Design and Implementation of Micro Mechatronic Systems- SMA Drive Polymer Microgripper

A micromechatronic gripper was designed, fabricated, and tested with the proposed control system. By following realization axioms, the microgripper system including a polyurethane (PU) gripper mechanism and shape memory alloy (SMA) actuator was designed and developed. The micromechatronic gripper system was realized with cross-sectional area of (π/4) × 5002 μm2 for clean room operation. A synergetic operation of SMA actuator for driving microgripper mechanism was investigated in visual-based control. By incorporating with inverse Preisach compensator, an explicit self-tuning controller through Ziegler-Nichols criterion was selected for controlling the self-biased SMA actuator. The application of the gripper system for gripping and transporting a glass particle of 30 μm was tested

In situ structures of the genome and genome-delivery apparatus in a single-stranded RNA virus.

Packaging of the genome into a protein capsid and its subsequent delivery into a host cell are two fundamental processes in the life cycle of a virus. Unlike double-stranded DNA viruses, which pump their genome into a preformed capsid, single-stranded RNA (ssRNA) viruses, such as bacteriophage MS2, co-assemble their capsid with the genome; however, the structural basis of this co-assembly is poorly understood. MS2 infects Escherichia coli via the host 'sex pilus' (F-pilus); it was the first fully sequenced organism and is a model system for studies of translational gene regulation, RNA-protein interactions, and RNA virus assembly. Its positive-sense ssRNA genome of 3,569 bases is enclosed in a capsid with one maturation protein monomer and 89 coat protein dimers arranged in a T = 3 icosahedral lattice. The maturation protein is responsible for attaching the virus to an F-pilus and delivering the viral genome into the host during infection, but how the genome is organized and delivered is not known. Here we describe the MS2 structure at 3.6 Å resolution, determined by electron-counting cryo-electron microscopy (cryoEM) and asymmetric reconstruction. We traced approximately 80% of the backbone of the viral genome, built atomic models for 16 RNA stem-loops, and identified three conserved motifs of RNA-coat protein interactions among 15 of these stem-loops with diverse sequences. The stem-loop at the 3' end of the genome interacts extensively with the maturation protein, which, with just a six-helix bundle and a six-stranded β-sheet, forms a genome-delivery apparatus and joins 89 coat protein dimers to form a capsid. This atomic description of genome-capsid interactions in a spherical ssRNA virus provides insight into genome delivery via the host sex pilus and mechanisms underlying ssRNA-capsid co-assembly, and inspires speculation about the links between nucleoprotein complexes and the origins of viruses

Sim-to-Real Segmentation in Robot-assisted Transoral Tracheal Intubation

Robotic-assisted tracheal intubation requires the robot to distinguish anatomical features like an experienced physician using deep-learning techniques. However, real datasets of oropharyngeal organs are limited due to patient privacy issues, making it challenging to train deep-learning models for accurate image segmentation. We hereby consider generating a new data modality through a virtual environment to assist the training process. Specifically, this work introduces a virtual dataset generated by the Simulation Open Framework Architecture (SOFA) framework to overcome the limited availability of actual endoscopic images. We also propose a domain adaptive Sim-to-Real method for oropharyngeal organ image segmentation, which employs an image blending strategy called IoU-Ranking Blend (IRB) and style-transfer techniques to address discrepancies between datasets. Experimental results demonstrate the superior performance of the proposed approach with domain adaptive models, improving segmentation accuracy and training stability. In the practical application, the trained segmentation model holds great promise for robot-assisted intubation surgery and intelligent surgical navigation.Comment: Extended abstract in IEEE ICRA 2023 Workshop (New Evolutions in Surgical Robotics: Embracing Multimodal Imaging Guidance, Intelligence, and Bio-inspired Mechanisms

Phenomenology of a String-Inspired Supersymmetric Model with Inverted Scalar Mass Hierarchy

Supersymmetric (SUSY) models with heavy sfermions ($m_{\tilde{f}} \sim 10$ TeV) in the first two generations and the third generation sfermion masses below 1 TeV can solve the SUSY flavor and the CP problems as well as satisfy naturalness constraints. We study the phenomenology of a string-inspired scenario and compare it with the minimal supergravity unified model (mSUGRA). The SUSY trilepton signature at the upgraded Tevatron, the $b\to s\gamma$ branching fraction and the neutralino dark matter relic density in this model can differ significantly from the mSUGRA model.Comment: Version to appear in Physics Letters

Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs

Video-assisted transoral tracheal intubation (TI) necessitates using an endoscope that helps the physician insert a tracheal tube into the glottis instead of the esophagus. The growing trend of robotic-assisted TI would require a medical robot to distinguish anatomical features like an experienced physician which can be imitated by utilizing supervised deep-learning techniques. However, the real datasets of oropharyngeal organs are often inaccessible due to limited open-source data and patient privacy. In this work, we propose a domain adaptive Sim-to-Real framework called IoU-Ranking Blend-ArtFlow (IRB-AF) for image segmentation of oropharyngeal organs. The framework includes an image blending strategy called IoU-Ranking Blend (IRB) and style-transfer method ArtFlow. Here, IRB alleviates the problem of poor segmentation performance caused by significant datasets domain differences; while ArtFlow is introduced to reduce the discrepancies between datasets further. A virtual oropharynx image dataset generated by the SOFA framework is used as the learning subject for semantic segmentation to deal with the limited availability of actual endoscopic images. We adapted IRB-AF with the state-of-the-art domain adaptive segmentation models. The results demonstrate the superior performance of our approach in further improving the segmentation accuracy and training stability.Comment: The manuscript is accepted by Medical & Biological Engineering & Computing. Code and dataset: https://github.com/gkw0010/EISOST-Sim2Real-Dataset-Releas