Mycotic keratitis caused by concurrent infections of exserohilum mcginnisii and candida parapsilosis

BACKGROUND: Mycotic keratitis in human cornea has been rarely reported to be associated with a co-infection of filamentous fungi and yeast. This paper aims to report a case of mycotic keratitis concurrently infected by Exserohilum mcginnisii and Candida parapsilosis. CASE PRESENTATION: A Chinese female presented two superposed corneal infiltrates with different size and texture on her left eye. In vivo confocal microscopy showed hyper-reflective multiple linear with highly branching structures distributing in the anterior corneal stroma. Inoculations of the corneal lesion scrape concurrently grew two similar superposed colonies on Sabouraud dextrose and chocolate agar plate. The larger colony exhibited mould, cottony and floccose at the edge, while the smaller one showed creamy and shiny surface. Modified slide culture for mould revealed hyphae were septate, and conidia were brown, smooth-walled, cylindrical to slight clavate with 6 to 13 pseudosepta. Based on the morphology of microscopic and macroscopic characteristics, the mould was identified as Exserohilum mcginnisii. Smear of the non-mould colony showed ellipse or ovoid budding yeast-like cells with abundant pseudomycelium. Vitek Yeast Biochemical Card test identified the yeast as Candida parapsilosis. With treatment of combined oral itraconazole with topical amphotericin B, a complete resolution of the corneal infiltrate was achieved within 1.5 months. CONCLUSION: This is the first documented case of human corneal infection by Exserohilum mcginnisii, and also the first report providing evidence of mycotic keratitis in human cornea concurrently infected by filamentous fungi and yeast

Multi-modal Facial Affective Analysis based on Masked Autoencoder

Human affective behavior analysis focuses on analyzing human expressions or other behaviors to enhance the understanding of human psychology. The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Aff-wild2 for the recognition of commonly used emotion representations, such as Action Units (AU), basic expression categories(EXPR), and Valence-Arousal (VA). The competition is committed to making significant strides in improving the accuracy and practicality of affective analysis research in real-world scenarios. In this paper, we introduce our submission to the CVPR 2023: ABAW5. Our approach involves several key components. First, we utilize the visual information from a Masked Autoencoder(MAE) model that has been pre-trained on a large-scale face image dataset in a self-supervised manner. Next, we finetune the MAE encoder on the image frames from the Aff-wild2 for AU, EXPR and VA tasks, which can be regarded as a static and uni-modal training. Additionally, we leverage the multi-modal and temporal information from the videos and implement a transformer-based framework to fuse the multi-modal features. Our approach achieves impressive results in the ABAW5 competition, with an average F1 score of 55.49\% and 41.21\% in the AU and EXPR tracks, respectively, and an average CCC of 0.6372 in the VA track. Our approach ranks first in the EXPR and AU tracks, and second in the VA track. Extensive quantitative experiments and ablation studies demonstrate the effectiveness of our proposed method