be taking over the research enterprise in the not-too-distant future. An ethics benchmark is outlined, and numerous research examples involving the themes of plagiarism, deception, fraud, confidentiality, and informed consent are discussed here. This narrative emphasizes that the canons of good scholarship require research and data collection for research to be conducted in an ethical fashion.

The auto-Poisson model describes georeferenced data consisting of counts exhibiting spatial dependence. Its conventional specification is plagued by being restricted to only situations involving negative spatial autocorrelation, and an intractable normalizing constant. Work summarized here accounts for spatial autocorrelation in the mean response specification by incorporating latent map pattern components. Results are reported for seven empirical datasets available in the literature. [Copyright &y& Elsevier]

The purpose of this paper is to identify error properties arising when source maps that individually contain error are added or when the ratio of one map with respect to another is computed. The research approach to the problem combines mathematical analysis and simulation where source maps and error processes have been constructed with specified properties. Geman and Geman's corruption model is used to represent error in individual source maps. The paper reports spatial and aspatial error properties arising from adding and ratioing error-corrupted maps. These are identified as functions of the true characteristics of the individual source maps and the errors inherent within them; the relative contribution of these two components to the errors in maps is quantified by regression (for addition) and ANOVA (for ratioing). The paper considers the broader usefulness of this type of experimental analysis in using artificially constructed maps in geographic information science.

This paper contributes to the ongoing debate about which spatial analysis functions should be coupled with a GIS by identifying research problems that need to be solved before a richer toolbox of spatial statistical techniques can be implemented in a GIS. Three general problem areas are addressed. The first replaces a sequential ordinary least squares linear regression implementation with a single regression analysis. The second establishes the effective sample size for a single variable in a georeferenced data set, a result useful when calculating confidence intervals for means. The third establishes the effective sample size for pairs of variables in a georeferenced data set, a result useful when calculating the significance of correlation coefficients. These three general problems allow four more specific research problems to be identified that are in need of definitive solutions before a richer toolbox of spatial statistical techniques can be relatively easily implemented in a GIS. Their complete solutions will involve both empirical assessments and simulation experiments. These four problems are represented by four principal equations posited in this paper, equations that offer considerable computational simplification for the implementation of spatial statistical techniques within a GIS. Sufficient evidence in support of them is presented here to allow their implementation at this time on an experimental basis. These equations remove the need for eigenfunction and nonlinear optimization routines, and maintain the standard linear regression technique as the workhorse of a GIS statistical analysis. They also strengthen the inferential basis for a spatial scientist.

Curry L., Griffith D. A. and Sheppard E. S. (1975) Those gravity parameters again, Reg. Studies9, 289-296. The arguments presented in a recent paper by Cliff et al. (1974) as regards the relation between gravity parameters and map pattern are reassessed. First, the experimental simulation model designed to support their assertions is examined and found wanting in several fundamental aspects. Second, their statistical argument is evaluated, and it is concluded that when attention is concentrated on specification and interpretation of the parameters rather than on bias in their estimates, then the gravity specification does indeed confound spatial interaction and spatial structure.

Performing data manipulations on maps that possess error as a result of the process of data collection leads to error propagation. The errors that are present in maps are modified by such operations in ways that may undermine the purposeofanalysisand lead to increased uncertainty in thevalidity ofthe conclusions that are drawn. This paper analyses how source map error propagates as a result of overlay operations. Geman and Geman's corruption model for individual source map error is used for the analysis which allows for attribute measurement error and location error that can then interact with the (true) source map geography. This paper reports theoretical results on the univariate overlay problem and then extends these results through simulation. Throughout a set of source maps and error processes are used with specified properties in order to examine in detail the interactions that can take place between the different elements of the source map structure and the error process. The paper uses ANOVA methods to quantify the contribution made by different components of the map and the measurement process to spatial (join count) and non-spatial (proportion of misclassified pixels and the Kappa index) descriptors of error. The paper concludes with a discussion of the usefulness of these results for managing error in spatial databases and for the development of automated uncertainty reporting.

The need for new therapeutic approaches to improve the metabolic and clinical efficacy of nutritional therapy has been increasingly emphasized. The field of nutrition support of catabolic, malnourished, or hospitalized patients is rapidly evolving in response to the beneficial effects observed with adjunctive therapies in animal models and in emerging clinical investigations. Enteral nutrition is being increasingly administered, and enteral diets are being tested to improve gut structure and function. Adjunctive therapies in enteral and parenteral nutrition are being actively investigated. These include administration of recombinant growth factors and anabolic steroid hormones (e.g., growth hormone, oxandrolone); conditionally essential amino acids (e.g., arginine, glutamine); novel lipid products (e.g., structured lipids, fish oils); nutrient antioxidants (e.g., vitamins C and E); and combinations of these approaches. It is likely that current methods of enteral and parenteral nutrition support will evolve in response to the results of these research studies.

A population density model is presented for urban areas containing multiple centers. Utilities of this model include evaluating time series for a transformation from monocentricity to polycentricity and testing the assumption of monocentricity at any point in time. Following a discussion of the method of calibrating this model, a case study of Toronto is presented. The major finding from 1971 and 1976 Toronto data is that no transformation is yet detectable. Interpretation of the model is exemplified in this empirical exercise.

The paper examines the small and large lattice properties of the exact maximum likelihood estimator for a spatial model where parameter estimation and missing data estimation are tackled simultaneously, A first order conditional autoregressive model is examined in detail. The paper concludes with an empirical analysis of remotely sensed data.

The effect of spatial autocorrelation on inferences made using ordinary least squares estimation is considered. It is found, in some cases, that ordinary least squares estimators provide a reasonable alternative to the estimated generalized least squares estimators recommended in the spatial statistics literature. One of the most serious problems in using ordinary least squares is that the usual variance estimators are severely biased when the errors are correlated. An alternative variance estimator that adjusts for any observed correlation is proposed. The need to take autocorrelation into account in variance estimation negates much of the advantage that ordinary least squares estimation has in terms of computational simplicity

Spatial statistics supplies advanced methods for analysing environmental data, and copes with observational interdependencies similar to the way principal components analysis treats multicollinearity. The U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) utilizes kriging from geostatistics for mapping and visualizing environmental data. A conceptual framework is articulated between the interpolation problem in kriging and the missing data problem in spatial statistics, with special reference to relations between the exponential semi-variogram and the conditional autoregressive models. Supercomputing experiments are summarized that simplify numerically the probability density function normalizing factor, which is of particular relevance to estimation tasks for the EMAP project.

This paper seeks to continue the building of a common foundation for spatial statistics and geostatistics. Equations from the conditional autoregressive (CAR) model of spatial statistics for estimating missing geo-referenced data have been found to be exactly those best linear unbiased estimates obtained with the exponential semi-variogram model of kriging, but in terms of the inverse covariance matrix rather than the covariance matrix itself. Further articulation of such relations, between the moving average (MA) and simultaneous autoregressive (SAR) or autoregressive response (AR) models of spatial statistics, and, respectively, the linear and Gaussian semi-variogram models of kriging, is outlined. The exploratory graphical and numerical work summarized in this paper indicates the following: (a) there is evidence to pair the moving average and linear models; (b) the simultaneous autoregressive and autoregressive response model pair with a Bessel function (modified of the second kind and order one) rather than the Gaussian semi-variogram model; (c) both specification error and measurement error can give rise to the nugget effect discussed in geostatistics; (d) restricting estimation to a geographic subregion introduces edge effects that increasingly bias semi-variogram model parameter estimates as the degree of spatial autocorrelation increases toward its upper limit; and (e) the theoretical spectral density function for a simultaneous autoregressive model is a direct extension of that for the conditional autoregressive model.

The Jacobian term appears in certain likelihood functions as a normalizing factor; it ensures that the use of variable transformations still leads to probability density functions whose complete integration yields unity. This term is particularly troublesome when dealing with spatial autoregressive models in that it requires numerically intensive solutions to accompanying parameter estimation problems. For these types of autoregressive models, the Jacobian term is a function of the eigenvalues of then-by-n connectivity matrix that depicts the geographic configuration of the areal units under study. This paper reports on Jacobian approximation results, based upon supercomputer and other experiments, for irregular lattices.

The use of computer technology to analyze and to manage geographic information is becoming commonplace among public and private organizations in virtually all 50 States. The manner in which this information technology is being assembled and deployed is of increasing interest to administrators, practitioners and academicians alike. This paper summarizes the findings for this type of activity in New York State. The reality is GIS dissemination activities are scattered about, the activities themselves are piecemeal, and the principal effort is for creating digital geo-referenced databases that support automated map production. With regard to New York State in particular, and an outcome of analysis of results obtained from an experts' meeting (the Minnowbrook Workshop), primary use of GIS is to automate the production of maps and to integrate environmental and socio-economic data for enhancing environmental management activities. With regard to the integration and linking of geographic information, the adoption of spatial data standards was defined by Minnowbrook Workshop participants as a high priority task. Major concerns relating to research needs focused on methodologies to represent adequately, to analyze, and to visualize spatial data at multiple scales of resolution. The consensus at the Workshop was that educational programs should include concepts and knowledge of data structures and topology, map understanding and spatial thinking, spatial modeling, and map accuracy. Pending New York State legislation (1994 Assembly Bill 7130-A and Senate Bill 5I69-A) embraces the spirit and content of many of the recommendations derived from the Minnowbrook Workshop.

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

The article discusses developments in rotorcraft, aircraft, rocket and spacecraft structures for 2014. Topics covered include research relating to nanocomposites, the development of the Continuous Trailing-Edge Flap at the Langley Research Center of National Aeronautics and Space Administration (NASA), and the unique flight test conducted by the National Research Council of Canada to study the biomedical effects of aircrew exposure to whole-body vibration (WBV).

Presents a crossword puzzle about fruits.

Presents an activity intended for children. How to do the activity; Answers.