J-Mark 845-DS Driver
Phosphorylation of the GluR1 S and S sites was significantly increased in the Mice with serine to alanine mutations of GluR1 S and S Prange O,; Gauthier-Campbell C,; Aguilera-Moreno A,; Nicoll RA,; Bredt DS He K,; Song L,; Cummings LW,; Goldman J,; Huganir RL,; Lee HK. J Med Internet Res. 2 Brian H Spitzberg, PhD,3 Li An, PhD,2 J Mark Gawron, PhD,4 Dipak K Gupta, PhD,5 Jiue-An .. Seattlec, Cite this:Nano Letters , 2, 8, .. Pei-Xi Wang, Vitor M. Zamarion, Wadood Y. Hamad, Mark J. MacLachlan P. H. Zhou, D. S. Xue. Journal of.
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J-Mark 845-DS Driver
Harvard Catalyst Profiles
Originally published in the Journal of Medical Internet Research http: This is an open-access article distributed under J-Mark 845-DS terms of the Creative Commons Attribution License http: The complete bibliographic information, a link J-Mark 845-DS the original publication on http: This article has been cited by other articles in PMC.
Abstract Background J-Mark 845-DS plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming.
- Mark Daly Harvard Catalyst Profiles Harvard Catalyst
In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at J-Mark 845-DS cost. Many data sources for infoveillance exist, J-Mark 845-DS this study focuses on status updates tweets from the Twitter microblogging website.
Objective The aim of this study was to explore the interaction between cyberspace J-Mark 845-DS activity, measured by keyword-specific tweets, J-Mark 845-DS real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness ILI and weekly pertussis incidence.
The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: Methods Tweets were collected J-Mark 845-DS a mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. J-Mark 845-DS
Tweet collection resulted J-Mark 845-DSflu, influenza, pertussis, and whooping cough tweets. The correlation coefficients between tweets or J-Mark 845-DS of tweets and disease occurrence were calculated and trends were presented graphically. Results Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis.
Mark Pletcher UCSF Profiles
Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL J-Mark 845-DS address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets.
Conclusions This study demonstrates that not only does keyword choice play an J-Mark 845-DS role in how well tweets correlate with disease occurrence, but that the subgroup J-Mark 845-DS tweets used for analysis is also important. This exploratory work shows potential in the J-Mark 845-DS of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field.
Twitter, infoveillance, infodemiology, cyberspace, syndromic surveillance, influenza, pertussis, whooping cough Introduction Background Use of the Internet has shifted from being solely a one-way transfer of information to an interactive multidimensional channel. J-Mark 845-DS
Cyberspace resides as a source of information accessible to the user who is able to contribute to cyberspace J-Mark 845-DS social media and online communities [ 1 ]. Infodemiology is the study of the distribution and causal factors J-Mark 845-DS information in cyberspace and its ability to improve public health [ 2 ]. The Internet provides many resources for infodemiology, including search engine queries ie, Google Flu Trends [ 3 ]publications, marketing campaigns, and user-generated content, such as blogs J-Mark 845-DS social media status updates [ 2 ].
Researchers are pioneering a variety of methods and applications using these resources for disease detection J-Mark 845-DS [ 4 ] for overview. This study focuses on the infodemiology of pertussis-related also called whooping cough and influenza-related status updates on Twitter tweets. Every year millions of Americans become infected with the flu, resulting in illness, missed work and school days, and death. Deaths from seasonal influenza occur mostly in young children and the elderly, largely because of flu J-Mark 845-DS and the exacerbation of existing conditions, such as congestive heart failure [ 5 ].
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Influenza causes a J-Mark 845-DS economic burden associated with J-Mark 845-DS in productivity because of missed work and health care costs [ 6 ]. Pertussis infects a much smaller population, but can result in severe complications, especially among those who are young and unvaccinated.
Death and violent convulsions occur in approximately 1. As of December 29,Washington J-Mark 845-DS had experienced pertussis cases, 5. The early notification J-Mark 845-DS disease outbreaks greatly increases the ability of affected communities to control and treat an epidemic. Traditional surveillance methods are a vital factor in the control of diseases, but there is often a time lag between the reporting of individual cases and the accumulation of these data into a report [ 9 ].