Aspergillus fumigatus is exceptional among microorganisms in being both a primary and opportunistic pathogen as well as a major allergen. Its conidia production is prolific, and so human respiratory ...tract exposure is almost constant. A. fumigatus is isolated from human habitats and vegetable compost heaps. In immunocompromised individuals, the incidence of invasive infection can be as high as 50% and the mortality rate is often about 50% (ref. 2). The interaction of A. fumigatus and other airborne fungi with the immune system is increasingly linked to severe asthma and sinusitis. Although the burden of invasive disease caused by A. fumigatus is substantial, the basic biology of the organism is mostly obscure. Here we show the complete 29.4-megabase genome sequence of the clinical isolate Af293, which consists of eight chromosomes containing 9,926 predicted genes. Microarray analysis revealed temperature-dependent expression of distinct sets of genes, as well as 700 A. fumigatus genes not present or significantly diverged in the closely related sexual species Neosartorya fischeri, many of which may have roles in the pathogenicity phenotype. The Af293 genome sequence provides an unparalleled resource for the future understanding of this remarkable fungus.
Key recognition tasks such as fine-grained visual categorization (FGVC) have benefited from increasing attention among computer vision researchers. The development and evaluation of new approaches ...relies heavily on benchmark datasets; such datasets are generally built primarily with categories that have images readily available, omitting categories with insufficient data. This paper takes a step back and rethinks dataset construction, focusing on intelligent image collection driven by: (i) the inclusion of all desired categories, and, (ii) the recognition performance on those categories. Based on a small, author-provided initial dataset, the proposed system recommends which categories the authors should prioritize collecting additional images for, with the intent of optimizing overall categorization accuracy. We show that mock datasets built using this method outperform datasets built without such a guiding framework. Additional experiments give prospective dataset creators intuition into how, based on their circumstances and goals, a dataset should be constructed.
While the community has seen many advances in recent years to address the challenging problem of Fine-grained Visual Categorization (FGVC), progress seems to be slowing-new state-of-the-art methods ...often distinguish themselves by improving top-1 accuracy by mere tenths of a percent. However, across all of the now-standard FGVC datasets, there remain sizeable portions of the test data that none of the current state-of-the-art (SOTA) models can successfully predict. This paper provides a framework for identifying and studying the errors that current methods make across diverse fine-grained datasets. Three models of difficulty-Prediction Overlap, Prediction Rank and Pair-wise Class Confusion-are employed to highlight the most challenging sets of images and classes. Extensive experiments apply a range of standard and SOTA methods, evaluating them on multiple FGVC domains and datasets. Insights acquired from coupling these difficulty paradigms with the careful analysis of experimental results suggest crucial areas for future FGVC research, focusing critically on the set of elusive images that none of the current models can correctly classify. Code is available at catalys1.github.io/elusive-images-fgvc.
For the task of image classification, researchers work arduously to develop the next state-of-the-art (SOTA) model, each bench-marking their own performance against that of their predecessors and of ...their peers. Unfortunately, the metric used most frequently to describe a model's performance, average categorization accuracy, is often used in isolation. As the number of classes increases, such as in fine-grained visual categorization (FGVC), the amount of information conveyed by average accuracy alone dwindles. While its most glaring weakness is its failure to describe the model's performance on a class-by-class basis, average accuracy also fails to describe how performance may vary from one trained model of the same architecture, on the same dataset, to another (both averaged across all categories and at the per-class level). We first demonstrate the magnitude of these variations across models and across class distributions based on attributes of the data, comparing results on different visual domains and different per-class image distributions, including long-tailed distributions and few-shot subsets. We then analyze the impact various FGVC methods have on overall and per-class variance. From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.
Our genome-wide association study of celiac disease previously identified risk variants in the IL2-IL21 region. To identify additional risk variants, we genotyped 1,020 of the most strongly ...associated non-HLA markers in an additional 1,643 cases and 3,406 controls. Through joint analysis including the genome-wide association study data (767 cases, 1,422 controls), we identified seven previously unknown risk regions (P < 5 x 10(-7)). Six regions harbor genes controlling immune responses, including CCR3, IL12A, IL18RAP, RGS1, SH2B3 (nsSNP rs3184504) and TAGAP. Whole-blood IL18RAP mRNA expression correlated with IL18RAP genotype. Type 1 diabetes and celiac disease share HLA-DQ, IL2-IL21, CCR3 and SH2B3 risk regions. Thus, this extensive genome-wide association follow-up study has identified additional celiac disease risk variants in relevant biological pathways.
For the task of image classification, researchers work arduously to develop the next state-of-the-art (SOTA) model, each bench-marking their own performance against that of their predecessors and of ...their peers. Unfortunately, the metric used most frequently to describe a model's performance, average categorization accuracy, is often used in isolation. As the number of classes increases, such as in fine-grained visual categorization (FGVC), the amount of information conveyed by average accuracy alone dwindles. While its most glaring weakness is its failure to describe the model's performance on a class-by-class basis, average accuracy also fails to describe how performance may vary from one trained model of the same architecture, on the same dataset, to another (both averaged across all categories and at the per-class level). We first demonstrate the magnitude of these variations across models and across class distributions based on attributes of the data, comparing results on different visual domains and different per-class image distributions, including long-tailed distributions and few-shot subsets. We then analyze the impact various FGVC methods have on overall and per-class variance. From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.
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