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Pen-drawing is an intuitive, convenient, and creative fabrication method for delivering emergent and adaptive design to real devices. To demonstrate the application of pen-drawing to robot construction, we developed pen-drawn Marangoni swimmers that perform complex programmed tasks using a simple and accessible manufacturing process. By simply drawing on substrates using ink-based Marangoni fuel, the swimmers demonstrate advanced robotic motions such as square and star-shaped trajectories, and navigate through maze. The versatility of pen-drawing allows the integration of the swimmers with time-varying substrates, enabling multi-step motion tasks such as cargo delivery and return to the original place. We believe that our pen-based approach will significantly expand the potential applications of miniaturized swimming robots and provide new opportunities for simple robotic implementations.

The tumor microbiome is a unique element that appears differently for each type of cancer in a person. Currently, intratumoral host-microbiome sequencing was only done by bulk tissue analysis which means little is understood when it comes to the spatial distribution of microbiota in the heterogeneous tumor microenvironment and the localized effect of the microbiota. Breast cancer is known to be one of the cancers rich in the microbiome. We focused on cancer stem cell-like microniches related to the tumor microenvironment of triple-negative breast cancer(TNBC). We adapted SLACS(Spatially resolved Laser activated Cell Sorter) which isolates the region of interest from immunofluorescence-stained samples using a laser, providing the spatial distribution of localized microbiota in rare cancer stem cell samples. By applying SLACS to point out the breast cancer stem cell micro-niches, we discovered that more microbes flourished in ALDH1 high/CD44 high groups rather than normal cells. We also identified the composition of the microbiota was different in cancer stem cells depending on the degree of expression of the marker. Comparing RNA-seq data of host and microbiome sequencing with functional studies, we reveal the distribution of microbiota in breast tumors and interactions between cancer stem cells and microbiota. Collectively, we found the spatial pattern of the microbiota localized in cancer stem cell microniches and how cells and bacteria interact with each other more precisely. Citation Format: Gyeongjun Kim. The spatially resolved host-microbiome sequencing platform reveals the interaction between bacteria and cancer stem cell microinches [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1365.

Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference time can be reduced in the field. This model also demands much less human effort to tag images with labels. The number of vehicles in an image becomes the label, rather than bounding boxes drawn around every single vehicle. Nonetheless, the labeling task takes considerable time whenever a CNN model is trained and tested for a new road segment. There are two ways to alleviate the human effort involved in using this method. A previous study used a pseudo label pre-training method, and another study employed an image synthesis method to solve the problem. Besides these two methods, we investigated the model transferability to reduce the labeling effort. Using a CNN that was fully trained on images of a road segment, we devised a robust way to utilize the trained model for another site by transforming the model output with a simple quadratic equation. The utility of the proposed method was confirmed at the expense of a minute amount of deterioration in accuracy.

Cameras for traffic surveillance are usually pole-mounted and produce images that reflect a birds-eye view. Vehicles in such images, in general, assume an ellipse form. A bounding box for the vehicles usually includes a large empty space when the vehicle orientation is not parallel to the edges of the box. To circumvent this problem, the present study applied bounding ellipses to a non-anchor-based, single-shot detection model (CenterNet). Since this model does not depend on anchor boxes, non-max suppression (NMS) that requires computing the intersection over union (IOU) between predicted bounding boxes is unnecessary for inference. The SpotNet that extends the CenterNet model by adding a segmentation head was also tested with bounding ellipses. Two other anchor-based, single-shot detection models (YOLO4 and SSD) were chosen as references for comparison. The model performance was compared based on a local dataset that was doubly annotated with bounding boxes and ellipses. As a result, the performance of the two models with bounding ellipses exceeded that of the reference models with bounding boxes. When the backbone of the ellipse models was pretrained on an open dataset (UA-DETRAC), the performance was further enhanced. The data augmentation schemes developed for YOLO4 also improved the performance of the proposed models. As a result, the best mAP score of a CenterNet with bounding ellipses exceeds 0.9., Comment: 13 pages, 6 figures