Density functional study of selected mono-zinc and gem-dizinc radical carbenoid cyclopropanation reactions: observation of an efficient radical zinc carbenoid cyclopropanation reaction and the influence of the leaving group on ring closure

We report a theoretical study of the cyclopropanation reactions of EtZnCHI, (EtZn)2CH EtZnCHZnI, and EtZnCIZnI radicals with ethylene. The mono-zinc and gem-dizinc radical carbenoids can undergo cyclopropanation reactions with ethylene via a two-step reaction mechanism similar to that previously reported for the CH2I and IZnCH2 radicals. The barrier for the second reaction step (ring closure) was found to be highly dependent on the leaving group of the cyclopropanation reaction. In some cases, the (di)zinc carbenoid radical undergoes cyclopropanation via a low barrier of about 5–7 kcal/mol on the second reaction step and this is lower than the CH2I radical reaction which has a barrier of about 13.5 kcal/mol for the second reaction step. Our results suggest that in some cases, zinc radical carbenoid species have cyclopropanation reaction barriers that can be competitive with their related molecular Simmons-Smith carbenoid species reactions and produce somewhat different cyclopropanated products and leaving groups

Alternative parameter choices for multi-step quasi-Newton methods

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PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition?

International audienceBackground and objective: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the “PEg TRAnsfer Workflow recognition” (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. Methods: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. Results: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). Conclusion: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition. © 202