RFARN: Retinal vessel segmentation based on reverse fusion attention residual network.

Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, this makes accurate segmentation of blood vessels from retinal images still challenging. In this paper, we propose an effective framework for retinal vascular segmentation, which is innovative mainly in the retinal image pre-processing stage and segmentation stage.

MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.

Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results.

Efficient BFCN for Automatic Retinal Vessel Segmentation.

Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor.

Corrigendum to "Efficient BFCN for Automatic Retinal Vessel Segmentation".

[This corrects the article DOI: 10.1155/2020/6439407.].

Effects of Steel-Slag Components on Interfacial-Reaction Characteristics of Permeable Steel-Slag-Bitumen Mixture.

In this paper, a permeable steel-slag-bitumen mixture (PSSBM) was first prepared according to the designed mixture ratio. Then, the interaction characteristics between steel slag and bitumen were studied. The chemical interaction between bitumen and steel slag was explored with a Fourier-transform infrared spectrometer (FT-IR).

The Interfacial Adhesion Performance and Mechanism of a Modified Asphalt-Steel Slag Aggregate.

The interfacial adhesion between asphalt and steel slag aggregate is a decisive factor in the formation of an asphalt-steel slag mixture and significantly affects the quality stability of steel slag-asphalt mixtures. In this study, the adhesion between an asphalt and steel slag aggregate, the interfacial microstructure, the adsorption and desorption characteristics, and chemical reactions were, respectively, explored by a PosiTestAT-A adhesion puller, a scanning electron microscope, a net adsorption test, an infrared spectrometer, and a dynamic shear rheometer. The mechanism of adhesion between the asphalt and steel slag aggregate was analyzed from the perspectives of physical adsorption and chemical reactions.

Properties of a Steel Slag-Permeable Asphalt Mixture and the Reaction of the Steel Slag-Asphalt Interface.

Steel slag is an industrial solid waste with the largest output in the world. It has the characteristics of wear resistance, good particle shape, large porosity, etc. At the same time, it has good adhesion characteristics with asphalt.

Flux balance analysis predicts Warburg-like effects of mouse hepatocyte deficient in miR-122a.

The liver is a vital organ involving in various major metabolic functions in human body. MicroRNA-122 (miR-122) plays an important role in the regulation of liver metabolism, but its intrinsic physiological functions require further clarification. This study integrated the genome-scale metabolic model of hepatocytes and mouse experimental data with germline deletion of Mir122a (Mir122a-/-) to infer Warburg-like effects.