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Introduction Hemorrhage is the most common cause of death among Special Operations Force (SOF) soldiers. Bringing remote damage control resuscitation into the far-forward combat environment is logistically challenging, as it requires blood products that generally require a robust cold chain. Alternatively, lyophilized products such as fibrinogen concentrate, which does not require thawing or blood group compatibility testing before use, might be advantageous in damage control resuscitation in the battlefield. In this report, we review the evidence for the use of fibrinogen concentrate in the Canadian SOF environment. Materials and methods The literature on the use of fibrinogen concentrate in the trauma setting was reviewed by Canadian Forces Services Working Group, in three separate meetings. Multiple stakeholders were consulted to obtain authoritative perspectives from subject matter experts on the use of fibrinogen concentrate in the Canadian SOF environment. We also conducted a comparison review of fibrinogen content, pathogen risk, shelf life, and methods required for use for fresh frozen plasma, cryoprecipitate, and fibrinogen concentrate relevant to their application in the far-forward combat environment. Results Indications and a protocol for the use of fibrinogen as an adjunct to fresh whole blood were formulated based on a literature review and clinical expert opinion. Alternative strategies and other lyophilized blood products were considered before selecting fibrinogen concentrate as the lyophilized blood product of choice. Fibrinogen concentrate is an ABO-universal blood product with an excellent safety profile. Training was conducted by subject matter experts within civilian trauma centers and at military training facilities. The clinical efficacy and safety were confirmed by monitoring the use of fibrinogen concentrate in deployed combat settings. Conclusion Fibrinogen concentrate is a useful adjunct to remote damage control resuscitation in the SOF environment. Fibrinogen concentrate was found to be robust for transport into the SOF environment and is widely accepted among SOF operators and medics.

We propose and validate two end-to-end deep learning architectures to learn foot pressure distribution maps (dynamics) from 2D or 3D human pose (kinematics). The networks are trained using 1.36 million synchronized pose+pressure data pairs from 10 subjects performing multiple takes of a 5-min long choreographed Taiji sequence. Using leave-one-subject-out cross validation, we demonstrate reliable and repeatable foot pressure prediction, setting the first baseline for solving a non-obvious pose to pressure cross-modality mapping problem in computer vision. Furthermore, we compute and quantitatively validate Center of Pressure (CoP) and Base of Support (BoS), two key components for stability analysis, from the predicted foot pressure distributions.

Fresh whole blood (FWB) transfusion is an option for providing volume and oxygen carrying capacity to bleeding Special Operations soldiers who are injured in an austere environment and who are far from a regular blood bank. Retrospective data from recent conflicts in Iraq and Afghanistan show an association between the use of FWB and survival. We reviewed the literature to document the issues surrounding FWB transfusion to Special Operations soldiers in the austere environment and surveyed the literature regarding best practice guidelines for and patient outcomes after FWB transfusions. Most literature regarding FWB transfusion is retrospective or historical. There is limited prospective evidence currently to change transfusion practice in tertiary care facilities, but FWB remains an option in the austere setting.

Motivated by various new applications of computational symmetry in computer vision and in an effort to advance machine perception of symmetry in the wild, we organize the third international symmetry detection challenge at ICCV 2017, after the CVPR 2011/2013 symmetry detection competitions. Our goal is to gauge the progress in computational symmetry with continuous benchmarking of both new algorithms and datasets, as well as more polished validation methodology. Different from previous years, this time we expand our training/testing data sets to include 3D data, and establish the most comprehensive and largest annotated datasets for symmetry detection to date; we also expand the types of symmetries to include densely-distributed and medial-axis-like symmetries; furthermore, we establish a challenge-and-paper dual track mechanism where both algorithms and articles on symmetry-related research are solicited. In this report, we provide a detailed summary of our evaluation methodology for each type of symmetry detection algorithm validated. We demonstrate and analyze quantified detection results in terms of precision-recall curves and F-measures for all algorithms evaluated. We also offer a short survey of the paper-track submissions accepted for our 2017 symmetry challenge.

We develop a computational tool that aligns motion capture (mocap) data to videos of 24-form simplified Taiji (TaiChi) Quan, a scripted motion sequence about 5 minutes long. With only prior knowledge that the subjects in video and mocap perform a similar pose sequence, we establish inter-subject temporal synchronization and spatial alignment of mocap and video based on body joint correspondences. Through time alignment and matching the viewpoint and orientation of the video camera, the 3D body joints from mocap data of subject A can be correctly projected onto the video performance of subject B. Initial quantitative evaluation of this alignment method shows promise in offering the first validated algorithmic treatment for cross-subject comparison of Taiji Quan performances. This work opens the door to subject-specific quantified comparison of long motion sequences beyond Taiji.

Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first deep-learning neural network for reflection and rotation symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common Object in COntext) dataset with nearly 11K consistent symmetry-labels from more than 400 human observers. We employ novel methods to convert discrete human labels into symmetry heatmaps, capture symmetry densely in an image and quantitatively evaluate Sym-NET against multiple existing computer vision algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO photos, Sym-NET significantly outperforms all other competitors. Beyond mathematically well-defined symmetries on a plane, Sym-NET demonstrates abilities to identify viewpoint-varied 3D symmetries, partially occluded symmetrical objects, and symmetries at a semantic level., To appear in the International Conference on Computer Vision (ICCV) 2017

Proper treatment for the compromised diabetic foot often requires surgical correction and subtotal pedal amputation. This article discusses various levels of amputation of the human foot, including digital, ray, transmetatarsal, midfoot, and Syme amputations. Surgical techniques and biomechanical considerations are presented in order to assist the surgeon in planning for the most functional outcome of the patient. A review of the literature and the experiences of the authors are presented. (J Am Podiatr Med Assoc 91(1): 6-12, 2001)

We describe a cognitive vision system for a mobile robot. This system works in a manner similar to the human vision system, using saccadic, vergence and pursuit movements to extract information from visual input. At each fixation, the system builds a 3D model of a small region, combining information about distance, shape, texture and motion. These 3D models are embedded within an overall 3D model of the robot's environment. This approach turns the computer vision problem into a search problem, with the goal of constructing a physically realistic model of the entire environment. At each step, the vision system selects a point in the visual input to focus on. The distance, shape, texture and motion information are computed in a small region and used to build a mesh in a 3D virtual world. Background knowledge is used to extend this structure as appropriate, e.g. if a patch of wall is seen, it is hypothesized to be part of a large wall and the entire wall is created in the virtual world, or if part of an object is recognized, the whole object's mesh is retrieved from the library of objects and placed into the virtual world. The difference between the input from the real camera and from the virtual camera is compared using local Gaussians, creating an error mask that indicates the main differences between them. This is then used to select the next points to focus on. This approach permits us to use very expensive algorithms on small localities, thus generating very accurate models. It also is task-oriented, permitting the robot to use its knowledge about its task and goals to decide which parts of the environment need to be examined. The software components of this architecture include PhysX for the 3D virtual world, OpenCV and the Point Cloud Library for visual processing, and the Soar cognitive architecture, which controls the perceptual processing and robot planning. The hardware is a custom-built pan-tilt stereo color camera. We describe experiments using both static and moving objects.

A case report about extensive osseous metaplasia of a plantar fibromatosis is presented. The authors are unaware of previous reports in the literature describing osseous metaplasia within plantar fibromatosis in this manner. This case shows that plantar fibromatosis may undergo osseous metaplasia in a fashion similar to fibromatous lesions elsewhere.

We are building a robot cognitive architecture that constructs a real-time virtual copy of itself and its environment, including people, and uses the model to process perceptual information and to plan its movements. This paper describes the structure of this architecture. The software components of this architecture include PhysX for the virtual world, OpenCV and the Point Cloud Library for visual processing, and the Soar cognitive architecture that controls the perceptual processing and task planning. The RS (Robot Schemas) language is implemented in Soar, providing the ability to reason about concurrency and time. This Soar/RS component controls visual processing, deciding which objects and dynamics to render into PhysX, and the degree of detail required for the task. As the robot runs, its virtual model diverges from physical reality, and errors grow. The Match-Mediated Difference component monitors these errors by comparing the visual data with corresponding data from virtual cameras, and notifies Soar/RS of significant differences, e.g. a new object that appears, or an object that changes direction unexpectedly. Soar/RS can then run PhysX much faster than real-time and search among possible future world paths to plan the robot's actions. We report experimental results in indoor environments.