Efficient and precise cell counting for RNAi screening of Orientia tsutsugamushi infection using deep learning techniques

Acquiring fluorescent scrub typhus images obtained through RNA interference screening for the analysis of 60 different human genes and 18 control genes poses challenges due to nonuniform or clumped cells and variations in image quality, rendering image processing (IP) counting inadequate. This study addresses three key questions concerning the application of deep learning methods to this dataset. Firstly, it explores the potential for object detection (OD) models to replace instance segmentation (IS) models in cell counting, striking a balance between accuracy and computational efficiency. Object detection models, including Faster R-CNN, You Only Look Once (YOLO), and Adaptive Training Sample Selection (ATSS) with reduced backbone sizes, outperform the instance segmentation model (Mask Region-Based Convolutional Neural Network: Mask R-CNN, Cascade Mask-RCNN) with both deep and shallow backbones. Notably, ATSS with Resnet-50 achieves an impressive mean average precision of 0.884 in just 33.1 milliseconds. Secondly, reducing the feature extractor size enhances cell counting efficiency, with OD models featuring reduced backbones demonstrating improved performance and faster processing. Finally, deep learning, especially OD models with shallow backbones, outperforms IP methods in both absolute and relative cell counting. This study demonstrates the potential for OD models to replace IS models, the efficiency gains achieved through the reduction of feature extractors, and the superiority of DL over IP in cell counting tasks

Speed meets accuracy: Advanced deep learning for efficient Orientia tsutsugamushi bacteria assessment in RNAi screening

This study investigates the use of advanced computer vision techniques for assessing the severity of Orientia tsutsugamushi bacterial infectivity. It uses fluorescent scrub typhus images obtained from molecular screening, and addresses challenges posed by a complex and extensive image dataset, with limited computational resources. Our methodology integrates three key strategies within a deep learning framework: transitioning from instance segmentation (IS) models to an object detection model; reducing the model's backbone size; and employing lower-precision floating-point calculations. These approaches were systematically evaluated to strike an optimal balance between model accuracy and inference speed, crucial for effective bacterial infectivity assessment. A significant outcome is that the implementation of the Faster R-CNN architecture, with a shallow backbone and reduced precision, notably improves accuracy and reduces inference time in cell counting and infectivity assessment. This innovative approach successfully addresses the limitations of image processing techniques and IS models, effectively bridging the gap between sophisticated computational methods and modern molecular biology applications. The findings underscore the potential of this integrated approach to enhance the accuracy and efficiency of bacterial infectivity evaluations in molecular research

Blended Engineering Design Process Learning Activities for Secondary School Students during COVID-19 Epidemic: Students’ Learning Activities and Perception

This study aims to present the teaching and learning activities of Engineering Design Processes (EDP) to secondary school students. The proposed teaching technique used was blended learning, which integrated group activities based on online learning and individual hands-on activities through independent study at home. The context of COVID-19 medical mask protection was used in comparison to the current situation. In order to test the effectiveness of the proposed learning activities, a single-group pretest–posttest design was employed to explore (a) the students’ perceptions of their problem-solving confidence before and after they underwent the proposed learning technique and (b) students’ perceptions of the designed course. After they had finished the 4 weeks of learning activities, the students were asked to complete the Students’ Perception on Problem-Solving Skill Questionnaire (SPPSS) and the Students’ Perception towards the Proposed Blended Engineering Design Process learning activities Questionnaire (SPBEDP) in order to gauge how confident they felt in their ability to solve problems and how they felt about the proposed course. There were 30 seventh-grade students enrolled in this course. An increase in the level of problem-solving confidence was found in the students after they were subjected to the proposed activities. Moreover, the students mentioned that, based on the proposed activities, “Identify Problem and Need”, “Design a Solution”, and “Developing Prototype” are the Engineering Design Process learning steps they enjoyed most since they were the steps in which they could use their creativity, and they were hands-on, fun, easy, challenging, and provided them with an opportunity to choose issues in which they are interested