Linköping University

About: Linköping University is a education organization based out in Linköping, Sweden. It is known for research contribution in the topics: Population & Health care. The organization has 15671 authors who have published 50013 publications receiving 1542189 citations.

Transancestral mapping and genetic load in systemic lupus erythematosus

Abstract: Systemic lupus erythematosus (SLE) is an autoimmune disease with marked gender and ethnic disparities. We report a large transancestral association study of SLE using Immunochip genotype data from 27,574 individuals of European (EA), African (AA) and Hispanic Amerindian (HA) ancestry. We identify 58 distinct non-HLA regions in EA, 9 in AA and 16 in HA (∼50% of these regions have multiple independent associations); these include 24 novel SLE regions (P<5 × 10-8), refined association signals in established regions, extended associations to additional ancestries, and a disentangled complex HLA multigenic effect. The risk allele count (genetic load) exhibits an accelerating pattern of SLE risk, leading us to posit a cumulative hit hypothesis for autoimmune disease. Comparing results across the three ancestries identifies both ancestry-dependent and ancestry-independent contributions to SLE risk. Our results are consistent with the unique and complex histories of the populations sampled, and collectively help clarify the genetic architecture and ethnic disparities in SLE.

Homoepitaxial growth of β-Ga2O3 layers by halide vapor phase epitaxy

Abstract: Thick high-purity β-Ga2O3 layers of high crystalline quality were grown homoepitaxially by halide vapor phase epitaxy (HVPE) using gaseous GaCl and O2 on (001) β-Ga2O3 substrates prepared by edge-defined film-fed growth. The surface morphology and structural quality of the grown layer improved with increasing growth temperature. X-ray diffraction ω-rocking curves for the (002) and (400) reflections for the layer grown at 1000 °C had small full widths at half maximum. Secondary ion mass spectrometry and electrical characteristics revealed that the growth of high-purity β-Ga2O3 layers with low effective donor concentration (Nd − Na < 1013 cm−3) is possible by HVPE.

Unveiling the Power of Deep Tracking

Abstract: In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking. We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness. We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking. Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy. Extensive experiments are performed on four challenging datasets. On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of 17% in EAO.