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The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction. SARS-CoV-2 nucleocapsid (N) protein is responsible for viral genome packaging. Here the authors employ single-molecule spectroscopy with all-atom simulations to provide the molecular details of N protein and show that it undergoes phase separation with RNA. [ABSTRACT FROM AUTHOR]

Given widespread habitat degradation and loss, reliable indicators are needed that provide a comprehensive assessment of community response to anthropogenic disturbance. The family Phyllostomidae (Order Chiroptera) has frequently been the focus of research evaluating bats' response to habitat disturbance in seasonally dry tropical forests (SDTFs). However, few studies compare this family to the larger bat assemblage to assess its efficacy as a bioindicator. We compared community and species-specific attributes of understory phyllostomid and all understory bat species: (1) along a gradient of habitat disturbance within a human-modified SDTF landscape; and (2) between forest and riparian habitats within each disturbance level. We captured 290 individuals belonging to 13 species and 4 families. Phyllostomid species exhibited greater sensitivity to disturbance than the understory bat community as a whole based on richness and beta diversity. Both groups were more sensitive to disturbance in forest than riparian habitat, but phyllostomid species were more likely to be lost from highly disturbed forest habitat. The two dominant species declined in abundance with disturbance but variation in body condition was species-specific. These results suggest that Phyllostomidae are more effective indicators of human disturbance in SDTF than the understory bat community as a whole and evaluation of bats' response to disturbance is best accomplished with a multifaceted approach. [ABSTRACT FROM AUTHOR]

Organic food consumption is increasing globally but still comprises a small share of the overall food market. Our understanding of organic consumption is primarily based on consumer surveys, while we lack robust data on the actual price and availability of organic products in most countries. To address this gap, we conducted a case study where we collected data directly from produce sellers in Seville, Spain, to evaluate the extent to which price, availability, and farmer price fairness potentially limit organic consumption in marketing channels with short food supply chains (SFSCs) and long food supply chains (LFSCs). Price of a family produce basket was not significantly different between conventional LFSC and organic SFSC channels. Organic produce was more available in SFSC than LFSC channels by an average of 14 weeks. Price fairness, measured as farmers' share of the value added, was higher in organic SFSC than organic LFSC and conventional LFSC channels. These results suggest that consumers in Seville can avoid the higher prices and lower availability of organic produce in supermarkets and ensure farmers greater revenue share by switching to organic SFSC channels like farmer-consumer cooperatives and direct sellers. However, the scarcity of such information for most products and markets limits consumers' ability to ascertain the economic and social benefits of different channels. This study highlights the need for systematic data collection and sharing from different marketing channels to better inform consumers about their food purchasing options and improve understanding of the factors limiting organic consumption. [ABSTRACT FROM AUTHOR]

Spatial cancer data analyses frequently utilize regression techniques to investigate associations between cancer incidences and potential covariates. Model specification, a process of formulating an appropriate model, is a well-recognized task in the literature. It involves a distributional assumption for a dependent variable, a proper set of predictor variables (i.e., covariates), and a functional form of the model, among other things. For example, one of the assumptions of a conventional statistical model is independence of model residuals, an assumption that can be easily violated when spatial autocorrelation is present in observations. A failure to account for spatial structure can result in unreliable estimation results. Furthermore, the difficulty of describing georeferenced data may increase with the presence of a positive and negative spatial autocorrelation mixture, because most current model specifications cannot successfully explain a mixture of spatial processes with a single spatial autocorrelation parameter. Particularly, properly accounting for a spatial autocorrelation mixture is challenging. This paper empirically investigates and uncovers a possible spatial autocorrelation mixture pattern in breast cancer incidences in Broward County, Florida, during 2000–2010, employing different model specifications. The analysis results show that Moran eigenvector spatial filtering provides a flexible method to examine such a mixture. [ABSTRACT FROM AUTHOR]

Analytical techniques continue to advance in efficacy, as well as complexity. However, it is sometimes unrealistic to employ complex analyses during time-constrained humanitarian disaster operations. We propose that simple, embedded analytics tools can provide an effective and practical means toward managing humanitarian operations. In this paper, we demonstrate a real-world application of our technique in a patient evacuation context. This paper contributes to literature and practice by showing how simple analytic methods and open-source imagery tools can offer significant value to the humanitarian operations literature. The application also highlights some challenges to drawing a clear picture from disparate data sources in the humanitarian operations domain. [ABSTRACT FROM AUTHOR]

Being a hot topic in recent years, many studies have been conducted with spatial data containing massive numbers of observations. Because initial developments for classical spatial autocorrelation statistics are based on rather small sample sizes, in the context of massive spatial datasets, this paper presents extensions to efficiency and statistical power comparisons between the Moran coefficient and the Geary ratio for different variable distribution assumptions and selected geographic neighborhood definitions. The question addressed asks whether or not earlier results for small n extend to large and massively large n, especially for non-normal variables; implications established are relevant to big spatial data. To achieve these comparisons, this paper summarizes proofs of limiting variances, also called asymptotic variances, to do the efficiency analysis, and derives the relationship function between the two statistics to compare their statistical power at the same scale. Visualization of this statistical power analysis employs an alternative technique that already appears in the literature, furnishing additional understanding and clarity about these spatial autocorrelation statistics. Results include: the Moran coefficient is more efficient than the Geary ratio for most surface partitionings, because this index has a relatively smaller asymptotic as well as exact variance, and the superior power of the Moran coefficient vis-à-vis the Geary ratio for positive spatial autocorrelation depends upon the type of geographic configuration, with this power approaching one as sample sizes become increasingly large. Because spatial analysts usually calculate these two statistics for interval/ration data, this paper also includes comments about the join count statistics used for nominal data. [ABSTRACT FROM AUTHOR]

Connected planar graphs are of interest to a variety of scholars. Being able to simulate a database of such graphs with selected properties would support specific types of inference for spatial analysis and other network-based disciplines. This paper presents a simple, efficient, and flexible connected planar graph generator for this purpose. It also summarizes a comparison between an empirical set of specimen graphs and their simulated counterpart set, and establishes evidence for positing a conjecture about the principal eigenvalue of connected planar graphs. Finally, it summarizes a probability assessment of the algorithm’s outcomes as well as a comparison between the new algorithm and selected existing planar graph generators. [ABSTRACT FROM AUTHOR]

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results. [ABSTRACT FROM AUTHOR]

Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches. [ABSTRACT FROM AUTHOR]

Leaf mass per area (LMA) is a primary plant functional trait that represents the cost of constructing a leaf. Ultimately, plants modify LMA by altering leaf thickness (LT), leaf dry matter content (LDMC), or both. While LMA can be modified through both of these constituents, studies of LMA have found that there is variation in whether LT or LDMC changes are responsible for LMA-and the relationships change depending on the species or functional groups being compared. In this study, we used a phylogenetic framework to determine that evolutionary shifts in LMA are driven by LT, and not LDMC, among 45 Serengeti grass species. We considered two alternative hypotheses that could result in evolutionary correlation of LMA on LT but not LDMC: either (1) LT is more labile than LDMC-and is therefore a less costly means to change LMA or (2) LDMC is tightly coupled to a different dimension of leaf variation (e.g., leaf hydraulics), leaving LT as the source of variation in LMA. LT was not more labile than LDMC, leading us to conclude that the evolution of LMA has been shaped by LT because LDMC is responding to other demands on leaf physiology. We speculate that leaf hydraulics provide this constraint on LDMC. The decoupling of LDMC from LT may allow plants to better optimize resource allocation in ecosystems where gradients in light competition, herbivory, and aridity place competing demands on leaf economics. [ABSTRACT FROM AUTHOR]

Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment. [ABSTRACT FROM AUTHOR]

Eigenvector spatial filtering (ESF) is becoming a popular way to address spatial dependence. Recently, a random effects specification of ESF (RE-ESF) is receiving considerable attention because of its usefulness for spatial dependence analysis considering spatial confounding. The objective of this study was to analyze theoretical properties of RE-ESF and extend it to overcome some of its disadvantages. We first compare the properties of RE-ESF and ESF with geostatistical and spatial econometric models. There, we suggest two major disadvantages of RE-ESF: it is specific to its selected spatial connectivity structure, and while the current form of RE-ESF eliminates the spatial dependence component confounding with explanatory variables to stabilize the parameter estimation, the elimination can yield biased estimates. RE-ESF is extended to cope with these two problems. A computationally efficient residual maximum likelihood estimation is developed for the extended model. Effectiveness of the extended RE-ESF is examined by a comparative Monte Carlo simulation. The main findings of this simulation are as follows: Our extension successfully reduces errors in parameter estimates; in many cases, parameter estimates of our RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE-ESF in many cases. [ABSTRACT FROM AUTHOR]

This paper addresses the theme of spatial autocorrelation impacting spatial equilibria, and hence an understanding of economic network resilience and vulnerability. It exploits the notion that spatial autocorrelation in the geographic distribution of origin and destination attributes and network autocorrelation in the flows between origins and destinations constitute two spatial autocorrelation components contained in spatial interaction data. It illustrates that a spatial interaction model specification needs to incorporate both components in order to furnish sound implications about associated economic network resilience and vulnerability. Such models also need to undergo sensitivity analyses in terms of changes in geographic scale and resolution. And, it furnishes a novel 3-D visualization of geographic flows, such as journey-to-work trips, in order to achieve a better comprehension of economic network resilience and vulnerability. [ABSTRACT FROM AUTHOR]

In this paper, we distinguish three constrained variants of the gravity model of spatial interaction: doubly constrained, production constrained and attraction constrained exponential gravity models. These model variants include origin- and/or destination-specific balancing factors that act as constraints to ensure that the estimated rows and columns of the flow data matrix sum to the observed row and column totals. Because flows are typically counts, the Poisson rather than the normal probability model specification furnishes the appropriate statistical distribution, and parameter estimation can be achieved via Poisson regression. This probability model specification motivates the use of origin and/or destination fixed effects or-under certain conditions-the use of origin- and/or destination-specific random effects for model estimation. The paper establishes theoretical connections between balancing factors, fixed effects represented by binary indicator variables and random effects. The results pertaining to both the doubly and singly constrained cases of spatial interaction are illustrated with an empirical example while accounting for spatial dependence between flows from locations neighbouring both the origins and destinations during estimation. [ABSTRACT FROM AUTHOR]

Interest in space–time modeling is experiencing a resurgence, in part because more and more sizeable space–time datasets are becoming readily available. Currently techniques to describe these data, many of which have existed for years, are being utilized and improved. This paper surveys general categories of these techniques (i.e., autoregressive-integrated-moving-average models, space–time autoregressive models, three-dimensional geostatistical models, and panel data models), in retrospect, demonstrates a future prospect (i.e., spatial filtering models), and suggests important topics for incorporation into a research agenda, including ones pertaining to non-normal random variables, panel data models, space–time heterogeneity, missing data, and distributional properties of space–time filters. [ABSTRACT FROM AUTHOR]

Since before the inception of work by Okabe, the intermingling of spatial autocorrelation (i.e., local distance and configuration) and distance decay (i.e., global distance) effects has been suspected in spatial interaction data. This convolution was first treated conceptually because technology and methodology did not exist at the time to easily or fully address spatial autocorrelation effects within spatial interaction model specifications. Today, however, sufficient computer power coupled with eigenfunction-based spatial filtering offers a means for accommodating spatial autocorrelation effects within a spatial interaction model for modest-sized problems. In keeping with Okabe’s more recent efforts to dissemination spatial analysis tools, this paper summarizes how to implement the methodology utilized to analyze a particular empirical flows dataset. [ABSTRACT FROM AUTHOR]

Separate space- or time-lags have been considered regularly in data analyses; as space–time models are more recently being studied extensively in data analytic fashion, joint estimation of both lags has to be considered explicitly. This paper addresses this issue, taking into special consideration parametric parsimony together with specification richness; use of the bivariate Poisson frequency distribution is advocated and applied to an empirical case. The relation of this approach to random effects specifications is investigated. Data for Belgian regional products constitute the empirical case study. [ABSTRACT FROM AUTHOR]

In the indoor environment, settled surface dust often functions as a reservoir of hazardous particulate contaminants. In many circumstances, a major contributing source to the dust pool is exterior soil. Young children are particularly susceptible to exposure to both outdoor derived soil and indoor derived dust present in the indoor dust pool. This is because early in life the exploratory activities of the infant are dominated by touching and mouthing behavior. Inadvertent exposure to dust through mouth contact and hand-to-mouth activity is an inevitable consequence of infant development. Clean-up of indoor dust is, in many circumstances, critically important in efforts to minimize pediatric exposure. In this study, we examine the efficiency of vacuum cleaner removal of footwear-deposited soil on vinyl floor tiles. The study utilized a 5 × 10 foot (c. 152.5 × 305 cm) test surface composed of 1-foot-square (c. 30.5 × 30.5 cm) vinyl floor tiles. A composite test soil with moderately elevated levels of certain elements (e.g., Pb) was repeatedly introduced onto the floor surface by footwear track-on. The deposited soil was subsequently periodically removed from randomly selected tiles using a domestic vacuum cleaner. The mass and loading of soil elements on the tiles following vacuuming were determined both by wet wipe collection and by subsequent chemical analysis. It was found that vacuum cleaner removal eliminated much of the soil mass from the floor tiles. However, a small percentage of the mass was not removed and a portion of this residual mass could be picked up by moistened hand-lifts. Furthermore, although the post-vacuuming tile soil mass was sizably reduced, for some elements (notably Pb) the concentration in the residual soil was increased. We interpret this increased metal concentration to be a particle size effect with smaller particles (with a proportionately higher metal content) remaining in situ after vacuuming. [ABSTRACT FROM AUTHOR]

Properly sampling soils and mapping soil contamination in urban environments requires that impacts of spatial autocorrelation be taken into account. As spatial autocorrelation increases in an urban landscape, the amount of duplicate information contained in georeferenced data also increases, whether an entire population or some type of random sample drawn from that population is being analyzed, resulting in conventional power and sample size calculation formulae yielding incorrect sample size numbers vis-à-vis model-based inference. Griffith (in Annals, Association of American Geographers, 95, 740–760, 2005) exploits spatial statistical model specifications to formulate equations for estimating the necessary sample size needed to obtain some predetermined level of precision for an analysis of georeferenced data when implementing a tessellation stratified random sampling design, labeling this approach model-informed, since a model of latent spatial autocorrelation is required. This paper addresses issues of efficiency associated with these model-based results. It summarizes findings from a data collection exercise (soil samples collected from across Syracuse, NY), as well as from a set of resampling and from a set of simulation experiments following experimental design principles spelled out by Overton and Stehman (in Communications in Statistics: Theory and Methods, 22, 2641–2660). Guidelines are suggested concerning appropriate sample size (i.e., how many) and sampling network (i.e., where). [ABSTRACT FROM AUTHOR]

Despite the dust cleanup and indoor air testing program led by the U.S. Environmental Protection Agency (EPA) and offered to all residents of Lower Manhattan (south of Canal Street), concern remains about local chemical residues from the collapse of the World Trade Center (WTC) buildings. Data on post-cleanup indoor airborne asbestos concentration, available from EPA Region 2, were analyzed to assess the possibility that the WTC site is the source of geographically concentrated rare post-cleanup exceedances of the health-based standard for asbestos. Recognizing that these rare exceedances may be attributable to sources other than the WTC disaster, and that these sources are very likely to exhibit geographic patterns, the data were analyzed using a spatial filter specification of the auto-Poisson probability model. Our analysis shows that ignoring geographic patterns latent in these exceedances affects the empirical probability of exceeding the health-based standards for airborne asbestos. We did not find any statistically-significant geographic pattern in the exceedance events that would indicate the WTC site as the source of the post-cleanup exceedances. Apparent geographic patterns may be due to the geographic variability in sampling intensity. Our analysis indicates the Residential Dust Cleanup Program lead by EPA Region 2 has been effective at reducing the concentration of air-borne asbestos in indoor air to below the health-based benchmark. [ABSTRACT FROM AUTHOR]

A common approach to modeling population density gradients across a city is to adjust the specification of a selected set of mathematical functions to achieve the best fit to an urban place’s empirical density values. In this paper, we employ a spatial regression approach that takes into account the spatial autocorrelation latent in urban population density. We also use a Minkowskian distance metric instead of Euclidean or network distance to better describe spatial separation. We apply our formulation to the 20 largest metropolitan areas in the US according to the 2000 census, using block group level data. The general model furnishes good descriptions for both monocentric and polycentric cities. [ABSTRACT FROM AUTHOR]

Statistics is a branch of mathematics concerned with the collection, quantification, analysis, interpretation, and presentation of real-world data, and the use of probability theory to estimate population parameters with these data. Spatial statistics is a subset of statistics that is concerned with handling the special problems associated with geographically distributed data, which include spatial point patterns, regional and lattice measurement aggregations, irregularly spaced site-specific measurements on a surface, and image analysis. Meanwhile, econometrics is concerned with the application of statistical methods to the study of economic data and problems. When coining the term spatial econometrics in 1979, Paelinck and Klaassen characterized it as a subset of econometrics that is concerned with the role of spatial dependence in regional economic model response and explanatory variables, asymmetries in spatial relationships, the specification of geographic structure governing spatial interactions, and the explicit modeling of space. We outline and discuss principal similarities (e.g., testing for the presence of spatial autocorrelation) and differences (e.g., map generalization) between spatial statistics and spatial econometrics. In doing so, our goal is to help clarify past, present, and future relationships between these two subfields. [ABSTRACT FROM AUTHOR]