Temporal trends (1999–2010) of perfluoroalkyl acids in commonly consumed food items

2014 ◽  
Vol 188 ◽  
pp. 102-108 ◽  
Author(s):  
Jana H. Johansson ◽  
Urs Berger ◽  
Robin Vestergren ◽  
Ian T. Cousins ◽  
Anders Bignert ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Perfluoroalkyl Acids ◽  
Food Items

Temporal trends of perfluoroalkyl acids in plasma samples of pregnant women in Hokkaido, Japan, 2003–2011

2013 ◽  
Vol 60 ◽  
pp. 89-96 ◽  
Author(s):  
Emiko Okada ◽  
Ikuko Kashino ◽  
Hideyuki Matsuura ◽  
Seiko Sasaki ◽  
Chihiro Miyashita ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Temporal Trends ◽  
Perfluoroalkyl Acids ◽  
Plasma Samples

Temporal Trends and Geographical Differences of Perfluoroalkyl Acids in Baltic Sea Herring and White-Tailed Sea Eagle Eggs in Sweden

2016 ◽  
Vol 50 (23) ◽  
pp. 13070-13079 ◽  
Author(s):  
Suzanne Faxneld ◽  
Urs Berger ◽  
Björn Helander ◽  
Sara Danielsson ◽  
Aroha Miller ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Temporal Trends ◽  
Perfluoroalkyl Acids ◽  
Geographical Differences

Diverging temporal trends of perfluoroalkyl acids (PFAAs) in primiparous women from Uppsala 1997-2014

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Anders Glynn* ◽  
Jonathan Benskin ◽  
Irina Gyllenhammar ◽  
Oskar Sandblom ◽  
Per Ola Darnerud ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Perfluoroalkyl Acids ◽  
Primiparous Women

scholarly journals Occurrence, temporal trends, and half-lives of perfluoroalkyl acids (PFAAs) in occupational workers in China

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jianjie Fu ◽  
Yan Gao ◽  
Lin Cui ◽  
Thanh Wang ◽  
Yong Liang ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Perfluoroalkyl Acids

Twitter Usage Using Common Reference to Tinnitus

2020 ◽  
Vol 29 (2) ◽  
pp. 206-217
Author(s):  
Jianyuan Ni ◽  
Monica L. Bellon-Harn ◽  
Jiang Zhang ◽  
Yueqing Li ◽  
Vinaya Manchaiah
Keyword(s):  
Nonprofit Organizations ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Cross Sectional ◽  
For Profit ◽  
Common Reference ◽  
Personal Experiences ◽  
Twitter Users ◽  
Sectional Analysis ◽  
Share Information

Objective The objective of the study was to examine specific patterns of Twitter usage using common reference to tinnitus. Method The study used cross-sectional analysis of data generated from Twitter data. Twitter content, language, reach, users, accounts, temporal trends, and social networks were examined. Results Around 70,000 tweets were identified and analyzed from May to October 2018. Of the 100 most active Twitter accounts, organizations owned 52%, individuals owned 44%, and 4% of the accounts were unknown. Commercial/for-profit and nonprofit organizations were the most common organization account owners (i.e., 26% and 16%, respectively). Seven unique tweets were identified with a reach of over 400 Twitter users. The greatest reach exceeded 2,000 users. Temporal analysis identified retweet outliers (> 200 retweets per hour) that corresponded to a widely publicized event involving the response of a Twitter user to another user's joke. Content analysis indicated that Twitter is a platform that primarily functions to advocate, share personal experiences, or share information about management of tinnitus rather than to provide social support and build relationships. Conclusions Twitter accounts owned by organizations outnumbered individual accounts, and commercial/for-profit user accounts were the most frequently active organization account type. Analyses of social media use can be helpful in discovering issues of interest to the tinnitus community as well as determining which users and organizations are dominating social network conversations.

Modelling In-Patient Bed Usage Behaviour in a Department of Geriatric Medicine

1993 ◽  
Vol 32 (01) ◽  
pp. 79-81 ◽  
Author(s):  
P. Millard ◽  
S. McClean
Keyword(s):  
Census Data ◽  
Geriatric Medicine ◽  
Temporal Trends ◽  
Exponential Model ◽  
Bed Occupancy ◽  
Lengths Of Stay ◽  
Geriatric Hospital ◽  
Using Data ◽  
Acute Patients

Abstract:The flow of patients through geriatric hospitals has been previously described in terms of acute and long-stay states where the bed occupancy at a census point is modelled by a mixed exponential model. Using data for sixteen years the model was fitted to successive annual census points, in order to provide a description of temporal trends. While the number of acute patients has remained fairly stable during the period, the model shows that there has been a decrease in the number of long-stay patients. Mean lengths of stay in our geriatric hospital before death or discharge have decreased during the study period for both acute and long-stay patients.Using these fits of the mixed exponential model to census data, a method is provided for predicting future turnover of patients. These predictions are reasonably good, except when the turnover patterns go through a period of flux in which assumption of stability no longer holds. Overall, a methodology is presented which relates census analysis to the behaviour of admission cohorts, thus producing a means of predicting future behaviour of patients and identifying where there is a change in patterns.

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