BackgroundNursing homes were ill-equipped for the pandemic; though facilities are required to have infection control staff, only 3% have taken a basic infection control course. Little is known about the implementation of effective practices outside of the acute care setting. We proposed an intervention utilizing Project ECHO, to connect Penn State University experts with nursing home staff and administrators to explore how infection control guidelines can be implemented effectively. MethodsA stratified cluster randomized design was used to assign nursing homes to either AHRQ-funded COVID-19 ECHO or AHRQ-funded COVID-19 ECHO+. Results136 nursing homes participated. There were no significant differences in COVID-19 infection rate, hospitalization, deaths, or influenza, between ECHO or ECHO+. DiscussionThe ECHO model has significant strengths when compared to traditional training, as it allows for remote learning delivered by a multidisciplinary team of experts and utilizes case discussions that match the context of nursing homes.

To contribute meaningfully to the fight against disease outbreaks, the media should not just funnel predetermined health news to the audience. It should also play the vital role of convincing citizens to protect themselves and to shun rumors, misinformation and conspiracy theories that disfigure reality about infectious diseases. The present study examines alleged Covid-19 information concealment in Nigeria, which is thought to account for citizen uncooperativeness in the fight against the disease. It highlights the distinctions between data privacy and information secrecy. Using Spearman’s rank correlation, Chi-square and linear regression, we analyzed data from 183 statistically selected respondents from two states and the Federal Capital Territory in Nigeria. Data were also generated from qualitative survey of 12 health editors, two public affairs analysts and two government officials. Results indicate negative relationships between compliance with Covid-19 containment measures, citizen opinions on Covid-19 information protection and opinions on Covid-19 controversies. Data from qualitative survey suggest that government’s protection of information forces editors to report stories without “human face.” The study highlights the need for distinctions between physical privacy, information privacy and related concepts in the enforcement of health information privacy. This is to avoid sacrificing the public’s right to know in the guise of health information protection.

Veterinary RecordVolume 192, Issue 5 p. 215-216 Antimicrobials Updated version of sheep AMU calculator Fiona Lovatt, Corresponding Author Fiona Lovatt chair fiona@flockhealth.co.uk Sheep Antibiotic Guardian GroupSearch for more papers by this authorDavid Wilson, David Wilson president Sheep Veterinary Society, c/o Moredun Research Institute, Pentland Science Park, Bush Loan, Penicuik, Midlothian, EH26 0PZSearch for more papers by this author Fiona Lovatt, Corresponding Author Fiona Lovatt chair fiona@flockhealth.co.uk Sheep Antibiotic Guardian GroupSearch for more papers by this authorDavid Wilson, David Wilson president Sheep Veterinary Society, c/o Moredun Research Institute, Pentland Science Park, Bush Loan, Penicuik, Midlothian, EH26 0PZSearch for more papers by this author First published: 03 March 2023 https://doi.org/10.1002/vetr.2812Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume192, Issue54/11 March 2023Pages 215-216 RelatedInformation

The global vehicle fleet is expanding rapidly. Vehicles pollute the world's cities, emitting rising volumes of carbon dioxide and other climate-altering greenhouse gases, because of massive amounts of petroleum usage. The frightening fact is that automotive application is expanding significantly faster than the human population, with no end in sight. If the current trend continues, nearly 3 billion automobiles might be in use by 2050, outnumbering 20 cars per 100 persons. The usage of electric vehicles is necessary to combat these consequences, and today's automakers are converting their automobiles to E- Vehicles. Additionally, it is quite affordable and user-friendly. Converting bicycles to electric cycles is one alternative method.

Multiple human tracking is a fundamental problem for scene understanding. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning have focused on accuracy and require substantial running time. This study aims to improve running speed by performing human detection at a certain frame interval because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that complements the detection results with optical flow, based on the fact that someone's appearance does not change much between adjacent frames. To maintain the tracking accuracy, we introduce robust interest point selection within human regions and a tracking termination metric calculated by the distribution of the interest points. On the MOT20 dataset in the MOTChallenge, the proposed SDOF-Tracker achieved the best performance in terms of the total running speed while maintaining the MOTA metric. Our code is available at https://github.com/hitottiez/sdof-tracker.