Who presents satisfied? Non-modifiable factors associated with patient satisfaction among gynecologic oncology clinic patients

To examine associations between non-modifiable patient factors and patient satisfaction (PS) among women presenting to a gynecologic oncology clinic

The health-related quality of life journey of gynecologic oncology surgical patients: Implications for the incorporation of patient-reported outcomes into surgical quality metrics

To report the changes in patient-reported quality of life for women undergoing gynecologic oncology surgeries

Cyclic Di-GMP-Mediated Repression of Swarming Motility by Pseudomonas aeruginosa PA14 Requires the MotAB Stator

The second messenger cyclic diguanylate (c-di-GMP) plays a critical role in the regulation of motility. In Pseudomonas aeruginosa PA14, c-di-GMP inversely controls biofilm formation and surface swarming motility, with high levels of this dinucleotide signal stimulating biofilm formation and repressing swarming. P. aeruginosa encodes two stator complexes, MotAB and MotCD, that participate in the function of its single polar flagellum. Here we show that the repression of swarming motility requires a functional MotAB stator complex. Mutating the motAB genes restores swarming motility to a strain with artificially elevated levels of c-di-GMP as well as stimulates swarming in the wild-type strain, while overexpression of MotA from a plasmid represses swarming motility. Using point mutations in MotA and the FliG rotor protein of the motor supports the conclusion that MotA-FliG interactions are critical for c-di-GMP-mediated swarming inhibition. Finally, we show that high c-di-GMP levels affect the localization of a green fluorescent protein (GFP)-MotD fusion, indicating a mechanism whereby this second messenger has an impact on MotCD function. We propose that when c-di-GMP level is high, the MotAB stator can displace MotCD from the motor, thereby affecting motor function. Our data suggest a newly identified means of c-di-GMP-mediated control of surface motility, perhaps conserved among Pseudomonas, Xanthomonas, and other organisms that encode two stator systems

Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud

We propose a novel concept to directly match feature descriptors extracted from RGB images, with feature descriptors extracted from 3D point clouds. We use this concept to localize the position and orientation (pose) of the camera of a query image in dense point clouds. We generate a dataset of matching 2D and 3D descriptors, and use it to train a proposed Descriptor-Matcher algorithm. To localize a query image in a point cloud, we extract 2D keypoints and descriptors from the query image. Then the Descriptor-Matcher is used to find the corresponding pairs 2D and 3D keypoints by matching the 2D descriptors with the pre-extracted 3D descriptors of the point cloud. This information is used in a robust pose estimation algorithm to localize the query image in the 3D point cloud. Experiments demonstrate that directly matching 2D and 3D descriptors is not only a viable idea but also achieves competitive accuracy compared to other state-of-the-art approaches for camera pose localization

The impact of surgical complications on health-related quality of life in women undergoing gynecologic and gynecologic oncology procedures: a prospective longitudinal cohort study

There are currently no assessments of the impact of surgical complications on health-related quality of life in gynecology and gynecologic oncology. This is despite complications being a central focus of surgical outcome measurement, and an increasing awareness of the need for patient-reported data when measuring surgical quality

Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Automatic discovery of category-specific 3D keypoints from a collection of objects of a category is a challenging problem. The difficulty is added when objects are represented by 3D point clouds, with variations in shape and semantic parts and unknown coordinate frames. We define keypoints to be category-specific, if they meaningfully represent objects’ shape and their correspondences can be simply established order-wise across all objects. This paper aims at learning such 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category. In order to do so, we model shapes defined by the keypoints, within a category, using the symmetric linear basis shapes without assuming the plane of symmetry to be known. The usage of symmetry prior leads us to learn stable keypoints suitable for higher misalignments. To the best of our knowledge, this is the first work on learning such keypoints directly from 3D point clouds for a general category. Using objects from four benchmark datasets, we demonstrate the quality of our learned keypoints by quantitative and qualitative evaluations. Our experiments also show that the keypoints discovered by our method are geometrically and semantically consistent

Theory of laser ion acceleration from a foil target of nanometers

A theory for laser ion acceleration is presented to evaluate the maximum ion energy in the interaction of ultrahigh contrast (UHC) intense laser with a nanometer-scale foil. In this regime the energy of ions may be directly related to the laser intensity and subsequent electron dynamics. This leads to a simple analytical expression for the ion energy gain under the laser irradiation of thin targets. Significantly, higher energies for thin targets than for thicker targets are predicted. Theory is concretized to the details of recent experiments which may find its way to compare with these results.Comment: 22 pages 7 figures. will be submitted to NJ