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.

Mitochondrial Turnover and Aging of Long-Lived Postmitotic Cells: The Mitochondrial–Lysosomal Axis Theory of Aging

Abstract: It is now generally accepted that aging and eventual death of multicellular organisms is to a large extent related to macromolecular damage by mitochondrially produced reactive oxygen species, mostly affecting long-lived postmitotic cells, such as neurons and cardiac myocytes. These cells are rarely or not at all replaced during life and can be as old as the whole organism. The inherent inability of autophagy and other cellular-degradation mechanisms to remove damaged structures completely results in the progressive accumulation of garbage, including cytosolic protein aggregates, defective mitochondria, and lipofuscin, an intralysosomal indigestible material. In this review, we stress the importance of crosstalk between mitochondria and lysosomes in aging. The slow accumulation of lipofuscin within lysosomes seems to depress autophagy, resulting in reduced turnover of effective mitochondria. The latter not only are functionally deficient but also produce increased amounts of reactive oxygen species, prompting lipofuscinogenesis. Moreover, defective and enlarged mitochondria are poorly autophagocytosed and constitute a growing population of badly functioning organelles that do not fuse and exchange their contents with normal mitochondria. The progress of these changes seems to result in enhanced oxidative stress, decreased ATP production, and collapse of the cellular catabolic machinery, which eventually is incompatible with survival.

High-efficiency perovskite–polymer bulk heterostructure light-emitting diodes

Abstract: Perovskite-based optoelectronic devices are gaining much attention owing to their remarkable performance and low processing cost, particularly for solar cells. However, for perovskite light-emitting diodes, non-radiative charge recombination has limited the electroluminescence efficiency. Here we demonstrate perovskite–polymer bulk heterostructure light-emitting diodes exhibiting external quantum efficiencies of up to 20.1% (at current densities of 0.1–1 mA cm−2). The light-emitting diode emissive layer comprises quasi-two-dimensional and three-dimensional (2D/3D) perovskites and an insulating polymer. Photogenerated excitations migrate from quasi-2D to lower-energy sites within 1 ps, followed by radiative bimolecular recombination in the 3D regions. From near-unity external photoluminescence quantum efficiencies and transient kinetics of the emissive layer with and without charge-transport contacts, we find non-radiative recombination pathways to be effectively eliminated, consistent with optical models giving near 100% internal quantum efficiencies. Although the device brightness and stability (T50 = 46 h in air at peak external quantum efficiency) require further improvement, our results indicate the significant potential of perovskite-based photon sources.

DUNet: A deformable network for retinal vessel segmentation

Abstract: Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.