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null (Ed.)Abstract Background Prior research has revealed positive effects of spatial activity participation (e.g., playing with blocks, sports) on current and future spatial skills. However, research has not examined the degree to which spatial activity participation remains stable over time, and little is known about how participating in spatial activities at multiple points in development impacts spatial thinking. In this study, adolescents completed measures of spatial thinking and questionnaires assessing their current and previous participation in spatial activities. Results Participation in childhood spatial activities predicted adolescent spatial activity participation, and the relation was stronger for females than for males. Adolescents’ current participation in spatial activities predicted spatial thinking skills, whereas participation in childhood spatial activities predicted adolescents’ spatial habits of mind, even when accounting for factors such as gender and academic performance. No cumulative benefit was incurred due to participating in spatial activities in both childhood and adolescence, and a lack of spatial activities in childhood was not made up for by later spatial activity participation. Conclusions These findings reveal a consistently positive relationship in spatial activity participation between childhood and adolescence. Results highlight the importance of participating in spatial activities during childhood, and underscore the differential impact that participation in spatial activities during childhood versus adolescence has on different facets of adolescents’ spatial thinking. Implications for the timing of interventions is discussed.more » « less
The biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems.
In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models.
We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.
The prevalence and intensity of parasites in wild hosts varies across space and is a key determinant of infection risk in humans, domestic animals and threatened wildlife. Because the immune system serves as the primary barrier to infection, replication and transmission following exposure, we here consider the environmental drivers of immunity. Spatial variation in parasite pressure, abiotic and biotic conditions, and anthropogenic factors can all shape immunity across spatial scales. Identifying the most important spatial drivers of immunity could help pre‐empt infectious disease risks, especially in the context of how large‐scale factors such as urbanization affect defence by changing environmental conditions.
We provide a synthesis of how to apply macroecological approaches to the study of ecoimmunology (i.e. macroimmunology). We first review spatial factors that could generate spatial variation in defence, highlighting the need for large‐scale studies that can differentiate competing environmental predictors of immunity and detailing contexts where this approach might be favoured over small‐scale experimental studies. We next conduct a systematic review of the literature to assess the frequency of spatial studies and to classify them according to taxa, immune measures, spatial replication and extent, and statistical methods.
We review 210 ecoimmunology studies sampling multiple host populations. We show that whereas spatial approaches are relatively common, spatial replication is generally low and unlikely to provide sufficient environmental variation or power to differentiate competing spatial hypotheses. We also highlight statistical biases in macroimmunology, in that few studies characterize and account for spatial dependence statistically, potentially affecting inferences for the relationships between environmental conditions and immune defence.
We use these findings to describe tools from geostatistics and spatial modelling that can improve inference about the associations between environmental and immunological variation. In particular, we emphasize exploratory tools that can guide spatial sampling and highlight the need for greater use of mixed‐effects models that account for spatial variability while also allowing researchers to account for both individual‐ and habitat‐level covariates.
We finally discuss future research priorities for macroimmunology, including focusing on latitudinal gradients, range expansions and urbanization as being especially amenable to large‐scale spatial approaches. Methodologically, we highlight critical opportunities posed by assessing spatial variation in host tolerance, using metagenomics to quantify spatial variation in parasite pressure, coupling large‐scale field studies with small‐scale field experiments and longitudinal approaches, and applying statistical tools from macroecology and meta‐analysis to identify generalizable spatial patterns. Such work will facilitate scaling ecoimmunology from individual‐ to habitat‐level insights about the drivers of immune defence and help predict where environmental change may most alter infectious disease risk.
There is significant work indicating that spatial ability has correlations to student success in STEM programs. Work also shows that spatial ability correlates to professional success in respective STEM fields. Spatial ability has thus been a focus of research in engineering education for some time. Spatial interventions have been developed to improve student’s spatial ability that range from physical manipulatives to the implementation of entire courses. These interventions have had positive impact upon student success and retention. Currently, researchers rely on a variety of different spatial ability instruments to quantify participants spatial ability. Researchers classify an individual’s spatial ability as the performance indicated by their results on such an instrument. It is recognized that this measured performance is constrained by the spatial construct targeted with that spatial instrument. As such, many instruments are available for the researchers use to assess the variety of constructs of spatial ability. Examples include the Purdue Spatial Visualization Test of Rotations (PSVTR), the Mental Cutting Test (MCT), and the Minnesota Paper Foam Board Test. However, at this time, there are no readily accessible spatial ability instruments that can be used to assess spatial ability in a blind or low vision population (BLV). Such an instrument would not only create an instrument capable of quantifying the impacts of spatially focused interventions upon BLV populations but also gives us a quantitative method to assess the effectiveness of spatial curriculum for BLV students. Additionally, it provides a method of assessing spatial ability development from tactile perspective, a new avenue for lines of research that expand beyond the visual methods typically used. This paper discusses the development of the Tactile Mental Cutting Test (TMCT), a non-visually accessible spatial ability instrument, developed and used with a BLV population. Data was acquired from individuals participating in National Federation of the Blind (NFB) Conventions across the United States as well as NFB sponsored summer engineering programs. The paper reports on a National Science Foundation funded effort to garner initial research findings on the application of the TMCT. It reports on initial findings of the instrument’s validity and reliability, as well as the development of the instrument over the first three years of this project.more » « less
Both historically and in terms of practiced academic organization, the anticipation should be that a flourishing synergistic interface exists between statistics and operations research in general, and between spatial statistics/econometrics and spatial optimization in particular. Unfortunately, for the most part, this expectation is false. The purpose of this paper is to address this existential missing link by focusing on the beneficial contributions of spatial statistics to spatial optimization, via spatial autocorrelation (i.e., dis/similar attribute values tend to cluster together on a map), in order to encourage considerably more future collaboration and interaction between contributors to their two parent bodies of knowledge. The key basic statistical concept in this pursuit is the median in its bivariate form, with special reference to the global and to sets of regional spatial medians. One-dimensional examples illustrate situations that the narrative then extends to two-dimensional illustrations, which, in turn, connects these treatments to the spatial statistics centrography theme. Because of computational time constraints (reported results include some for timing experiments), the summarized analysis restricts attention to problems involving one global and two or three regional spatial medians. The fundamental and foundational spatial, statistical, conceptual tool employed here is spatial autocorrelation: geographically informed sampling designs—which acknowledge a non-random mixture of geographic demand weight values that manifests itself as local, homogeneous, spatial clusters of these values—can help spatial optimization techniques determine the spatial optima, at least for location-allocation problems. A valuable discovery by this study is that existing but ignored spatial autocorrelation latent in georeferenced demand point weights undermines spatial optimization algorithms. All in all, this paper should help initiate a dissipation of the existing isolation between statistics and operations research, hopefully inspiring substantially more collaborative work by their professionals in the future.more » « less