32 research outputs found
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Periodic Ising Correlations
No full textWe consider the finite two-dimensional Ising model on a lattice with periodic boundaryconditions. Kaufman determined the spectrum of the transfer matrix on the finite,periodic lattice, and her derivation was a simplification of Onsager's famous result onsolving the two-dimensional Ising model. We derive and rework Kaufman's resultsby applying representation theory, which give us a more direct approach to computethe spectrum of the transfer matrix. We determine formulas for the spin correlationfunction that depend on the matrix elements of the induced rotation associated withthe spin operator. The representation of the spin matrix elements is obtained byconsidering the spin operator as an intertwining map. We wrap the lattice aroundthe cylinder taking the semi-infinite volume limit. We control the scaling limit of themulti-spin Ising correlations on the cylinder as the temperature approaches the criticaltemperature from below in terms of a Bugrij-Lisovyy conjecture for the spin matrixelements on the finite, periodic lattice. Finally, we compute the matrix representationof the spin operator for temperatures below the critical temperature in the infinite-volume limit in the pure state defined by plus boundary conditions
Hemocyte-mediated phagocytosis differs between honey bee (Apis mellifera) worker castes
No full textabstract: Honey bees as other insects rely on the innate immune system for protection against diseases. The innate immune system includes the circulating hemocytes (immune cells) that clear pathogens from hemolymph (blood) by phagocytosis, nodulation or encapsulation. Honey bee hemocyte numbers have been linked to hemolymph levels of vitellogenin. Vitellogenin is a multifunctional protein with immune-supportive functions identified in a range of species, including the honey bee. Hemocyte numbers can increase via mitosis, and this recruitment process can be important for immune system function and maintenance. Here, we tested if hemocyte mediated phagocytosis differs among the physiologically different honey bee worker castes (nurses, foragers and winter bees), and study possible interactions with vitellogenin and hemocyte recruitment. To this end, we adapted phagocytosis assays, which—together with confocal microscopy and flow cytometry—allow qualitative and quantitative assessment of hemocyte performance. We found that nurses are more efficient in phagocytic uptake than both foragers and winter bees. We detected vitellogenin within the hemocytes, and found that winter bees have the highest numbers of vitellogenin-positive hemocytes. Connections between phagocytosis, hemocyte-vitellogenin and mitosis were worker caste dependent. Our results demonstrate that the phagocytic performance of immune cells differs significantly between honey bee worker castes, and support increased immune competence in nurses as compared to forager bees. Our data, moreover, provides support for roles of vitellogenin in hemocyte activity.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.018410
Dataset: "Mixing Chemistry and Pigments: X-ray Fluorescence Spectroscopy as a Nondestructive Technique for Analysis of Pigments in a Painted Japanese Handscroll" ; F1907.375a, XRF 2018
No full textScanning x-ray fluorescence data
and relevant photodocumentation images of a Japanese handscroll, The Miraculous
Interventions of the Jizō Bosatsu (Freer Gallery of Art, Smithsonian
Institution, Washington, DC: Gift of Charles Lang Freer, F1907.375a). Scans
were performed in 2018 with a Bruker Tracer 5g handheld XRF. The XRF data
provided includes x and y position and the fitted element intensities for
relevant XRF peaks. Any negative intensity values have been replaced by a zero
value. The scanner x,y are rotated compared to the image; transforming the data
may be necessary for proper orientation (rotate 90 deg). The scanner x position
has been shifted to account for scanner travel direction. Data location is
described as the handscroll scene (S##), a sequential letter for the scan area
within the scene (e.g., A = 1), with a descriptor (e.g., woman).Only select scenes from the larger study are provided for use to accompany the publication: "Mixing
Chemistry and Pigments: X-ray Fluorescence Spectroscopy as a Nondestructive
Technique for Analysis of Pigments in a Painted Japanese Handscroll" by
Kathryn L. Rowberg, Grethe Hystad, Matthew L. Clarke, Jazmin Gonzalez, and
Johnathon M. Taylor in “Contextualizing Chemistry in Art & Archaeology:
Inspiration for Faculty” Editors: Kevin Braun and Kristin Labby. American
Chemical Society, 2021, pp 217-231, DOI: 10.1021/bk-2021-1386.ch010Details about the data collection
may be found in:Clarke, M.L., Gabrieli, F., Rowberg, K.L. et al. Imaging spectroscopies to characterize a 13th century Japanese handscroll, The Miraculous Interventions of Jizō Bosatsu. Herit Sci 9, 20 (2021). https://doi.org/10.1186/s40494-021-00497-1Image credit: Freer Gallery of Art, Smithsonian
Institution, Washington, DC: Gift of Charles Lang Freer, F1907.375a (detail) /
Department of Conservation and Scientific Research, Photograph by Jiro Ueda)</p
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Shifting Rural Quantification Towards Study of Health Disparities: Applications in Statistical and Spatial Analysis of Rural Colorectal Cancer
No full textQuantitative measurement of the urban-rural continuum for examination of U.S. rural health disparities is a relatively new research area, where only a handful of studies have investigated health disparities using quantitative rural measures and even fewer have attempted to integrate health variables within said measures. Most U.S. rural health disparity studies and more specifically, rural colorectal cancer disparity studies, utilize various categorical and demographics/economics-based rural coding systems, which were not created for health disparity research. Further, both categorical rural classification schemes and more recent attempts to build quantitative health-focused measures are spatially and temporally static, which reduces translatability for study of cancer disparities across spatial units and time frames. In other words, to the knowledge of the author, no previous research has produced health-focused quantitative rural metrics that can be both flexibly translated to match the relevant time frames of health datasets and upscaled/downscaled to the desired spatial unit of analysis. Finally, spatial principles are inconsistently applied in rural colorectal cancer disparity studies, reducing inferential ability from results. Colorectal cancer is considered one of the most burdensome cancers for U.S. rural areas, so improvement of both measurement of the urban-rural divide for study of health disparities and application of spatial methods may help solidify findings of previous work. In this manuscript, there were two overarching goals: 1) construct spatiotemporal health disparity-focused continuous measurements of rural disadvantage that could be upscaled and downscaled and 2) utilize statistical and spatial methods to assess relationships between rural disadvantage and U.S. colorectal cancer mortality and screening.
In Chapter 1, a county-level rural disadvantage index with integrated health factors was constructed using principal component analysis to weight ten rural indicator variables in three rural dimensions, followed by application to overall county-level cancer mortality via quantile regression. To the knowledge of the author, the index produced in this chapter is the first county-level quantitative rural measure with integrated health variables. Based on choropleth mapping, the constructed index showed improved numeric range and gradient over a popular existing rural measure while still retaining expected urban/rural trends. Spatiotemporal analysis showed only gradual change in index values for most U.S. counties, indicating stability over time. Results of the quantile regression showed that higher rural disadvantage index values were associated with higher cancer mortality rates, reflecting previous rural cancer disparity work. However, this effect was only present in the upper deciles of the probability distribution of mortality rates, indicating more complexity than previously understood. The county rural disadvantage index computed in this chapter should be considered a first step in attempting to integrate health variables into county-level quantitative rural measures.
In Chapter 2, I applied the county-level rural disadvantage index to both global and local spatial models to explore rural colorectal cancer mortality and screening disparities. For the global mortality and screening linear models, Moran eigenvector spatial filtering was utilized to remove spatial autocorrelation from the residuals, while for the local models, geographically weighted regressions were used to determine if spatial non-stationarity existed in the relationships between rural disadvantage and both mortality and screening rates. To the best knowledge of the author, this paper constitutes the first instance of Moran eigenvector spatial filtering being used for spatial analysis of colorectal cancer mortality and screening. The global spatially filtered models displayed increasing colorectal cancer mortality rates and decreasing colorectal screening rates, respectively, as rural disadvantage increased, which reflected findings from previous work. In comparison to base global linear models, however, the magnitudes of effect of the spatially filtered models were reduced, displaying the importance of modeling spatial nuance. The geographically weighted regressions suggested that spatial non-stationarity existed in relationships between rural disadvantage and both mortality and screening, indicating the utility of local modeling. This Chapter provided a spatial modeling framework on which future rural colorectal cancer disparity analyses can account for spatial autocorrelation and spatial non-stationarity.
In Chapter 3, the same rural indicator variables used in Chapter 1 were extended to construction of a sub-neighborhood grid-based rural disadvantage index for the state of Texas. A negative binomial hurdle model was then fit to examine the association between gridded index values and high spatial resolution colorectal cancer incidence-based mortality rates, while empirical Bayesian kriging and a spatial union procedure were also utilized to identify high colorectal cancer mortality risk-at-diagnosis areas. The rural disadvantage index produced in this chapter is the first sub-neighborhood quantitative rural measure produced for the state of Texas and the third sub-neighborhood quantitative rural measure for the U.S. more generally. Moreover, this paper is the first instance of empirical Bayesian kriging being utilized for cancer outcome spatial point data. Choropleth mapping showed that the constructed index had improved numeric range and gradient over an existing high resolution rural measure while mostly retaining expected urban/rural structure. The negative binomial hurdle model found that among Texas grid cells with at least one death, incidence-based mortality rates increased significantly as rural disadvantage values increased. The empirical Bayesian kriging procedure successfully identified high colorectal cancer mortality risk-at-diagnosis areas for the state of Texas, while the spatial union procedure displayed where these areas overlap with high rural disadvantage areas. The resulting sub-neighborhood maps have potential to inform where funding, colorectal cancer screening, and/or clinical services may best be micro-targeted
FUTURE AND CURRENT APPROACHES FOR MODELING THE DISTRIBUTION OF MINERALS ON EARTH AND OTHER PLANETS
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