47 research outputs found

    Contextualising Title Pages by Material Culture: Typography & List of Rarities A Case Study Don Saltero’s Coffeehouse Catalogues, 1729 – 1795.

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    Using A Catalogue of the Rarities to Be Seen at Don Saltero’s Coffeehouse in Chelsea. To Which Is Added, a Compleat List of the Donors Thereof - published in London, England between 1729 and 1795, this case study contextualises the catalogue’s title pages through a change over time examination of the typography and then listed of rarities through strategies from material culture to understand better the intersecting identities floating around the public sphere. What was reflected were characteristics of religion, nationhood, and gender. Don Saltero’s rarities catalogues were a topic of discussion for patrons of Don Saltero’s coffeehouse in London. Catalogues analysed in this research existed in the coffee house environment, private homes, and wherever these catalogues ended up. The catalogues added legitimacy to the collections they accompanied and did so by placing objects within various Enlightenment discussions and tying the listed objects to contemporary cultural knowledge. Additionally, the object’s descriptions allowed spectators and readers to interact with the ‘science’ of the emerging field of natural history. They presented catalogues in a way that emulated emerging scientific works within the academic sphere of the natural world. The sources used here gained further fame and legitimacy through the connection to well-known naturalist Sir Hans Sloane, a physician to the royal family, president of the Royal Society, and founder of the British Museum. Owning rarity collections was often an elite enterprise, but a collection’s stories were deliberately pitched to a much broader audience offering access to the collections and the ideas they represented. Thus, these catalogues add significance to their collections by expanding public discourse on objects known as rarities

    Glacial and geomorphic effects of a supraglacial lake drainage and outburst event, Everest region, Nepal Himalaya

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    A set of supraglacial ponds filled rapidly between April and July 2017 on Changri Shar Glacier in the Everest region of Nepal, coalescing into a similar to 180 000 m(2) lake before sudden and complete drainage through Changri Shar and Khumbu glaciers (15-17 July). We use PlanetScope and Pleiades satellite orthoimagery to document the system's evolution over its very short filling period and to assess the glacial and proglacial effects of the outburst flood. We also use high-resolution stereo digital elevation models (DEMs) to complete a detailed analysis of the event's glacial and geomorphic effects. Finally, we use discharge records at a stream gauge 4 km downstream to refine our interpretation of the chronology and magnitude of the outburst. We infer largely subsurface drainage through both of the glaciers located on its flow path, and efficient drainage through the lower portion of Khumbu Glacier. The drainage and subsequent outburst of 1.36 +/- 0.19 x 10(6) m(3) of impounded water had a clear geomorphic impact on glacial and proglacial topography, including deep incision and landsliding along the Changri Nup proglacial stream, the collapse of shallow englacial conduits near the Khumbu terminus and extensive, enhanced bank erosion at least as far as 11 km downstream below Khumbu Glacier. These sudden changes destroyed major trails in three locations, demonstrating the potential hazard that short-lived, relatively small glacial lakes pose

    Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

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    Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all meta-analyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary

    Improving fluid registration through white matter segmentation in a twin study design

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    Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry

    A non-conservative Lagrangian framework for statistical fluid registration: SAFIRA

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    In this paper, we used a non-conservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3D brain images. This algorithm is named SAFIRA, acronym for Statistically-Assisted Fluid Image Registration Algorithm. A non-statistical version of this algorithm was implemented [9], where the deformation was regularized by penalizing deviations from a zero rate of strain. In [9], the terms regularizing the deformation included the covariance of the deformation matrices () and the vector fields (q). Here we used a Lagrangian framework to re-formulate this algorithm, showing that the regularizing terms essentially allow non-conservative work to occur during the flow. Given 3D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the non-conservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms performance on 92 3D brain scans from healthy monozygotic and dizygotic twins; 2D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for largescale neuroimaging studies