A Pedagogy of Walking With Our Sisters

This article examines the pedagogical and ethical implications of a white settler’s encounter with the Walking With Our Sisters commemorative art installation honouring missing and murdered Indigenous women. I argue that the installation offers a pedagogical intervention in official state memory and conventional approaches to teaching difficult knowledge. I offer an analysis of the centrality of embodiment, vulnerability, the visual, and affective force in the memorial in order make legible the pedagogy of affect and non-mastery at work in the exhibit and the ethical possibilities of such an approach to social justice education. I respond to the task of accountability and responsibility I felt summoned to address as a learner-participant in remembering the ongoing racial and gendered violence of white settler colonialism

Case 14 : Rural Residence and Associated Health Disparities: The Case of Chatham-Kent

Rural populations face notably higher rates of chronic diseases than urban areas, specifically, cardiovascular diseases, chronic respiratory diseases, cancer, and diabetes. This case focuses on Chatham-Kent, a small, rural town in Southwestern Ontario, to illustrate this point. More specifically, the case focuses on an epidemiological approach to provide evidence of the health disparities due to an individual’s place of residence. Based on the comparison of agestandardized rates, does the rural community of Chatham-Kent experience greater health disparities? This case provides the reader with practice in calculating and interpreting crude and agestandardized rates and the ability to disseminate findings about the health status of a given population

Creating a Zero Waste Culture: Responsible Reuse and Recycling

Course Code: ENR/AEDE 4567The project examines OSU's goal of zero waste by 2025 through minimizing waste, specifically involved with student life on the Columbus campus. Contamination of the recycling stream was also a vital concern for the university and is addressed in the report provided.Academic Major: Environment, Economy, Development, and Sustainabilit

Spatiotemporal dynamics of multiple shear-banding events for viscoelastic micellar fluids in cone-plate shearing flows

We characterize the transient response of semi-dilute wormlike micellar solutions under an imposed steady shear flow in a cone-plate geometry. By combining conventional rheometry with 2-D Particle Image Velocimetry (PIV), we can simultaneously correlate the temporal stress response with time-resolved velocimetric measurements. By imposing a well defined shear history protocol, consisting of a stepped shear flow sweep, we explore both the linear and nonlinear responses of two surfactant solutions: cetylpiridinium chloride (CPyCl) and sodium salicylate (NaSal) mixtures at concentrations of [66:40] mM and [100:60] mM, respectively. The transient stress signal of the more dilute solution relaxes to its equilibrium value very fast and the corresponding velocity profiles remain linear, even in the strongly shear-thinning regime. The more concentrated solution also exhibits linear velocity profiles at small shear rates. At large enough shear rates, typically larger than the inverse of the relaxation time of the fluid, the flow field reorganizes giving rise to strongly shear-banded velocity profiles. These are composed of an odd number of shear bands with low-shear-rate bands adjacent to both gap boundaries. In the non-linear regime long transients (much longer than the relaxation time of the fluid) are observed in the transient stress response before the fluid reaches a final, fully-developed state. The temporal evolution in the shear stress can be correlated with the spatiotemporal dynamics of the multiple shear-banded structure measured using RheoPIV. In particular our experiments show the onset of elastic instabilities in the flow which are characterized by the presence of multiple shear bands that evolve and rearrange in time resulting in a slow increase in the average torque acting on the rotating fixture

It’s How Many Terabytes?! A Case Study on Managing Large Born Digital Audio-visual Acquisitions

In October 2014, the University of California Irvine (UCI) Special Collections and Archives acquired a born digital collection of 2.5 terabytes – the largest born digital collection acquired by the department to date. This case study describes the challenges we encountered when applying existing archival procedures to appraise, store, and provide access to a large born digital collection. It discusses solutions when they could be found and ideas for solutions when they could not, lessons learned from the experience, and the impact on born-digital policy and procedure at UCI Libraries. Working with a team of archivists, librarians, IT, and California Digital Library (CDL) staff, we discovered issues and determined solutions that will guide our procedures for future acquisitions of large and unwieldy born digital collections.

Allergens induce enhanced bronchoconstriction and leukotriene production in C5 deficient mice

Abstract Background Previous genetic analysis has shown that a deletion in the complement component 5 gene-coding region renders mice more susceptible to allergen-induced airway hyperresponsiveness (AHR) due to reduced IL-12 production. We investigated the role of complement in a murine model of asthma-like pulmonary inflammation. Methods In order to evaluate the role of complement B10 mice either sufficient or deficient in C5 were studied. Both groups of mice immunized and challenged with a house dust extract (HDE) containing high levels of cockroach allergens. Airways hyper-reactivity was determined with whole-body plesthysmography. Bronchoalveolar lavage (BAL) was performed to determine pulmonary cellular recruitment and measure inflammatory mediators. Lung homogenates were assayed for mediators and plasma levels of IgE determined. Pulmonary histology was also evaluated. Results C5-deficient mice showed enhanced AHR to methylcholine challenge, 474% and 91% increase above baseline Penh in C5-deficient and C5-sufficient mice respectively, p < 0.001. IL-12 levels in the lung homogenate (LH) were only slightly reduced and BAL IL-12 was comparable in C5-sufficient and C5-deficient mice. However, C5-deficient mice had significantly higher cysteinyl-leukotriene levels in the BAL fluid, 1913 +/- 246 pg/ml in C5d and 756 +/- 232 pg/ml in C5-sufficient, p = 0.003. Conclusion These data demonstrate that C5-deficient mice show enhanced AHR due to increased production of cysteinyl-leukotrienes.http://deepblue.lib.umich.edu/bitstream/2027.42/116863/1/12931_2006_Article_520.pd

QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation -- analysis of ranking metrics and benchmarking results

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing metrics to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This metric (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QUBraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTSResearch reported in this publication was partly supported by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), under award numbers NIH/NCI/ITCR:U01CA242871 and NIH/NCI/ITCR:U24CA189523. It was also partly supported by the National Institute of Neurological Disorders and Stroke (NINDS) of the NIH, under award number NIH/NINDS:R01NS042645.Document signat per 92 autors/autores: Raghav Mehta1 , Angelos Filos2 , Ujjwal Baid3,4,5 , Chiharu Sako3,4 , Richard McKinley6 , Michael Rebsamen6 , Katrin D¨atwyler6,53, Raphael Meier54, Piotr Radojewski6 , Gowtham Krishnan Murugesan7 , Sahil Nalawade7 , Chandan Ganesh7 , Ben Wagner7 , Fang F. Yu7 , Baowei Fei8 , Ananth J. Madhuranthakam7,9 , Joseph A. Maldjian7,9 , Laura Daza10, Catalina Gómez10, Pablo Arbeláez10, Chengliang Dai11, Shuo Wang11, Hadrien Raynaud11, Yuanhan Mo11, Elsa Angelini12, Yike Guo11, Wenjia Bai11,13, Subhashis Banerjee14,15,16, Linmin Pei17, Murat AK17, Sarahi Rosas-González18, Illyess Zemmoura18,52, Clovis Tauber18 , Minh H. Vu19, Tufve Nyholm19, Tommy L¨ofstedt20, Laura Mora Ballestar21, Veronica Vilaplana21, Hugh McHugh22,23, Gonzalo Maso Talou24, Alan Wang22,24, Jay Patel25,26, Ken Chang25,26, Katharina Hoebel25,26, Mishka Gidwani25, Nishanth Arun25, Sharut Gupta25 , Mehak Aggarwal25, Praveer Singh25, Elizabeth R. Gerstner25, Jayashree Kalpathy-Cramer25 , Nicolas Boutry27, Alexis Huard27, Lasitha Vidyaratne28, Md Monibor Rahman28, Khan M. Iftekharuddin28, Joseph Chazalon29, Elodie Puybareau29, Guillaume Tochon29, Jun Ma30 , Mariano Cabezas31, Xavier Llado31, Arnau Oliver31, Liliana Valencia31, Sergi Valverde31 , Mehdi Amian32, Mohammadreza Soltaninejad33, Andriy Myronenko34, Ali Hatamizadeh34 , Xue Feng35, Quan Dou35, Nicholas Tustison36, Craig Meyer35,36, Nisarg A. Shah37, Sanjay Talbar38, Marc-Andr Weber39, Abhishek Mahajan48, Andras Jakab47, Roland Wiest6,46 Hassan M. Fathallah-Shaykh45, Arash Nazeri40, Mikhail Milchenko140,44, Daniel Marcus40,44 , Aikaterini Kotrotsou43, Rivka Colen43, John Freymann41,42, Justin Kirby41,42, Christos Davatzikos3,4 , Bjoern Menze49,50, Spyridon Bakas∗3,4,5 , Yarin Gal∗2 , Tal Arbel∗1,51 // 1Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada, 2Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England, 3Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA, 4Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA, 5Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 6Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland, 7Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA, 8Department of Bioengineering, University of Texas at Dallas, Texas, USA, 9Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, 10Universidad de los Andes, Bogotá, Colombia, 11Data Science Institute, Imperial College London, London, UK, 12NIHR Imperial BRC, ITMAT Data Science Group, Imperial College London, London, UK, 13Department of Brain Sciences, Imperial College London, London, UK, 14Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India, 15Department of CSE, University of Calcutta, Kolkata, India, 16 Division of Visual Information and Interaction (Vi2), Department of Information Technology, Uppsala University, Uppsala, Sweden, 17Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA, 18UMR U1253 iBrain, Université de Tours, Inserm, Tours, France, 19Department of Radiation Sciences, Ume˚a University, Ume˚a, Sweden, 20Department of Computing Science, Ume˚a University, Ume˚a, Sweden, 21Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain, 22Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand, 23Radiology Department, Auckland City Hospital, Auckland, New Zealand, 24Auckland Bioengineering Institute, University of Auckland, New Zealand, 25Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA, 26Massachusetts Institute of Technology, Cambridge, MA, USA, 27EPITA Research and Development Laboratory (LRDE), France, 28Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA, 29EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicˆetre, France, 30School of Science, Nanjing University of Science and Technology, 31Research Institute of Computer Vision and Robotics, University of Girona, Spain, 32Department of Electrical and Computer Engineering, University of Tehran, Iran, 33School of Computer Science, University of Nottingham, UK, 34NVIDIA, Santa Clara, CA, US, 35Biomedical Engineering, University of Virginia, Charlottesville, USA, 36Radiology and Medical Imaging, University of Virginia, Charlottesville, USA, 37Department of Electrical Engineering, Indian Institute of Technology - Jodhpur, Jodhpur, India, 38SGGS ©2021 Mehta et al.. License: CC-BY 4.0. arXiv:2112.10074v1 [eess.IV] 19 Dec 2021 Mehta et al. Institute of Engineering and Technology, Nanded, India, 39Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center, 40Department of Radiology, Washington University, St. Louis, MO, USA, 41Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA, 42Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, 43Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 44Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA, 45Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, USA, 46Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland, 47Center for MR-Research, University Children’s Hospital Zurich, Zurich, Switzerland, 48Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India, 49Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, 50Department of Informatics, Technical University of Munich, Munich, Germany, 51MILA - Quebec Artificial Intelligence Institute, Montreal, QC, Canada, 52Neurosurgery department, CHRU de Tours, Tours, France, 53 Human Performance Lab, Schulthess Clinic, Zurich, Switzerland, 54 armasuisse S+T, Thun, Switzerland.Preprin

Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain

The nature of contacts between hosts can be important in facilitating or impeding the spread of pathogens within a population. Networks constructed from contacts between hosts allow examination of how individual variation might influence the spread of infections. Studying the contact networks of livestock species managed under different conditions can additionally provide insight into their influence on these contact structures. We collected high-resolution proximity and GPS location data from nine groups of domestic cattle (mean group size = 85) in seven dairy herds employing a range of grazing and housing regimes. Networks were constructed from cattle contacts (defined by proximity) aggregated by different temporal windows (2 h, 24 h, and approximately 1 week) and by location within the farm. Networks of contacts aggregated over the whole study were highly saturated but dividing contacts by space and time revealed substantial variation in cattle interactions. Cows showed statistically significant variation in the frequency of their contacts and in the number of cows with which they were in contact. When cows were in buildings, compared to being on pasture, contact durations were longer and cows contacted more other cows. A small number of cows showed evidence of consistent relationships but the majority of cattle did not. In one group where management allowed free access to all farm areas, cows showed asynchronous space use and, while at pasture, contacted fewer other cows and showed substantially greater between-individual variation in contacts than other groups. We highlight the degree to which variations in management (e.g. grazing access, milking routine) substantially alter cattle contact patterns, with potentially major implications for infection transmission and social interactions. In particular, where individual cows have free choice of their environment, the resulting contact networks may have a less-risky structure that could reduce the likelihood of direct transmission of infections