scholarly journals Intra- and interspecific discrimination of Scorpaena species from the Aegean, Black, Mediterranean and Marmara seas

2021 ◽  
Vol 85 (3) ◽  
pp. 197-209
Author(s):  
Serdar Yedier ◽  
Derya Bostanci
Keyword(s):  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Canonical Discriminant Analysis ◽  
Species Level ◽  
Morphometric Traits ◽  
Morphometric Measurements ◽  
Morphometric Characters ◽  
Components Analysis

This study was conducted to discriminate five Scorpaena species and populations of each species according to morphometric characters. A total of 1865 fish specimens were collected from the eight locations in the four Turkish seas: Antalya, Balıkesir, Çanakkale, Hatay, İzmir, Marmara Ereğlisi, Ordu and Şile. In the study, 26 morphometric traits were measured for intra- and interspecific discrimination of five Scorpaena species. The data were subjected to analysis of variance, principal components analysis (PCA) and canonical discriminant analysis. As results of the PCA, 10 traits for S. maderensis and S. scrofa, 12 traits for S. elongata and 13 traits for S. notata and S. porcus were found to be important for intraspcific discrimination. The overall classification scores of intraspecific discrimination were determined as 94.6% for S. elongata, 90.5% for S. maderensis, 96.7% for S. notata, 96.5% for S. porcus and 92.2% for S. scrofa. The PCA indicated that 13 morphometric measurements among the 26 traits are important in the interspecific discrimination of five Scorpaena species. The cross-validated canonical discriminant analysis was correctly classified as 97.4% at the Scorpaena species level. The discrimination of correctly classified species ranged from 94.8% to 100%. Finally, we demonstrated that the morphometric characters examined in the present study can be used successfully in the intra- and interspecific discrimination of Scorpaena species from different habitats.

Quantitative Phenotyping and Discriminant Analysis Improve Scoring Classification in Late B-Lymphoproliferative Disorders.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1463-1463
Author(s):  
Georges Jung ◽  
Sylvie Thiebault ◽  
Jean-Claude Eisenmann ◽  
Eckart Wunder ◽  
Marie Haas ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Scoring System ◽  
Low Grade ◽  
Surface Markers ◽  
Correct Classification ◽  
Chi Square ◽  
Components Analysis

Abstract Multivariate analysis classification of chronic lymphocytic leukemia (CLL) and lymphoma (non-CLL) disorders is investigated in 299 patients by an extended panel of surface markers, and compared with Matutes classical scoring proposal. Diagnosis was based on clinical features, cell morphology, node or bone marrow histology, and immunological scoring system. Results are obtained on directly labeled tumoral cells by flow cytometry gating. Patients included 154 CLL, 2 Richter transformation, and 143 lymphoma (26 follicular, 49 lymphocytic, 18 other low-grade, 7 Waldenström macroglobulinemia, 13 mantel, 11 diffuse large-cell, 6 Burkitt, 4 marginal zone-cell, 5 hairy-cell leukemia, 2 MALT, 1 prolymphocytic leukemia, 1 SLVL). For CD43, FMC7, CD23, CD5, CD79b (% stained cells) and CD20, CD22 surface antigen intensities Chi-Square values indicate very high probability of correct classification (varing from 621 to 94.9; p<0.0000). If, alternatively, % of CD22, CD20, CD19 and intensities of CD79b, CD5, CD19, CD43, CD23 and kappa/lamba chains are employed, Chi-Square yields values of lower significance (varing from 65 to 0.1; p<0.0000 to 0.6573). Using classical panel scoring with CD79b, 82.4 % of patients were correctly classified, compared to 84.5% after replacing CD79b by CD22 intensity. If CD43 is added, correct classification increased to 89.6% and 88.1% of patients, respectively; this improvement is due to better allocation of CLL. In discriminant analysis 91.3% of patients are correctly classified with the panel including CD79b, and 90.9% with CD22 intensity. CD43 enhances the allocation of either one to 94.3%. Using our previous discriminant analysis with CD79b (Jung G, et al. Br J Haematol.2003; 120:496–499), this blind analysis correctly classified the population in 87.1%, compared to 91.3% with the new one. By adding CD43, it moved from 92.4% up to 94.3%. In order to find the optimal combination of the selected best markers, a stepwise probit discrimination was performed. Using CD43 and FMC7 yields a correct classification of 90.3%; after addition of CD5, CD79b, CD23, and CD22 intensity, efficiency increased to 94.6%. Further added markers don’t improve classification. Efficiency of this panel was further confirmed by hierarchical cluster and principal components analysis. Cluster analysis with squared Euclidian distances separated CLL from non-CLL patients with low overlaps: 86.6% of cases are correctly identified. Separated points in the plot representing patients with CLL and non-CLL, obtained by principal components analysis of surface markers, confirm the high predictive potential of this panel. The same analysis of surface marker positions for non-CLL suggests use of: % of CD79b, FMC7, and CD22 intensity, and for CLL: % of CD5, CD23, CD43. So, the addition of CD43 improves as well the discriminant function as the scoring system. Our selected panel of best markers is useful in distinguishing CLL from non-CLL and offers a better distinction by discriminant analysis. Furthermore quantitative expression of each marker and its predictive value improve diagnosis and classification.

scholarly journals Basic dimensions of experience of architectural objects’ expressiveness: Effect of expertise

Psihologija ◽  
2015 ◽  
Vol 48 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Slobodan Markovic ◽  
Djordje Alfirevic
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Processing Fluency ◽  
High Regularity ◽  
Components Analysis

The purpose of the present study was to compare the structure of experience of architectural expressiveness of architects and non-architects. Twenty architects and twenty non-architects rated twenty photographs of architectural objects on thirty expressiveness scales. Principal components analysis revealed four factors for both groups of participants: Aggressiveness, Regularity, Color and Aesthetics. In a cluster analysis two clusters of architectural objects were obtained: Choleric (high Aggressiveness and Color) and Phlegmatic (low Aggressiveness and Color, and high Regularity). All objects were highly rated on Aesthetics. Analysis of variance has shown that architects rated both clusters as less aggressive than non-architects. Also, experts rated the Phlegmatic cluster as more aesthetic, while nonexperts rated the Choleric cluster as more aesthetic. These results supported the Processing Fluency model: compared to non-architects, architects processed the expressive information of minimalistic objects (Phlegmatic cluster) with ease, which led towards positive hedonic reactions and higher.

Robust Statistics: A Survey and Some Prescriptions

1976 ◽  
Vol 1 (4) ◽  
pp. 285-312 ◽  
Author(s):  
Howard Wainer
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Standard Deviation ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Robust Statistics ◽  
Robust Methods ◽  
The Mean ◽  
Components Analysis

It is noted that the usual estimators that are optimal under a Gaussian assumption are very vulnerable to the effects of outliers. A survey of robust alternatives to the mean, standard deviation, product moment correlation, t-test, and analysis of variance is offered. Robust methods of factor analysis, principal components analysis and multivariate analysis of variance are also surveyed, as are schemes for outlier detection.

The Fitness Facility Membership Questionnaire: A Measure of Reasons for Joining

1997 ◽  
Vol 85 (3) ◽  
pp. 907-916 ◽  
Author(s):  
Jan L. Drummond ◽  
Heather S. Lenes
Keyword(s):  
Discriminant Analysis ◽  
Intrinsic Motivation ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Extrinsic Motivation ◽  
Varimax Rotation ◽  
Community Based ◽  
Recreational Facilities ◽  
Fitness Facility ◽  
Components Analysis

This study describes the development of the Fitness Facility Membership Questionnaire of 43 items, designed to identify reasons for joining a fitness facility. Items were generated from responses to an open-ended questionnaire. A 50-item version of the questionnaire was completed by 152 members of five community-based fitness facilities. Principal components analysis with varimax rotation yielded 8 factors, accounting for 63.8% of the variance. The factors were labeled Socialization, Aquatic-related Facilities, Extrinsic Motivation, Recreational Facilities, Intrinsic Motivation, Resistance Equipment, Aerobic Equipment, and Amenities. The internal consistency of the eight factors was acceptable with Cronbach coefficients alpha ranging from .72 to .89. Discriminant analysis of responses is also presented.

scholarly journals Εφαρμογές πολυπαραμετρικών στατιστικών τεχνικών στη χημική ανάλυση

2012 ◽  
Author(s):  
Ελένη Φαρμάκη
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Classification Trees ◽  
Components Analysis ◽  
Artificial Neural

Σε αυτή τη διατριβή μελετήθηκε η εφαρμογή πολυπαραμετρικών τεχνικών σε μεγάλες βάσεις δεδομένων ταξινόμησης, με σκοπό τη θεωρητική τους παρουσίαση, τη σύγκριση αυτών και την εξαγωγή συμπερασμάτων, σχετικά με το πεδίο εφαρμογής τους και το χειρισμό τους, τις δυνατότητες και τους περιορισμούς τους. Χρησιμοποιήθηκαν μη επιβλεπόμενες τεχνικές όπως Principal Components Analysis/Factor Analysis (PCA/FA) και Cluster Analysis (CA) αλλά και επιβλεπόμενες όπως Discriminant Analysis (DA), Classification Trees (CT) και Artificial Neural Networks (ANN). Ιδιαίτερη έμφαση δόθηκε στις τεχνικές CT και ANN (μελετήθηκαν τρεις μέθοδοι και αρχιτεκτονικές αντίστοιχα για καθεμιά από αυτές). Ερευνήθηκαν τα πλεονεκτήματα, μειονεκτήματα και ιδιαιτερότητες τους και βελτιστοποιήθηκαν τα μοντέλα ταξινόμησης των τεχνικών. Όλες οι τεχνικές συγκρίθηκαν μεταξύ τους, με κριτήριο τα αποτελέσματα τους (της ορθής ταξινόμησης των δειγμάτων) σε τρεις βάσεις δεδομένων οι οποίες αφορούσαν τους προσδιορισμούς α) μετάλλων-μεταλλοειδών στους τρεις ταμιευτήρες που χρησιμοποιούνται για την ύδρευση της πρωτεύουσας (Υλίκη, Μόρνο και Μαραθώνα), β) μετάλλων-μεταλλοειδών και ανόργανων στοιχείων σε θαλάσσια δείγματα ιζημάτων από μεγάλες ιχθυοκαλλιέργειες της χώρας, γ) σπανίων γαιών σε δείγματα ελαιολάδων από διάφορες περιοχές. Η DA αν και είναι παραμετρική τεχνική με πολλούς περιορισμούς στην εφαρμογή της, ανταποκρίθηκε στις ανάγκες των προβλημάτων και παρείχε πάντα μια πρώτη άποψη για το πρόβλημα (δυνατότητα ή όχι γραμμικού διαχωρισμού των ομάδων με βάση το Canonical plot της ανάλυσης και αρχική αξιολόγηση των μεταβλητών). Τα ποσοστά ορθής ταξινόμησης που παρείχε ήταν αρκετές φορές συγκρίσιμα με των πιο προηγμένων τεχνικών. Τα CT με 3 διαφορετικές μεθόδους και αρκετή ευελιξία (παρείχαν πολλές παραμέτρους προς δοκιμή και βελτιστοποίηση), επέτυχαν υψηλά ποσοστά ταξινόμησης με λίγες ή πολλές μεταβλητές (περισσότερες συνήθως των ANN), κατασκευάζοντας επαναλήψιμα μοντέλα με δυνατότητες γενίκευσης. Τα ANN αποδείχθηκαν ιδιαίτερα ευέλικτη τεχνική, με δυνατότητες αποτελεσματικής αξιολόγησης των μεταβλητών και εφαρμογής τους σε απλές αλλά και πολυπλοκότερες βάσεις προσεγγίζοντας γραμμικές και μη γραμμικές συναρτήσεις. Κατασκευάστηκαν ανθεκτικά και ευέλικτα μοντέλα. Μειονέκτημά τους αποτέλεσαν ωστόσο, τα φαινόμενα υπερπροσαρμογής που παρουσιάζουν και χρειάστηκαν προσεκτικοί χειρισμοί για την αποφυγή τους. Έτσι, τα διαθέσιμα δείγματα διαχωρίστηκαν σε τρεις ομάδες: χρησιμοποιήθηκαν εκτός της συνήθους ομάδας εκπαίδευσης, επιπλέον ομάδες επικύρωσης και ελέγχου. Με τον τρόπο αυτό, έγινε άμεση ταυτοποίηση των φαινομένων υπερπροσαρμογής (ώστε να διακόπτεται αυτόματα η εκπαίδευση του μοντέλου), αλλά και δοκιμή των μοντέλων σε νέα, “’άγνωστα” δείγματα, ώστε να ελέγχεται η δυνατότητα γενίκευσης αυτών. Ο διαχωρισμός σε ομάδες έγινε είτε τυχαία (όπως επιτάσσει η σύγχρονη βιβλιογραφία), είτε με βάση της προκατεργασίας με DA (μέθοδος που δεν έχει χρησιμοποιηθεί ποτέ στο παρελθόν). Επιπλέον, έγινε προσπάθεια εφαρμογής όσο το δυνατόν απλούστερων δομών με λίγες παραμέτρους (μεταβλητές, βάρη) αλλά και λειτουργικές μονάδες επεξεργασίας (νευρώνες).

scholarly journals Morphometric variability in populations of Palaemonetes spp. (Crustacea, Decapoda, Palaemonidae) from the Peruvian and Brazilian Amazon Basin

2005 ◽  
Vol 95 (3) ◽  
pp. 327-334 ◽  
Author(s):  
Carmen Rosa García-Dávila ◽  
Célio Magalhães ◽  
José Camilo Hurtado Guerrero
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Amazon Basin ◽  
Function Analysis ◽  
Rank Correlation ◽  
Bivariate Analysis ◽  
Morphometric Characters ◽  
Intrapopulation Variation ◽  
Components Analysis

Morphometric variability among shrimp populations of the genus Palaemonetes Heller, 1869 from seven lakes (Huanayo and Urcococha, in Peru; Amanã, Mamirauá, Camaleão, Cristalino e Iruçanga, in Brasil) in the Amazon Basin, presumably belonging to Palaemonetes carteri Gordon, 1935 and Palaemonetes ivonicus Holthuis, 1950, were studied. The morphometric studies were carried out from the ratios obtained from the morphometric characters. Multivariated analysis (Principal Components Analysis-PCA, Discriminant Function Analysis and Cluster Analysis) were applied over the ratios. Intra- and interpopulation variations of the rostrum teeth, and the number of spines in the male appendix, were analyzed through descriptive statistics and bivariate analysis (Spearman Rank Correlation test). Results indicated a wide plasticity and overlapping in the studied ratios between populations. The Principal Components Analysis was not able to separate different populations, revealing a large intrapopulation plasticity and strong interpopulation similarity in the studied ratios. Although the Discriminant Functions Analysis was not able to fully discriminate populations, they could be allocated in three subgroups: 1) Cristalino and Iruçanga; 2) Huanayo, Urcococha and Camaleão and 3) Mamirauá and Amanã. The first two groups were morphometrically separated from each other, whereas the third one presented a strong overlap with the former two. The Cluster Analysis confirmed the first two subgroups separation, and indicated that the first and third groups were closely related. Rostrum teeth and number of spines in the appendix masculina showed a large intrapopulation variation and a strong overlapping among the studied populations, regardless of the species.

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