Jitter Remains Stable Throughout a Single Fiber EMG Session in Healthy and My-Asthenic Muscles

2021 ◽  
pp. 1-4
Author(s):  
Evangelos Anagnostou ◽  
Panagiota Dimopoulou ◽  
Vasiliki Zouvelou ◽  
Nikolaos Karandreas ◽  
Thomas Zambelis
Keyword(s):  
Linear Regression ◽  
Myasthenia Gravis ◽  
Roc Analysis ◽  
Temporal Order ◽  
Regression Line ◽  
Single Fiber ◽  
Orbicularis Oculi ◽  
Rank Number ◽  
The Individual

Fatigability is the hallmark of myasthenia gravis (MG). It is not clear, however, whether there is an analogous increase in jitter during the course of a single fiber electromyography (SFEMG) session. The individual jitter values of all potentials of 76 normal and 44 myasthenic orbicularis oculi muscles were assigned a rank number according to their temporal order in which they were collected and linear regression was performed to determine if the slope of the regression line was significantly different from zero. Control and MG subjects displayed rather flat linear regression lines with non-significant positive or negative slopes. Accordingly, ROC analysis yielded areas under the curve near 0.5. We conclude that there is no systematic jitter increase during the collection of 20 potential pairs in a typical SFEMG session.

Stimulated single-fiber EMG of the frontalis and orbicularis oculi muscles in ocular myasthenia gravis

2003 ◽  
Vol 28 (4) ◽  
pp. 501-503 ◽  
Author(s):  
J. Valls-Canals ◽  
M. Povedano ◽  
J. Montero ◽  
J. Pradas
Keyword(s):  
Myasthenia Gravis ◽  
Single Fiber ◽  
Orbicularis Oculi ◽  
Ocular Myasthenia ◽  
Ocular Myasthenia Gravis

scholarly journals 218 Quantifying resilience in growing pigs under a heavy disease challenge using daily individual feed intake records

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 45-46
Author(s):  
Austin M Putz ◽  
John Harding ◽  
Micheal Dyck ◽  
PigGen Canada ◽  
Frederic Fortin ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Feed Intake ◽  
Regression Line ◽  
Genetic Correlations ◽  
Growing Pigs ◽  
Simple Linear Regression ◽  
Treatment Rate ◽  
Immune Parameters ◽  
The Individual

Abstract Resilience is defined as the ability to maintain productivity through any number of stressors such as disease or heat stress. A total of 2273 animals, in groups of ~60–75 piglets, were sent through a natural disease challenge barn every three weeks that consisted of three phases: i) a healthy quarantine nursery to collect immune parameters, ii) a challenge nursery, and iii) a challenged finishing unit which was attached to the challenge nursery. Individual feed intake (FI) was collected in the finishing unit with IVOG® feeders and aggregated into daily totals. Three resilience phenotypes were extracted from the individual trends in feed intake over time, including the root mean square error (RMSE), the quantile regression (QR), and run of depression (ROD) phenotypes. The RMSE phenotype was calculated by fitting a simple linear regression of FI on age within animal and taking the square root of the average squared residual from the model. To calculate the QR phenotype, a 5% quantile regression was fitted across all daily feed intake records to set a lower bound for off-feed days. The QR phenotype was quantified as the proportion of days within animal that fell below the overall quantile regression line. The ROD phenotype was calculated by fitting a within animal linear regression line, flagging extended consecutive stretches of days below that regression line (i.e. a ROD), and calculating the percentage of days that fall within a ROD for each animal. Heritability estimates for the FI resilience phenotypes ranged from 0.10±0.04 to 0.17±0.04. Genetic correlations of the FI resilience phenotypes with mortality and treatment rate ranged from 0.66±20 to 0.94±0.20. This research demonstrates that resilience phenotypes can effectively quantify resilience and can add value to a breeding program to improve resilience to many stressors. Funded by Genome Canada, Genome Alberta, and PigGen Canada.

scholarly journals Assessment of shoreline changes for setback zone establishment from Son Tra (Da Nang city) to Cua Dai (Hoi An city), Vietnam

2020 ◽  
Vol 42 (4) ◽  
pp. 363-383
Author(s):  
Ngo Van Liem ◽  
Dang Van Bao ◽  
Dang Kinh Bac ◽  
Ngo Chi Cuong ◽  
Pham Thi Phuong Nga ◽  
...  
Keyword(s):  
Linear Regression ◽  
Coastal Erosion ◽  
Regression Line ◽  
Central Area ◽  
Remote Sensing Data ◽  
Shoreline Change ◽  
Shoreline Changes ◽  
Climate Change Response ◽  
Changes Over Time ◽  
Change Response

The most important function of the coastal setback is to minimize damage due to coastal erosion, climate change response, and sea-level rise. There are many directions and methods of researching and assessing coastal changes and coastal erosion. This study presents the results of the shoreline changes in the area from Son Tra (Da Nang City) to Cua Dai (Hoi An City), Central Vietnam based on remote sensing data from 1965 to 2019. Three methods are used to include End Point Ratio (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR). The results show that the EPR method is effective when calculating the rate of shoreline changes only at two different times. For more objective and reliable calculation, it is necessary to assess the shoreline changes over time. Meanwhile, the LRR method was shown to be superior because all shoreline data were taken into account during the construction of the regression line. However, when there is much shoreline data with different reliability, the WLR method proved more superior because of limited objective errors. The results show that from 1965 to 2019, the coast of the Son Tra - Cua Dai area had quite complicated fluctuations, of which the northern area (Son Tra) tended to accretion, the central area tends to be alternate between accretion and erosion, while the south area (Cua Dai) is strong to very strong erosion. The coast with sudden changes is the Cua Dai area with the shoreline change envelope (SCE) reaching 512m. The results also allow us to divide the coast of the Son Tra - Cua Dai area into 30 segments. They are clustered into 8 groups with different levels of erosion and accretion. This is an important basis for the setback zone establishment in the study area.

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