Erratum: 541 A Framework for Multicultural and Multidisciplinary Near-Peer Mentoring for Artificial Intelligence in Healthcare Education: A University of Florida Friend Group - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.462.].

Parasitic egg recognition using convolution and attention network.

Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

Erratum: 89 Bridging Cell Biology and Engineering Sciences: Interdisciplinary Team-based Training in Computational Pathology - ERRATUM.

[This corrects the article DOI: 10.1017/cts.2023.172.].

Localization and classification of space objects using EfficientDet detector for space situational awareness.

Space situational awareness (SSA) systems play a significant role in space navigation missions. One of the most essential tasks of this system is to recognize space objects such as spacecrafts and debris for various purposes including active debris removal, on-orbit servicing, and satellite formation. The complexity of object recognition in space is due to several sensing conditions, including the variety of object sizes with high contrast, low signal-to-noise ratio, noisy backgrounds, and several orbital scenarios. Existing methods have targeted the classification of images containing space objects with complex backgrounds using various convolutional neural networks. These methods sometimes lose attention on the objects in these images, which leads to misclassification and low accuracy. This paper proposes a decision fusion method that involves training an EfficientDet model with an EfficientNet-v2 backbone to detect space objects. Furthermore, the detected objects were augmented by blurring and by adding noise, and were then passed into the EfficientNet-B4 model for training. The decisions from both models were fused to find the final category among 11 categories. The experiments were conducted by utilizing a recently developed space object dataset (SPARK) generated from realistic space simulation environments. The dataset consists of 11 categories of objects with 150,000 RGB images and 150,000 depth images. The proposed object detection solution yielded superior performance and its feasibility for use in real-world SSA systems was demonstrated. Results show significant improvement in accuracy (94%), and performance metric (1.9223%) for object classification and in mean precision (78.45%) and mean recall (92.00%) for object detection.

Bimodal Targeting Using Sulfonated, Mannosylated PEI for Combined Gene Delivery and Photodynamic Therapy.

Photodynamic therapy (PDT) and gene delivery have both been used to target both cancer cells and tumor-associated macrophages (TAMs). Given the complex nature of tumor tissue, there could be merit in combining these strategies simultaneously. In this study, we developed a bimodal targeting approach to both cancer cells and macrophages, employing materials conducive to both gene delivery and PDT. Polymers libraries were created that consisted of cationic polyethyleneimine (PEI) conjugated to the photosensitizer pyropheophorbide-a, with sulfonation (to target selectin-expressing cells) and mannosylation (to target TAMs). Polyplexes, consisting of these polymers electrostatically bound to DNA, were analyzed for transfection efficacy and cytotoxicity toward epithelial cells and macrophages to assess dual-targeting. This study provides preliminary proof of principle for using modified PEI for targeted gene delivery and PDT.