A case report of donovanosis in HIV-positive female.

Donovanosis is a chronic granulomatous ulcerative sexually transmitted infection caused by Klebsiella (Calymmatobacterium) granulomatis. A 39-year-old female patient with underlying HIV infection presented to the department of dermatology outpatient department with a painless ulcer over the left labia majora for 3 months. Histopathological examination revealed histiocyte which contains granular material resembling coccobacilli and Giemsa staining was positive for Donovan bodies. She was treated with doxycycline 100 mg twice daily and azithromycin 1 g once weekly for 3 weeks and further azithromycin 1 g weekly for the next 9 weeks till complete healing of the lesion. Due to the rarity of this condition in our region, we present this case of donovanosis in an HIV-positive female patient.

Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4.

Remote eye tracking has become an important tool for the online analysis of learning processes. Mobile eye trackers can even extend the range of opportunities (in comparison to stationary eye trackers) to real settings, such as classrooms or experimental lab courses. However, the complex and sometimes manual analysis of mobile eye-tracking data often hinders the realization of extensive studies, as this is a very time-consuming process and usually not feasible for real-world situations in which participants move or manipulate objects. In this work, we explore the opportunities to use object recognition models to assign mobile eye-tracking data for real objects during an authentic students' lab course. In a comparison of three different Convolutional Neural Networks (CNN), a Faster Region-Based-CNN, you only look once (YOLO) v3, and YOLO v4, we found that YOLO v4, together with an optical flow estimation, provides the fastest results with the highest accuracy for object detection in this setting. The automatic assignment of the gaze data to real objects simplifies the time-consuming analysis of mobile eye-tracking data and offers an opportunity for real-time system responses to the user's gaze. Additionally, we identify and discuss several problems in using object detection for mobile eye-tracking data that need to be considered.