Clinical Features of Dominant and Recessive Interferon γ Receptor 1 Deficiencies

Background Interferon ? receptor 1 (IFN? R1) deficiency is a primary immunodeficiency with allelic dominant and recessive mutations characterised clinically by severe infections with mycobacteria. We aimed to compare the clinical features of recessive and dominant IFN?R1 deficiencies. Methods We obtained data from a large cohort of patients worldwide. We assessed these people by medical histories, records, and genetic and immunological studies. Data were abstracted onto a standard form. Findings We identified 22 patients with recessive complete IFN?R1 deficiency and 38 with dominant partial deficiency. BCG and environmental mycobacteria were the most frequent pathogens. In recessive patients, 17 (77%) had environmental mycobacterial disease and all nine BCG-vaccinated patients had BCG disease. In dominant patients, 30 (79%) had environmental mycobacterial disease and 11 (73%) of 15 BCG-vaccinated patients had BCG disease. Compared with dominant patients, those with recessive deficiency were younger at onset of first environmental mycobacterial disease (mean 3·1 years [SD 2·5] vs 13·4 years [14·3], p=0·001), had more mycobacterial disease episodes (19 vs 8 per 100 person-years of observation, p=0·0001), had more severe mycobacterial disease (mean number of organs infected by Mycobacterium avium complex 4·1 [SD 0·8] vs 2·0 [1·1], p=0·004), had shorter mean disease-free intervals (1·6 years [SD 1·4] vs 7·2 years [7·6], p

An FPGA Based Coprocessor for GLCM and Haralick Texture Features and Their Application in Prostate Cancer Classification

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.

Cartographic veracity in medieval mapping: analyzing geographical variation in the Gough Map of Great Britain

This article explores statistical approaches for assessing the relative accuracy of medieval mapping. It focuses on one particular map, the Gough Map of Great Britain. This is an early and remarkable example of a medieval “national” map covering Plantagenet Britain. Conventionally dated to c. 1360, the map shows the position of places in and coastal outline of Great Britain to a considerable degree of spatial accuracy. In this article, aspects of the map's content are subjected to a systematic analysis to identify geographical variations in the map's veracity, or truthfulness. It thus contributes to debates among historical geographers and cartographic historians on the nature of medieval maps and mapping and, in particular, questions of their distortion of geographic space. Based on a newly developed digital version of the Gough Map, several regression-based approaches are used here to explore the degree and nature of spatial distortion in the Gough Map. This demonstrates that not only are there marked variations in the positional accuracy of places shown on the map between regions (i.e., England, Scotland, and Wales), but there are also fine-scale geographical variations in the spatial accuracy of the map within these regions. The article concludes by suggesting that the map was constructed using a range of sources, and that the Gough Map is a composite of multiscale representations of places in Great Britain. The article details a set of approaches that could be transferred to other contexts and add value to historic maps by enhancing understanding of their contents.