A survey of epicuticular waxes among genera of Triticeae. III. Synthesis and conclusion

1982 ◽  
Vol 60 (9) ◽  
pp. 1761-1770 ◽  
Author(s):  
Bernard R. Baum ◽  
A. P. Tulloch

Characteristics of ultrastructural morphology and chemical composition of epicuticular waxes on glumes of Triticeae were combined for two series of numerical taxonomic analyses. The first, incorporating within-genus variability, utilized frequencies and information radius. The information radius matrix was subjected to Jardine–Sibson Bk clustering, then transformed to Euclidean distances for distance Wagner and principal-coordinate analyses. The second series employed a table of average character values for each genus which was subjected to four ordinations: (i) principal-component analysis of the correlation matrix, (ii) principal-component analysis of the variance-covariance matrix, (iii) principal-coordinate analysis, and (iv) nonmetric multidimensional scaling. The results are compared and general inferences are drawn. Occurrence of wax filaments on the glumes was highly correlated with presence of appreciable amounts of β-diketones in wax from the whole plant. While some genera, such as Triticum and Aegilops, appeared less closely related than expected from classification based on morphology, this procedure has suggested relationships between other genera, such as Roegneria and Hordeum and Secale and Elymus. The genera Leymus, Elymus, and Aneurolepidium were also closely related to each other and more distantly to Elytrigia, Triticum, and Agropyron. A relatively close relationship was also shown between the seven genera, Crithopsis, Eremopyron, Heteranthelium, Hordelymus, Psathyrostachys, Sitanion, and Taeniatherum, which have waxes which do not contain any β-diketones.

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1330
Author(s):  
Panagiotis K. Gkonis ◽  
Panagiotis T. Trakadas ◽  
Lambros E. Sarakis

The goal of the study presented in this paper is to evaluate the performance of a proposed transmission scheme in multiuser multiple-input multiple-output (MIMO) configurations, via code reuse. Hence, non-orthogonal multiple access (NOMA) is performed. To this end, a correlation matrix of the received data is constructed at the transmitter, with feedback as only the primary eigenvector of the equivalent channel matrix, which is derived after principal component analysis (PCA) at the receiver. Afterwards, users experiencing improved channel quality (i.e., diagonal terms of the correlation matrix) along with reduced multiple access interference (i.e., the inner product of transmission vectors) are the potential candidates for their assigned code to be reused. As the results indicate, considering various MIMO configurations, the proposed approach can achieve almost 33% code assignment gain (CAG), when successive interference cancellation (SIC) is employed in mobile receivers. However, even in the absence of SIC, CAG is still maintained with a tolerable average bit error rate (BER) degradation.


2020 ◽  
Author(s):  
Florian Privé

AbstractHere we propose a simple, robust and effective method for global ancestry inference and grouping from Principal Component Analysis (PCA) of genetic data. The proposed approach is particularly useful for methods that need to be applied in homogeneous samples. First, we show that Euclidean distances in the PCA space are proportional to FST between populations. Then, we show how to use this PCA-based distance to infer ancestry in the UK Biobank and the POPRES datasets. We propose two solutions, either relying on projection of PCs to reference populations such as from the 1000 Genomes Project, or by directly using the internal data. Finally, we conclude that our method and the community would benefit from having an easy access to a reference dataset with an even better coverage of the worldwide genetic diversity than the 1000 Genomes Project.


2001 ◽  
Vol 33 ◽  
pp. 133-138 ◽  
Author(s):  
Yunhe Zhao ◽  
Antony K. Liu

AbstractThe two-dimensional wavelet transform is a highly efficient band-pass filter, which can be used to track features in satellite images from sequential paths. Wavelet analysis of NASA scatterometer and Special Sensor Microwave/Imager data has been used to obtain daily sea-ice drift information for the Arctic region. Comparison with ice motion derived from ocean buoys shows good quantitive agreement. Furthermore, the scatterometer results definitely complement passive-microwave radiometer results when there are cloud or surface effects. This outcome allows three sets of sea-ice-drift daily results from scatterometer, radiometer and buoy data to be merged as a composite map by data-fusion techniques. Based on the composite maps, the ice-flow streamlines are highly correlated with surface air-pressure contours. In order to quantify the wind effects on ice motion, empirical orthogonal functions are used in the principal-component analysis to isolate generalized patterns inherent in 6 months (fall/winter) of daily sea-ice motion data. It is found that 30% of sea-ice motion is highly correlated with 50% of the pressure field in modes 1 and 2. For the higher modes, sea-ice motion is also affected by ocean current, bathymetry and coastal boundary and therefore is not highly correlated with the wind field.


Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4094
Author(s):  
Carlos Martin-Barreiro ◽  
John A. Ramirez-Figueroa ◽  
Xavier Cabezas ◽  
Víctor Leiva ◽  
M. Purificación Galindo-Villardón

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.


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