scholarly journals Hooked on you: shape of attachment structures in cymothoid isopods reflects parasitic strategy

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Charles Baillie ◽  
Rachel L. Welicky ◽  
Kerry A. Hadfield ◽  
Nico J. Smit ◽  
Stefano Mariani ◽  
...  
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Shape Variation ◽  
Geometric Morphometric ◽  
Parasite Ecology ◽  
Attachment Structures ◽  
Main Driver ◽  
Shared Ancestry ◽  
Host Exploitation

Abstract Background Parasite attachment structures are critical traits that influence effective host exploitation and survival. Morphology of attachment structures can reinforce host specificity and niche specialisation, or even enable host switching. Therefore, it is important to understand the determinants of variation in attachment structures. Cymothoid isopods are striking ectoparasites of fishes that include the infamous ‘tongue-biters.’ They are known to parasitise hosts in one of four qualitatively distinct anatomical regions. Here, we quantify variation in cymothoid attachment structures — hook-like appendages called dactyli — and test whether differences in dactylus shape are correlated with parasite mode (where they attach), allometry, or both, using multivariate ordinary least squares regression. We also assess the influence of shared ancestry on shape using a molecular phylogeny to weight our models using phylogenetic generalised least squares regression. Results We find clear differences in shape between externally-attaching and internally-attaching cymothoids but also between anterior and posterior dactyli across various species with the same attachment mode. Allometric effects are significant for anterior but not posterior dactyli. Mouth-attaching species show greater shape variability than gill- and mouth-attaching species. We find no evidence that there are clade-specific patterns of association between parasite mode and dactylus shape. Conclusions Parasite mode appears to be the main driver of attachment morphology. This likely reflects several components of parasite ecology including feeding and functional demands of attachment in different microhabitats. Geometric morphometric approaches to the quantification of shape variation of simple structures is an effective tool that provides new insights into the evolvability of parasite attachment.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

scholarly journals Using Quantile Regression to Estimate Intervention Effects Beyond the Mean

2019 ◽  
Vol 79 (5) ◽  
pp. 883-910 ◽  
Author(s):  
Spyros Konstantopoulos ◽  
Wei Li ◽  
Shazia Miller ◽  
Arie van der Ploeg
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Large Scale ◽  
Social Science Research ◽  
Empirical Work ◽  
Science Research ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Intervention Effects ◽  
Education Data

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails).

scholarly journals Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

2019 ◽  
Vol 11 (14) ◽  
pp. 1730 ◽  
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Spectral Band ◽  
Ordinary Least Squares ◽  
Landsat 8 ◽  
Eastern Siberia ◽  
Landscape Changes ◽  
Least Squares Regression ◽  
Spectral Bandpass ◽  
Sentinel 2

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.

Informing Prescribing Allocations at District Level in England

1997 ◽  
Vol 2 (3) ◽  
pp. 154-159 ◽  
Author(s):  
Nigel Rice ◽  
Roy Carr-Hill ◽  
David Roberts ◽  
David Lloyd
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Least Squares ◽  
General Practitioners ◽  
Supply And Demand ◽  
Ordinary Least Squares ◽  
Reciprocal Relationship ◽  
District Level ◽  
Least Squares Regression ◽  
Adult Males

Objectives: To derive a predictive model based on the morbidity, demographic and socio-economic characteristics of district populations to explain variations in prescribing costs in England. Method: Inter-relations between morbidity, demographic, socio-economic, general practice supply characteristics and net ingredient cost per age, sex and temporary resident originated prescribing unit (ASTRO-PU) were explored statistically for 90 districts in England using 1994 cost data. The possibility of mutual inter-relationship between ‘supply’ and ‘demand’ was examined; then the associations between a range of factors and prescribing costs were estimated using ordinary least squares regression and the predictive power of the possible models was systematically examined. Results: Whilst there was a relatively weak relationship between the supply factors that were measured, there did not appear to be any reciprocal relationship. Three parsimonious models estimated using ordinary least squares multiple regression techniques based on combinations of permanent sickness, low birth weight and the proportion of general practitioners registered for postgraduate certificate of education were identified. The models explained up to 61% of variation between districts in prescribing costs. Conclusions: ‘Need’ and ‘supply’ characteristics are independently associated with variations in prescribing costs at district level. The negative association between the proportion of general practitioners eligible for postgraduate education allowance and prescribing costs may reflect ‘better’ prescribing but could not be introduced into a resource allocation formula without introducing perverse incentives. The combination of permanent sickness and low birth weight complement each other by providing a proxy measure of morbidity mostly applicable to adult males (permanent sickness) and mothers (low birth weight being a measure of maternal health). These variables should be considered further for use in the process of allocating resources for prescribing to districts.

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