Brief of Natural Resources in Opposition to Plaintiff\u27s Opening Briefs, Appendix A

Findings of Fact before the Indian Claims Commissio

Brief of Natural Resources in Opposition to Plaintiff\u27s Opening Briefs, Appendix A

Findings of Fact before the Indian Claims Commissio

Recommendations for chemical weed control in grain sorghum

12/77/15MHarold Kerr, Joseph H. Scott, O. Hale Fletchall and L. E. Anderson (Department of Agronomy, College of Agriculture

Which are the Parameters to be Controlled in Red Cell Products (Whole Blood, Red Cell Concentrates, Washed Red Cells, Leucocyte Poor Red Cell Concentrates, Frozen Red Cells) in Order that They May be Offered to the Medical Profession as Standardised Products with Specified Properties?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73259/1/j.1423-0410.1980.tb01863.x.pd

Chemical weed control in field corn for 1982 -- part 1 : preplanting incorporated treatments

Harold D. Kerr, Joseph H. Scott, E. J. Peters, L. E. Anderson, O. Hale Fletchall, David Guethle, Zane R. Helsel and Howard Guscar (Department of Agronomy, College of Agriculture)New 1/82/15

Chemical weed control in field corn for 1982, Part 2. Pre-emergence and postemergence

Harold D. Kerr, Joseph H. Scott, E. J. Peters, L. E. Anderson, O. Hale Fletchall, David Guethle, Zane R. Helsel and Howard Guscar (Department of Agronomy, College of Agriculture)Revised 1/82/15

Bostonia: The Boston University Alumni Magazine. Volume 32

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Radiological Society of North America expert consensus document on reporting chest CT findings related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA

Routine screening CT for the identification of coronavirus disease 19 (COVID-19) pneumonia is currently not recommended by most radiology societies. However, the number of CT examinations performed in persons under investigation for COVID-19 has increased. We also anticipate that some patients will have incidentally detected findings that could be attributable to COVID-19 pneumonia, requiring radiologists to decide whether or not to mention COVID-19 specifically as a differential diagnostic possibility. We aim to provide guidance to radiologists in reporting CT findings potentially attributable to COVID-19 pneumonia, including standardized language to reduce reporting variability when addressing the possibility of COVID-19. When typical or indeterminate features of COVID-19 pneumonia are present in endemic areas as an incidental finding, we recommend contacting the referring providers to discuss the likelihood of viral infection. These incidental findings do not necessarily need to be reported as COVID-19 pneumonia. In this setting, using the term viral pneumonia can be a reasonable and inclusive alternative. However, if one opts to use the term COVID-19 in the incidental setting, consider the provided standardized reporting language. In addition, practice patterns may vary, and this document is meant to serve as a guide. Consultation with clinical colleagues at each institution is suggested to establish a consensus reporting approach. The goal of this expert consensus is to help radiologists recognize findings of COVID-19 pneumonia and aid their communication with other health care providers, assisting management of patients during this pandemic. Published under a CC BY 4.0 license

Fast and Accurate Border Detection in Dermoscopy Images Using Statistical Region Merging

Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist-determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.http://dx.doi.org/10.1117/12.70907