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Spatio-temporal hierarchical modeling is an extremely attractive way to model the spread of crime or terrorism data over a given region, especially when the observations are counts and must be modeled discretely. The spatiotempora...
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Spatio-temporal hierarchical modeling is an extremely attractive way to model the spread of crime or terrorism data over a given region, especially when the observations are counts and must be modeled discretely. The spatiotemporal diffusion is placed, as a matter of convenience, in the process model allowing for straightforward estimation of the diffusion parameters through Bayesian techniques. However, this method of modeling does not allow for the existence of self-excitation, or a temporal data model dependency, that has been shown to exist in criminal and terrorism data. In this manuscript we will use existing theories on how violence spreads to create models that allow for both spatio-temporal diffusion in the process model as well as temporal diffusion, or self-excitation, in the data model. We will further demonstrate how Laplace approximations similar to their use in Integrated Nested Laplace Approximation can be used to quickly and accurately conduct inference of self-exciting spatio-temporal models allowing practitioners a new way of fitting and comparing multiple process models. We will illustrate this approach by fitting a self-exciting spatio-temporal model to terrorism data in Iraq and demonstrate how choice of process model leads to differing conclusions on the existence of self-excitation in the data and differing conclusions on how violence spread spatially-temporally in that country from 2003-2010.
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Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation t...
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Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation that farmers will mitigate projected yield losses by planting longer season hybrids that lengthen the grain fill period. Here, we ask: (a) how current hybrid maturity length relates to thermal availability of the local climate, and (b) if farmers are shifting to longer season hybrids in response to a warming climate. To address these questions, we used county-level Pioneer brand hybrid sales (Corteva Agriscience) across 17 years and 650 counties in 10 Midwest states (IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI). Northern counties were shown to select hybrid maturities with growing degree day (GDD degrees C) requirements more closely related to the environmentally available GDD compared to central and southern counties. This measure, termed "thermal overlap," ranged from complete 106% in northern counties to a mere 63% in southern counties. The relationship between thermal overlap and latitude was fit using split-line regression and a breakpoint of 42.8 degrees N was identified. Over the 17-years, hybrid maturities shortened across the majority of the Midwest with only a minority of counties lengthening in select northern and southern areas. The annual change in maturity ranged from -5.4 to 4.1 GDD year(-1) with a median of -0.9 GDD year(-1). The shortening of hybrid maturity contrasts with widespread expectations of hybrid maturity aligning with magnitude of warming. Factors other than thermal availability appear to more strongly impact farmer decision-making such as the benefit of shorter maturity hybrids on grain drying costs, direct delivery to ethanol biorefineries, field operability, labor constraints, and crop genetics availability. Prediction of hybrid choice under future climate scenarios must include climatic factors, physiological-genetic attributes, socio-economic, and operational constraints.
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Numerous predictive models have been developed to determine the likelihood that non-native plants will escape from cultivation and potentially become invasive. Given the substantial biological and economic costs that can result fr...
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Numerous predictive models have been developed to determine the likelihood that non-native plants will escape from cultivation and potentially become invasive. Given the substantial biological and economic costs that can result from the introduction of a new invasive plant and the unending pressures of world trade and transport, the creation and implementation of effective predictive models are becoming increasingly important. One key question in the development of such models focuses on the geographic scope at which models can best be developed and applied. We have developed models to predict woody-plant naturalization in five local areas within the Upper Midwest (United States). Herein, we consider whether naturalization can be reasonably predicted from a single model for the entire region or whether local models are required for each specific area. We develop a random forest model to predict the probability of naturalization in the region and compare out-of-sample prediction errors between the regional and local models. The regional model makes better predictions of the probability of naturalization for those species observed to naturalize but worse predictions for those not currently observed to naturalize. This model development process has given us an opportunity (not previously addressed in the literature) to examine the strengths and weaknesses of local and regional approaches, with the ultimate intent of optimizing geographic scope.
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Developing effective butterfly monitoring strategies is key to understanding how butterflies interact with urban environments, and, in turn, to developing local conservation practices. We investigated two urban habitat types (publ...
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Developing effective butterfly monitoring strategies is key to understanding how butterflies interact with urban environments, and, in turn, to developing local conservation practices. We investigated two urban habitat types (public gardens and restored/reconstructed prairies) and compared three survey methods (Pollard transects, purposive point counts, and random point counts) to determine which was most productive for detecting butterflies and assessing family diversity. We conducted 66 butterfly surveys by using each method (198 total) from May through September in 2015 and 2016 at six sites (three public gardens and three prairies) in Ames, Ankeny and Des Moines, Iowa. All survey methods were used on 11 sampling dates at each site. Overall, we observed 2,227 butterflies representing 38 species: 1,076 in public gardens and 1,151 in prairie areas. We used a smaller data set standardized for survey effort, including 1,361 of these sightings, to compare survey methods and habitat types. Although there were no significant differences in number of butterfly sightings between the two habitats, more sightings (798) were documented by using purposive point counts when compared to Pollard transects (297) or random point counts (266) (for both comparisons, p < 0.0001). Occupancy modeling also indicated that purposive point counts were most effective in detecting certain species of butterflies, most notably those within the Pieridae (whites, sulphurs) and Papilionidae (swallowtails). We conclude that public gardens and restored/reconstructed prairies in urban settings can provide important butterfly habitat, and that purposive point-count surveys are most effective for detecting butterflies in these relatively small-scale landscape features.
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Common Carp Cyprinus carpio and Bigmouth Buffalo Ictiobus cyprinellus are widespread across the Midwest, where they can achieve high population densities in shallow lakes. Despite their contrasting statuses as invasive and native ...
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Common Carp Cyprinus carpio and Bigmouth Buffalo Ictiobus cyprinellus are widespread across the Midwest, where they can achieve high population densities in shallow lakes. Despite their contrasting statuses as invasive and native fishes, these species are commonly managed together due to their capacity to have deleterious effects on water quality. However, intra-annual sampling variability is poorly understood for both species, making it difficult to track population changes. We compared boat electrofishing CPUE and proportional size distribution (PSD) of Common Carp and Bigmouth Buffalo during May-October 2018-2020 in seven shallow northwestern Iowa lakes. We also estimated sampling effort necessary to reach targets of fish captured (100) and precision (relative standard error < 25%) and to detect 25% and 50% differences in CPUE between samples. Common Carp CPUE peaked in June (29 fish/h), declined across the summer, and was lowest in October (9 fish/h). Bigmouth Buffalo CPUE exhibited bimodal peaks in May (16 fish/h) and October (12 fish/h), with low catches during August (2 fish/h). Common Carp size structure was largest during October (PSD of preferred-size fish [PSD-P] = 92) and smallest in July (PSD-P = 58). Bigmouth Buffalo size structure was smaller in June (PSD-P = 20) than in all other months (PSD-P = 34). Our targets for number of fish captured and precision were typically reached within twenty-five 15-min electrofishing transects for Common Carp during periods of high catch rates but were not reached after 100 transects for Bigmouth Buffalo in most cases. Our results suggest that timing of sampling is important for reliably and efficiently assessing Common Carp and Bigmouth Buffalo populations. We recommend the sampling of Common Carp with electrofishing during June and the sampling of Bigmouth Buffalo during May in Midwestern shallow lakes to maximize catch rates while minimizing the influence of seasonal sampling variability.
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Excessive demands on maternal nutritional status may be a risk factor for poor birth outcomes. This study examined the association between breastfeeding during late pregnancy (>= 28 weeks) and the risk of having a small-for-gestat...
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Excessive demands on maternal nutritional status may be a risk factor for poor birth outcomes. This study examined the association between breastfeeding during late pregnancy (>= 28 weeks) and the risk of having a small-for-gestational-age (SGA) newborn, using a matched case-control design (78 SGA cases: birthweight <10th percentile for gestational age; 150 non-SGA controls: 50th percentile 收起
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Detection of somatic mutations in human leukocyte antigen (HLA) genes using whole-exome sequencing (WES) is hampered by the high polymorphism of the HLA loci, which prevents alignment of sequencing reads to the human reference gen...
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Detection of somatic mutations in human leukocyte antigen (HLA) genes using whole-exome sequencing (WES) is hampered by the high polymorphism of the HLA loci, which prevents alignment of sequencing reads to the human reference genome. We describe a computational pipeline that enables accurate inference of germline alleles of class I HLA-A, B and C genes and subsequent detection of mutations in these genes using the inferred alleles as a reference. Analysis of WES data from 7,930 pairs of tumor and healthy tissue from the same patient revealed 298 nonsilent HLA mutations in tumors from 266 patients. These 298 mutations are enriched for likely functional mutations, including putative loss-of-function events. Recurrence of mutations suggested that these 'hotspot' sites were positively selected. Cancers with recurrent somatic HLA mutations were associated with upregulation of signatures of cytolytic activity characteristic of tumor infiltration by effector lymphocytes, supporting immune evasion by altered HLA function as a contributory mechanism in cancer.
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Hypericum perforatum extracts have been used to treat diseases, including mild-to-moderate depression and inflammatory conditions. It is particularly important to identify which constituents present in the H. perforatum extracts a...
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Hypericum perforatum extracts have been used to treat diseases, including mild-to-moderate depression and inflammatory conditions. It is particularly important to identify which constituents present in the H. perforatum extracts are responsible for its anti-inflammatory activity since consumers are taking H. perforatum preparations to treat inflammation. We used a combination of four putative bioactive constituents, called the 4-component-system that interacted synergistically to explain the light-activated anti-inflammatory activity of an H. perforatum fraction in RAW 264.7 mouse macrophages. We also combined the constituents at concentrations detected in the fraction to identify key molecular targets. LPS was used to model an inflammatory response, and the 4-component-system and H. perforatum fraction were used as treatments that inhibited LPS-induced prostaglandin E2 (PGE2) production in RAW 264.7 mouse macrophages in the studies of gene expression profiles. We used Affymetrix genechips, statistical analysis, and quantitative real-time PCR to identify key gene targets of the 4-component-system and the sub-fraction from an H. perforatum ethanol extract. The H. perforatum sub-fraction, with or without LPS stimulation, affected far more genes than the 4-component-system with and without LPS. Genes involved in Janus kinase, as well as a signal transducer and activator of transcription (JAKSTAT) and eicosanoid pathways were identified that could account for the reduction in PGE2 observed with both treatments in LPS-stimulated macrophages. Ten genes may be particularly important targets for activity of the 4-component-system and the fraction with LPS stimulation and these genes were involved in inflammatory signaling pathways, namely the JAKSTAT and eicosanoid pathways.
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Accurate methods to predict the naturalization of non-native woody plants are key components of risk-management programs being considered by nursery and landscape professionals. The objective of this study was to evaluate four dec...
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Accurate methods to predict the naturalization of non-native woody plants are key components of risk-management programs being considered by nursery and landscape professionals. The objective of this study was to evaluate four decision-tree models to predict naturalization (first tested in Iowa) on two new sets of data for non-native woody plants cultivated in the Chicago region. We identified life-history traits and native ranges for 193 species (52 known to naturalize and 141 not known to naturalize) in two study areas within the Chicago region. We used these datasets to test four models (one continental-scale and three regional-scale) as a form of external validation. Application of the continental-scale model resulted in classification rates of 72-76%, horticulturally limiting (false positive) error rates of 20-24%, and biologically significant (false negative) error rates of 5-6%. Two regional modifications to the continental model gave increased classification rates (85-93%) and generally lower horticulturally limiting error rates (16-22%), but similar biologically significant error rates (5-8%). A simpler method, the CART model developed from the Iowa data, resulted in lower classification rates (70-72%) and higher biologically significant error rates (8-10%), but, to its credit, it also had much lower horticulturally limiting error rates (5-10%). A combination of models to capture both high classification rates and low error rates will likely be the most effective until improved protocols based on multiple regional datasets can be developed and validated.
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Purpose Statistical equivalence testing is more appropriate than conventional tests of difference to assess the validity of physical activity (PA) measures. This article presents the underlying principles of equivalence testing an...
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Purpose Statistical equivalence testing is more appropriate than conventional tests of difference to assess the validity of physical activity (PA) measures. This article presents the underlying principles of equivalence testing and gives three examples from PA and fitness assessment research.
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