51 research outputs found

    ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos

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    Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modeling in natural language processing, has been successfully applied to surgical phase recognition. Existing works based on transformer mainly focus on modeling attention dependency, without introducing auto-regression. In this work, an Auto-Regressive Surgical Transformer, referred as ARST, is first proposed for on-line surgical phase recognition from laparoscopic videos, modeling the inter-phase correlation implicitly by conditional probability distribution. To reduce inference bias and to enhance phase consistency, we further develop a consistency constraint inference strategy based on auto-regression. We conduct comprehensive validations on a well-known public dataset Cholec80. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and achieves an inference rate of 66 frames per second (fps).Comment: 11 Pages, 3 figure

    Cancer Stem Cells and Immunosuppressive Microenvironment in Glioma

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    Glioma is one of the most common malignant tumors of the central nervous system and is characterized by extensive infiltrative growth, neovascularization, and resistance to various combined therapies. In addition to heterogenous populations of tumor cells, the glioma stem cells (GSCs) and other nontumor cells present in the glioma microenvironment serve as critical regulators of tumor progression and recurrence. In this review, we discuss the role of several resident or peripheral factors with distinct tumor-promoting features and their dynamic interactions in the development of glioma. Localized antitumor factors could be silenced or even converted to suppressive phenotypes, due to stemness-related cell reprogramming and immunosuppressive mediators in glioma-derived microenvironment. Furthermore, we summarize the latest knowledge on GSCs and key microenvironment components, and discuss the emerging immunotherapeutic strategies to cure this disease

    Interaction of MSE Abutments with Bridge Superstructures under Seismic Loading \u2013 Shaking Table Tests

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    65A0556This report presents results from shaking table tests on half-scale mechanically-stabilized earth (MSE) bridge abutments. The testing program consists of five tests where the direction of shaking is in the longitudinal direction of the bridge beam, and one test where the direction of shaking is perpendicular to the bridge beam. The longitudinal shaking tests include a baseline configuration and a parametric study of different configurations to investigate the effects of bridge surcharge stress, reinforcement spacing, reinforcement stiffness, and steel reinforcement on the seismic response of MSE bridge abutments. Experimental design of the scale model followed established similitude relationships for shaking table testing in a 1g gravitational field, including scaling for of geometry, reinforcement stiffness, backfill soil modulus, bridge surcharge stress, and characteristics of the earthquake motions. Facing displacements, bridge seat settlements, accelerations, vertical and lateral stresses, reinforcement strains, and contact forces between the bridge beam and bridge seat were measured for different instrumented sections to evaluate the three-dimensional dynamic response during a series of applied shaking motions. Results indicate that reinforcement spacing and reinforcement stiffness have the most significant effects on the facing displacements and bridge seat settlements for dynamic loading conditions

    Modeling the Seismic Compression of Unsaturated Sands

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