Uppsala University

About: Uppsala University is a education organization based out in Uppsala, Sweden. It is known for research contribution in the topics: Population & Gene. The organization has 36485 authors who have published 107509 publications receiving 4220668 citations. The organization is also known as: Uppsala universitet & uu.se.

Large–scale geographical variation confirms that climate change causes birds to lay earlier

Abstract: Advances in the phenology of organisms are often attributed to climate change, but alternatively, may reflect a publication bias towards advances and may be caused by environmental factors unrelated to climate change. Both factors are investigated using the breeding dates of 25 long-term studied populations of Ficedula flycatchers across Europe. Trends in spring temperature varied markedly between study sites, and across populations the advancement of laying date was stronger in areas where the spring temperatures increased more, giving support to the theory that climate change causally affects breeding date advancement.

Effects of environment on compensatory mutations to ameliorate costs of antibiotic resistance.

Abstract: Most types of antibiotic resistance impose a biological cost on bacterial fitness. These costs can be compensated, usually without loss of resistance, by second-site mutations during the evolution of the resistant bacteria in an experimental host or in a laboratory medium. Different fitness-compensating mutations were selected depending on whether the bacteria evolved through serial passage in mice or in a laboratory medium. This difference in mutation spectra was caused by either a growth condition-specific formation or selection of the compensated mutants. These results suggest that bacterial evolution to reduce the costs of antibiotic resistance can take different trajectories within and outside a host.

Convex set functions ind-space

Abstract: Given subsets A and B of Euclidean d-space R a and 0 ~ 0, we set A + B -{x + Y l x E A, y E B} and OA = {Ox Ix 6 A }. Further given a convex subset g2 of R d we shall say that a set function F : 2 ~ \ {~} ~ [0, + ~ ] is convex (concave} if the inequality F(OA + (1 O)B) ~ Or(A} + (1 0 ) / ' ( B ) (>=.) holds for all It ~ A, B c_ D, and all 0 < 0 < 1. Here we shall s tudy such set functions of the special form given in the following

Deep learning based tissue analysis predicts outcome in colorectal cancer

Abstract: Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.