Acute Retinal Necrosis Associated with Pseudorabies Virus Infection: A Case Report and Literature Review.

PURPOSE: To analyze a case of acute retinal necrosis (ARN) associated with pseudorabies virus (PRV) infection and discusses the clinical characteristics of PRV-induced ARN (PRV-ARN). METHODS: Case report and literature review of ocular features in PRV-ARN. RESULTS: A 52-year-old female diagnosed with encephalitis presented with bilateral vision loss, mild anterior uveitis, vitreous opacity, occlusive retinal vasculitis, and retinal detachment in her left eye. The result of metagenomic next-generation sequencing (mNGS) indicated that both cerebrospinal fluids and vitreous fluid tested positive for PRV. CONCLUSION: PRV, a zoonosis, can infect both humans and mammals. Patients affected with PRV may experience severe encephalitis and oculopathy, and the infection has been associated with high mortality and disability. ARN is the most common ocular disease, which develops rapidly following encephalitis and is characterized by five figures: bilateral onset, rapid progression, severe visual impairment, poor response to systemic antiviral drugs, and an unfavorable prognosis.

Output-Related and -Unrelated Fault Monitoring with an Improvement Prototype Knockoff Filter and Feature Selection Based on Laplacian Eigen Maps and Sparse Regression.

In the process industry, fault monitoring related to output is an important step to ensure product quality and improve economic benefits. In order to distinguish the influence of input variables on the output more accurately, this paper introduces a subalgorithm of fault-unrelated block partition into the prototype knockoff filter (PKF) algorithm for its improvement. The improved PKF algorithm can divide the input data into three blocks: fault-unrelated block, output-related block, and output-unrelated block. Removing the data of fault-unrelated blocks can greatly reduce the difficulty of fault monitoring. This paper proposes a feature selection based on the Laplacian Eigen maps and sparse regression algorithm for output-unrelated blocks. The algorithm has the ability to detect faults caused by variables with small contribution to variance and proves the descent of the algorithm from a theoretical point of view. The output relation block is monitored by the Broyden-Fletcher-Goldfarb-Shanno method. Finally, the effectiveness of the proposed fault detection method is verified by the recognized Eastman process data in Tennessee.