scholarly journals Is Whip Use Important to Thoroughbred Racing Integrity? What Stewards’ Reports Reveal about Fairness to Punters, Jockeys and Horses

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1985
Author(s):  
Kirrilly Thompson ◽  
Phil McManus ◽  
Dene Stansall ◽  
Bethany J. Wilson ◽  
Paul D. McGreevy
Keyword(s):  
Logistic Regression ◽  
Mixed Method ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Empirical Support ◽  
Statistical Testing ◽  
Units Of Analysis ◽  
Thoroughbred Racing ◽  
Method Analysis ◽  
Qualitative Coding

The idea that whip use is critical to thoroughbred racing integrity is culturally entrenched but lacks empirical support. To test the longstanding beliefs that whip use aids steering, reduces interference, increases safety and improves finishing times, we conducted a mixed-method analysis of 126 race reports produced by official stewards of the British Horseracing Authority, representing 1178 jockeys and their horses. We compared reports from 67 “Hands and Heels” races, where whips are held but not used (whipping-free, WF), with 59 reports from case-matched races where whipping was permitted (whipping permitted, WP). Qualitative coding was used to identify and categorise units of analysis for statistical testing via logistic regression and linear mixed model regression. For both types of race, we explored stewards having anything to report at all, movement on course, interference on course, incidents related to jockey behaviour and finishing times. There were no statistically significant differences between WF and WP races for anything to report (OR: 3.06; CI: 0.74–14.73), movement on course (OR: 0.90; CI: 0.37–2.17), interference (OR: 0.90; CI: 0.37–2.17), jockey-related incidents (OR: 1.24; CI: 0.32–5.07), and race times (0.512 s, t = 1.459, p = 0.150). That is, we found no evidence that whip use improves steering, reduces interference, increases safety or improves finishing times. These findings suggest that the WF races do not compromise racing integrity. They also highlight the need for more effective ways to improve the steering of horses.

scholarly journals LuxUS: DNA Methylation Analysis Using Generalized Linear Mixed Model with Spatial Correlation

2019 ◽  
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data

AbstractMotivationDNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level.ResultsWe have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that by utilizing the spatial correlation we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing data set show that LuxUS is able to detect biologically significant differentially methylated cytosines.AvailabilityThe tool is available at https://github.com/hallav/LuxUS.Supplementary informationSupplementary data are available at bioRxiv.

scholarly journals Newborn Infant Parasympathetic Evaluation for the assessment of analgosedation adequacy in infants treated by mechanical ventilation - a multicenter pilot study.

2021 ◽  
Author(s):  
Wojciech Walas ◽  
Ewelina Malinowska ◽  
Zenon Halaba ◽  
Tomasz Szczapa ◽  
Julita Latka-Grot ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Logistic Regression ◽  
Newborn Infant ◽  
Neonatal Intensive Care ◽  
Continuous Monitoring ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Multivariable Logistic Regression Model ◽  
Mechanically Ventilated ◽  
Newborn Infant Parasympathetic Evaluation

IntroductionAdequate analgosedation is important in infants treated in Pediatric/Neonatal Intensive Care Unit (P/NICU), because both too deep and insufficient analgosedation is disadvantageous. To assess the severity of pain, several behavioral and behavioral-physiological scales are used, but their usefulness is limited. It is therefore justified to search for additional methods to assess the adequacy of analgosedation in these patients. The aim of the presented study is to evaluate usefulness of Newborn Infant Parasympathetic Evaluation (NIPE) in the assessment of analgosedation quality in infants requiring mechanical ventilation, who are treated in P/NICUs.Material and methodsWe performed simultaneously 180 COMFORT B assessments and Heart Rate Variability (HRV) measurements using NIPE monitor in 30 mechanically ventilated infants receiving analgosedation. Generalized linear mixed model with logit link function was used in order to perform logistic regression analysis to assess relationship between NIPEi/NIPEm and deep sedation.ResultsMultivariable logistic regression model has shown that NIPEi and NIPEm values were higher when analgosedation was deep as compared to when it was moderate or insufficient [OR (95% CI): NIPEm- 1.065 (1.007 - 1.126), p=0.03; NIPEi- 1.068 (1.016 - 1.123), p=0.01].ConclusionsNIPE indexes are significantly higher in patients whose assessment on the behavioral scale indicates deep analgosedation as compared to those in whom it indicates moderate or insufficient analgosedation. Allowing continuous monitoring, NIPE device may be a valuable assisting tool in the assessment of analgosedation quality in mechanically ventilated newborns and infants.

scholarly journals LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation

2020 ◽  
Vol 36 (17) ◽  
pp. 4535-4543
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Linear Mixed Model ◽  
Epigenetic Modification ◽  
Random Effect ◽  
Generalized Linear Mixed Model ◽  
Statistical Testing ◽  
Supplementary Information

Abstract Motivation DNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level. Results We have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that using the spatial correlation, we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing dataset show that LuxUS is able to detect biologically significant differentially methylated cytosines. Availability and implementation The tool is available at https://github.com/hallav/LuxUS. Supplementary information Supplementary data are available at Bioinformatics online.

Logistic regression when covariates are random effects from a non-linear mixed model

2011 ◽  
Vol 53 (5) ◽  
pp. 735-749 ◽  
Author(s):  
Rolando De la Cruz ◽  
Guillermo Marshall ◽  
Fernando A. Quintana
Keyword(s):  
Logistic Regression ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Non Linear

scholarly journals First report of canine parvovirus molecular detection in Bangladesh

2021 ◽  
Vol 14 (4) ◽  
pp. 1038-1043
Author(s):  
F. M. Yasir Hasib ◽  
Sharmin Akter ◽  
Sharmin Chowdhury
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Clinical Signs ◽  
Canine Parvovirus ◽  
Amplification Refractory Mutation System ◽  
Effective Prevention

Background and Aim: Canine parvovirus (CPV) is the most important cause of mortality in dogs in many parts of the world. Clinical cases exhibit characteristic signs, including foul-smelling bloody diarrhea, vomiting, fever, and dehydration. This study assessed field and vaccine variants of parvovirus in the Chattogram metropolitan area, Bangladesh. The investigation also aimed to identify risk factors for this disease. This research is the first to identify the presence of CPV in Bangladesh through molecular examination. Materials and Methods: From October to December 2019, a total of 100 dogs were included in the study. Rectal swabs were taken from all dogs. Twenty dogs showed clinical signs of parvovirus. All clinically affected animals along with 20 randomly selected healthy dogs were tested using amplification refractory mutation system (ARMS)-polymerase chain reaction (PCR) to identify variants from the samples. Logistic regression model analysis was performed to determine the possible risk factors for CPV. Results: ARMS-PCR showed the presence of all three variants, CPV2a, CPV2b, and CPV2c, in clinically ill dogs, and vaccines available in the study area showed either CPV2a or CPV2b strain. The CPV2c variants showed a higher incidence than the other variants. All apparently healthy animals tested were molecularly negative. Multivariable logistic regression model (generalized linear mixed model) indicated that exotic breeds were 3.83 times more likely to be infected by CPV than local breeds. Furthermore, dogs reared in semi-intensive and extensive management systems were 3.64 and 3.79 times more likely to be infected, respectively, than those reared in an intensive management system. Conclusion: These findings provide practitioners and pet owners information on the occurrence of different variants and help design effective prevention strategies for CPV infection.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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