Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms

2-Selenouridine Triphosphate Synthesis and Se-RNA Transcription

2-Selenouridine (SeU) is one of the naturally occurring modifications of Se-tRNAs (SeU-RNA) at the wobble position of the anticodon loop. Its role in the RNA-RNA interaction, especially during the mRNA decoding, is elusive. To assist the research exploration, herein we report the enzymatic synthesis of the SeU-RNA via 2-selenouridine triphosphate (SeUTP) synthesis and RNA transcription. Moreover, we have demonstrated that the synthesized SeUTP is stable and recognizable by T7 RNA polymerase. Under the optimized conditions, the transcription yield of SeU-RNA can reach up to 85% of the corresponding native RNA. Furthermore, the transcribed SeU-hammerhead ribozyme has the similar activity as the corresponding native, which suggests usefulness of SeU-RNAs in function and structure studies of noncoding RNAs, including the Se-tRNAs

Autologistic network model on binary data for disease progression study

This paper focuses on analysis of spatiotemporal binary data with absorbing states. The research was motivated by a clinical study on amyotrophic lateral sclerosis (ALS), a neurological disease marked by gradual loss of muscle strength over time in multiple body regions. We propose an autologistic regression model to capture complex spatial and temporal dependencies in muscle strength among different muscles. As it is not clear how the disease spreads from one muscle to another, it may not be reasonable to define a neighborhood structure based on spatial proximity. Relaxing the requirement for prespecification of spatial neighborhoods as in existing models, our method identifies an underlying network structure empirically to describe the pattern of spreading disease. The model also allows the network autoregressive effects to vary depending on the muscles’ previous status. Based on the joint distribution derived from this autologistic model, the joint transition probabilities of responses among locations can be estimated and the disease status can be predicted in the next time interval. Model parameters are estimated through maximization of penalized pseudo‐likelihood. Postmodel selection inference was conducted via a bias‐correction method, for which the asymptotic distributions were derived. Simulation studies were conducted to evaluate the performance of the proposed method. The method was applied to the analysis of muscle strength loss from the ALS clinical study.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/1/biom13111.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/2/biom13111-sup-0001-autolog_supp-biom.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/3/biom13111-sup-0003-supmat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152664/4/biom13111_am.pd

Efferocytosis signatures as prognostic markers for revealing immune landscape and predicting immunotherapy response in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a highly lethal liver cancer with late diagnosis; therefore, the identification of new early biomarkers could help reduce mortality. Efferocytosis, a process in which one cell engulfs another cell, including macrophages, dendritic cells, NK cells, etc., plays a complex role in tumorigenesis, sometimes promoting and sometimes inhibiting tumor development. However, the role of efferocytosis-related genes (ERGs) in HCC progression has been poorly studied, and their regulatory effects in HCC immunotherapy and drug targeting have not been reported.Methods: We downloaded efferocytosis-related genes from the Genecards database and screened for ERGs that showed significant expression changes between HCC and normal tissues and were associated with HCC prognosis. Machine learning algorithms were used to study prognostic gene features. CIBERSORT and pRRophetic R packages were used to evaluate the immune environment of HCC subtypes and predict treatment response. CCK-8 experiments conducted on HCC cells were used to assess the reliability of drug sensitivity prediction.Results: We constructed a prognostic prediction model composed of six genes, and the ROC curve showed good predictive accuracy of the risk model. In addition, two ERG-related subgroups in HCC showed significant differences in tumor immune landscape, immune response, and prognostic stratification. The CCK-8 experiment conducted on HCC cells confirmed the reliability of drug sensitivity prediction.Conclusion: Our study emphasizes the importance of efferocytosis in HCC progression. The risk model based on efferocytosis-related genes developed in our study provides a novel precision medicine approach for HCC patients, allowing clinicians to customize treatment plans based on unique patient characteristics. The results of our investigation carry noteworthy implications for the development of individualized treatment approaches involving immunotherapy and chemotherapy, thereby potentially facilitating the realization of personalized and more efficacious therapeutic interventions for HCC

Microstructural and Electron-Emission Characteristics of Nb-Si-N Films in Surface-Conduction Electron-Emitter Display

AbstractWe proposed ternary nitride Nb-Si-N film as a promising surface-conduction electron emitter (SCE) in surface-conduction electron-emitter display (SED). Nb-Si-N films consisted of continuous NbN polycrystalline phase with (Si3-xNb4x)N4 amorphous phase in NbN grain boundaries. After electroforming, serrated nanogaps were observed in Nb-Si-N SCE strips. The emission current of Nb-Si-N SCE array of 1×18 cells was 6.50μA with anode voltage of 1.5kV and device voltage of 22V, indicating satisfying potential for display applications comparing with NbN SCEs. © 2009 Published by Elsevier B.V

An Evaluation of the Mellor-Yamada-Janjić Formulation Parameters for the QNSE Scheme in the WRF Model over the Lower Yangtze River Valley

Accurate description of boundary layer processes is important for numerical simulations, and some model parameters in the boundary layer schemes play an important role in the model simulations. The Quasi-Normal Scale Elimination (QNSE) scheme in the Weather Research and Forecasting (WRF) model version 3.1.1 reverts into the Mellor-Yamada-Janjić (MYJ) model under unstable and neutral conditions. The parameters (A1, A2, B1, B2, C1) that affect the turbulent mixing in the MYJ formulation are the proportional coefficients of turbulence length scales and the master turbulence length scale. This study examines the model simulations sensitivity to different MYJ parameters. The simulation results show that MYJ parameters play a significant role in rainfall simulations. The analysis results imply that the parameters may affect the rainfall mainly by changing turbulent mixing and coupling with other physical process, such as cumulus convection processes, and then changing heat, momentum, and moisture transfer. The previous parameters used in the original MYJ formulation are not always the best and none of the parameters are always the best. It may be more appropriate that the parameters should be adopted in their plausible physical bounds depending on the planetary boundary layer (PBL) structures characteristics under specific meteorological and geographical circumstances