Quantifying tooth variation within a single population of Albertosaurus sarcophagus (Theropoda: Tyrannosauridae) and implications for identifying isolated teeth of tyrannosauridsThis article is one of a series of papers published in this Special Issue on the theme Albertosaurus.

2010 ◽  
Vol 47 (9) ◽  
pp. 1227-1251 ◽  
Author(s):  
Lisa G. Buckley ◽  
Derek W. Larson ◽  
Miriam Reichel ◽  
Tanya Samman
Keyword(s):  
Multivariate Analyses ◽  
Phylogenetic Signal ◽  
A Priori ◽  
Principal Component ◽  
Canonical Variate ◽  
Large Sample Size ◽  
Data Set ◽  
Theropod Dinosaurs ◽  
Degree Of Separation ◽  
Lower Maastrichtian

Documenting variation in theropod dinosaurs is usually hindered by the lack of a large sample size and specimens representing several ontogenetic stages. Here, variation within 140 disassociated and seven in situ tyrannosaur teeth from the Upper Cretaceous (lower Maastrichtian) monodominant Albertosaurus sarcophagus (Theropoda: Tyrannosauridae) bonebed is documented. This sample represents the largest data set of teeth from one population of A. sarcophagus containing both adult and juvenile specimens. Tooth variation was assessed using multivariate analyses (principal component, discriminant, and canonical variate analyses). Heterodonty in the teeth of A. sarcophagus contributes to the large amount of variation in the data set. Premaxillary teeth are significantly different from maxillary and dentary teeth, but there is no quantifiable difference between a priori identified maxillary and dentary teeth. Juvenile and adult teeth of A. sarcophagus show apparent quantitative differences that are size dependent on closer investigation, suggesting a cautious approach when interpreting multivariate analyses to identify novel tooth morphologies. Multivariate analyses on teeth of A. sarcophagus and published tooth data from other North American tyrannosaurid species reveals species-level clusters with little separation. The degree of separation among tooth clusters may reveal a phylogenetic signal in tyrannosaurid teeth.

scholarly journals Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4798
Author(s):  
Munmi Sarma ◽  
Noelia Romero ◽  
Xavier Cetó ◽  
Manel del Valle
Keyword(s):  
A Priori ◽  
Electronic Tongue ◽  
Principal Component ◽  
Canonical Variate ◽  
Ann Model ◽  
Inclusion Removal ◽  
Discrimination Ability ◽  
Improved Performance ◽  
Voltammetric Electronic Tongue ◽  
The Impact

Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

The variation of angles between anterior and posterior carinae of tyrannosaurid teeth

2012 ◽  
Vol 49 (3) ◽  
pp. 477-491 ◽  
Author(s):  
Miriam Reichel
Keyword(s):  
Strong Correlation ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Tooth Size ◽  
Data Set ◽  
In Situ Data ◽  
Principal Component Analyses ◽  
Canonical Variates ◽  
Discriminant Analyses

Tyrannosaurid tooth measurements have been shown to be a powerful tool for systematic analyses, as well as for studies on function and evolution of theropod dentition. In this analysis, a variable not previously addressed in depth is added to the tyrannosaurid data set. The angle between the anterior and posterior carinae can be difficult to measure consistently and a method is hereby proposed through the use of a digitizer. Five tyrannosaurid genera were analyzed: Tyrannosaurus , Tarbosaurus , Albertosaurus , Daspletosaurus , and Gorgosaurus . Only in situ data were used, and therefore some of the taxa had a limited amount of information available for this analysis. The measurements were analyzed through multivariate analyses using Paleontological Statistics (PAST), version 2.06. The analyses included principal component analyses (PCAs), discriminant analyses (DAs), and canonical variates analyses (CVAs). The results of these analyses revealed that the angle between carinae contributes significantly to the variation in the tyrannosaurid tooth data set. Additionally, this variable showed a strong correlation to tooth function (and, consequently, to tooth families), rather than tooth size. The variation observed between taxa at this stage seems insufficient for systematic purposes, however additional in situ data would help improve the effectiveness of this tool.

A multivariate approach to infer locomotor modes in Mesozoic mammals

Paleobiology ◽  
2015 ◽  
Vol 41 (2) ◽  
pp. 280-312 ◽  
Author(s):  
Meng Chen ◽  
Gregory P. Wilson
Keyword(s):  
Multivariate Analyses ◽  
Canonical Variate ◽  
Postcranial Skeleton ◽  
Morphometric Data ◽  
Linear Measurements ◽  
Data Set ◽  
Limited Region ◽  
Selective Pressures ◽  
Mesozoic Mammals ◽  
Long Time

AbstractEcomorphological diversity of Mesozoic mammals was presumably constrained by selective pressures imposed by contemporary vertebrates. In accordance, Mesozoic mammals for a long time had been viewed as generalized, terrestrial, small-bodied forms with limited locomotor specializations. Recent discoveries of Mesozoic mammal skeletons with distinctive postcranial morphologies have challenged this hypothesis. However, ecomorphological analyses of these new postcrania have focused on a single taxon, a limited region of the skeleton, or have been largely qualitative.For more comprehensive locomotor inference in Mesozoic mammals, we applied multivariate analyses to a morphometric data set of extant small-bodied mammals. We used 30 osteological indices derived from linear measurements of appendicular skeletons of 107 extant taxa that sample 15 orders and eight locomotor modes. Canonical variate analyses show that extant small-bodied mammals of different locomotor modes have detectable and predictable morphologies. The resulting morphospace occupation reveals a morphofunctional continuum that extends from terrestrial to scansorial, arboreal, and gliding modes, reflecting an increasingly slender postcranial skeleton with longer limb output levers adapted for speed and agility, and extends from terrestrial to semiaquatic/semifossorial and fossorial modes, reflecting an increasingly robust postcranial skeleton with shorter limb output levers adapted for powerful, propulsive strokes. We used this morphometric data set to predict locomotor mode in ten Mesozoic mammals within the Docodonta, Multituberculata, Eutriconodonta, “Symmetrodonta,” and Eutheria. Our results indicate that these fossil taxa represent five of eight locomotor modes used to classify extant taxa in this study, in some cases confirming and in other cases differing from prior ecomorphological assessments. Together with previous locomotor inferences of 19 additional taxa, these results show that by the Late Jurassic mammals had diversified into all but the saltatorial and active flight locomotor modes, and that this diversification was greatest in the Eutriconodonta and Multituberculata, although sampling of postcranial skeletons remains uneven across taxa and through time.

Diversity and variation of theropod dinosaur teeth from the uppermost Santonian Milk River Formation (Upper Cretaceous), Alberta: a quantitative method supporting identification of the oldest dinosaur tooth assemblage in Canada

2008 ◽  
Vol 45 (12) ◽  
pp. 1455-1468 ◽  
Author(s):  
Derek W. Larson
Keyword(s):  
Principal Component ◽  
Canonical Variate Analysis ◽  
Canonical Variate ◽  
Large Sample Size ◽  
Accurate Identification ◽  
Theropod Dinosaur ◽  
Variate Analysis ◽  
Dinosaur Teeth ◽  
Milk River Formation ◽  
Range Of Variation

The Santonian Deadhorse Coulee Member of the Milk River Formation preserves the oldest dinosaur body fossils found in Alberta. However, vertebrate remains consist almost exclusively of isolated elements and microvertebrate assemblages. Here, 1572 relatively complete shed non-avian theropod teeth from 20 localities in the Deadhorse Coulee Member are measured and analyzed to assess species diversity. Teeth are referred to or similar to Tyrannosaurinae indet., cf. Richardoestesia gilmorei , cf. Richardoestesia isosceles , Dromaeosauridae indet., Dromaeosaurinae indet., Velociraptorinae indet., and cf. Paronychodon lacustris . For the taxa identified, the large sample size allows for the assessment of their range of variation and accurate identification, without the benefit of comparable material of this age. Multivariate statistics, including a principal component analysis and a canonical variate analysis, provide reasonable separation of all taxa, although better results are achieved by separate analyses based on qualitative observations of denticle shape. The best results of the canonical variate analysis identified 96.0% of specimens correctly. This corroborates the qualitative identification of specimens and illustrates a valid way of evaluating diversity in areas and formations from which no described jaw material is known.

RootProf: software for multivariate analysis of unidimensional profiles

2014 ◽  
Vol 47 (3) ◽  
pp. 1087-1096 ◽  
Author(s):  
Rocco Caliandro ◽  
Danilo Benny Belviso
Keyword(s):  
Multivariate Analysis ◽  
Latent Variable ◽  
Clustering Algorithm ◽  
High Energy Physics ◽  
A Priori ◽  
Principal Component ◽  
High Energy ◽  
Quantitative Information ◽  
Quantitative Phase Analysis ◽  
Data Set

RootProfis a multi-purpose program which implements multivariate analysis of unidimensional profiles. Series of measurements, performed on related samples or on the same sample by varying some external stimulus, are analysed to find trends in data, classify them and extract quantitative information. Qualitative analysis is performed by using principal component analysis or correlation analysis. In both cases the data set is projected in a latent variable space, where a clustering algorithm classifies data points. Group separation is quantified by statistical tools. Quantitative phase analysis of a series of profiles is implemented by whole-profile fitting or by an unfolding procedure, and relies on a variety of pre-processing methods. Supervised quantitative analysis can be applied, provideda prioriinformation on some samples is provided.RootProfcan be applied to measurements from different techniques, which can be combined by means of a covariance analysis. A specific analysis for powder diffraction data allows estimation of the average size of crystal domains.RootProfborrows its graphics and data analysis capabilities from the Root framework, developed for high-energy physics experiments.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

scholarly journals PROMIS Scales for Assessment of Persistent Post-COVID Symptoms: A Cross Sectional Study

2021 ◽  
Vol 12 ◽  
pp. 215013272110304
Author(s):  
Ravindra Ganesh ◽  
Aditya K. Ghosh ◽  
Mark A. Nyman ◽  
Ivana T. Croghan ◽  
Stephanie L. Grach ◽  
...  
Keyword(s):  
Health Status ◽  
Social Roles ◽  
Disease Process ◽  
Cross Sectional Study ◽  
Large Sample Size ◽  
Sectional Study ◽  
Cross Sectional ◽  
Data Set ◽  
Patient Reported ◽  
The Impact

Objective Persistent post-COVID symptoms are estimated to occur in up to 10% of patients who have had COVID-19. These lingering symptoms may persist for weeks to months after resolution of the acute illness. This study aimed to add insight into our understanding of certain post-acute conditions and clinical findings. The primary purpose was to determine the persistent post COVID impairments prevalence and characteristics by collecting post COVID illness data utilizing Patient-Reported Outcomes Measurement Information System (PROMIS®). The resulting measures were used to assess surveyed patients physical, mental, and social health status. Methods A cross-sectional study and 6-months Mayo Clinic COVID recovered registry data were used to evaluate continuing symptoms severity among the 817 positive tested patients surveyed between March and September 2020. The resulting PROMIS® data set was used to analyze patients post 30 days health status. The e-mailed questionnaires focused on fatigue, sleep, ability to participate in social roles, physical function, and pain. Results The large sample size (n = 817) represented post hospitalized and other managed outpatients. Persistent post COVID impairments prevalence and characteristics were determined to be demographically young (44 years), white (87%), and female (61%). Dysfunction as measured by the PROMIS® scales in patients recovered from acute COVID-19 was reported as significant in the following domains: ability to participate in social roles (43.2%), pain (17.8%), and fatigue (16.2%). Conclusion Patient response on the PROMIS® scales was similar to that seen in multiple other studies which used patient reported symptoms. As a result of this experience, we recommend utilizing standardized scales such as the PROMIS® to obtain comparable data across the patients’ clinical course and define the disease trajectory. This would further allow for effective comparison of data across studies to better define the disease process, risk factors, and assess the impact of future treatments.

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