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In this work we propose a new Prediction from Expert Demonstration (PED) methodology to provide haptic assistance in robot assisted trocar surgery. Data was collected from expert (clinician) demonstrations for the procedure of trocar insertion. We encode a set of force, torque and penetration trajectories by using a Gaussian Mixture Model (GMM). A generalization of these profiles and associated parameters are retrieved by Gaussian Mixture Regression (GMR). A haptic assistance mode was devised to help novices perform the procedure based on the proposed PED model. We validated the methodology for surgical assistance on (n=15) participants. The PED haptic model was tested for instrument deviation, penetration force and penetration depth. Preliminary study results showed that participants with PED haptic assistance performed the task with more consistency and exerted lesser penetration force than subjects without assistance.

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Omicron strain is the latest variant of concern of SarsCov2 virus. The mutations in this strain in the S protein Receptor Binding domain (RBD) enable it to be more transmissible as well as escape neutralizing activity by antibodies in response to vaccine. Thus, Omicron specific strategies are need to counter infection by this strain.We investigated a collection of approved drugs shown to antagonize the binding of native strain RBD to human ACE2, for their ability to antagonize binding to Omicron strain RBD.While most of the drugs the drugs that antagonize binding to native RBD are also active for Omicron RBD but some were inactive, namely drugs that contain iodine are completely inactive against Omicron RBD. Our data strongly indicate that presence of a single iodine molecule in the drug renders it inactive against Omicron strain. Thus, there is molecular specificity of drugs for antagonizing Omicron strain RBD versus native strain RBD of this virus. Such information will pave way for specific drugs for Omicron. A pragmatic message from our data is that the often-used iodine containing mouth wash and rises may be ineffective in antagonizing receptor binding of Omicron strain.

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The basic step for video analysis is the detection of shots in a given video. A shot is sequence of frames captured in a single continuous action in time and space using a single camera. The boundary between two adjacent shots may be an abrupt change (hard cut) or gradual change. In literature, many shot boundary detection algorithms have been proposed for detecting the hard cut or gradual changes like fadein/out and dissolve. The performance of these algorithms degrades with zooming, lighting change conditions, and fast moving type of videos. In this paper, a novel algorithm based on Gaussian Mixture Model (GMM) is developed for shot boundary detection. The behavior of GMM with abrupt and gradual change is used for detection of hard cut, fadein/out and dissolve. Experimental results shows credibility of the proposed algorithm with zooming, lighting change conditions, and fast moving type of videos.