scholarly journals Use of Principal Component Analysis to Combine Genetic Merit for Heat Stress and for Fat and Protein Yield in Spanish Autochthonous Dairy Goat Breeds

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 736
Author(s):  
Alberto Menéndez-Buxadera ◽  
Eva Muñoz-Mejías ◽  
Manuel Sánchez ◽  
Juan Manuel Serradilla ◽  
Antonio Molina
Keyword(s):  
Principal Component Analysis ◽  
Heat Stress ◽  
Genetic Variability ◽  
Negative Impact ◽  
Genetic Correlations ◽  
Principal Component ◽  
Component Analysis ◽  
Dairy Goat ◽  
Environment Interaction ◽  
Dairy Goats

We studied the effect of the Temperature Humidity Index (THI) (i.e., the average of temperature and relative humidity registered at meteorological stations) closest to the farms taken during the test day (TD), for total daily protein and fat yields (fpy) of the three main Spanish dairy goats. The data were from Florida (11,244 animals and 126,825 TD), Malagueña (12,215 animals and 141,856 TD) and Murciano Granadina (5162 animals and 62,834 TD) breeding programs and were studied by different linear models to estimate the nature of the fpy response throughout the THI and the weeks of lactation (Days in Milk, DIM) trajectories. The results showed an antagonism between THI and DIM, with a marked depression in the fpy level in animals kept in the hot zone of the THI values (THI > 25) compared with those in the cold zone (THI ≤ 16), with a negative impact equivalent to production of 13 to 30 days. We used a Reaction Norm model (RN), including THI and DIM as fixed covariates and a Test Day Model (TDM), to estimate the genetic (co)variance components. The heritability and genetic correlations estimated with RN and TDM showed a decreased pattern along the scale of THI and DIM, with slight differences between breeds, meaning that there was significant genetic variability in the animal’s ability to react to different levels of THI, which is not constant throughout the DIM, showing the existence of genotype-environment interaction. The breeding values (BV) of all animals for each level of THI and DIM were subject to a principal component analysis, and the results showed that 89 to 98% of the variance between the BV was explained by the two first eigenvalues. The standardized BV were weighted with the corresponding eigenvector coefficients to construct an index that showed, in a single indicator, the most complete expression of the existing genetic variability in the animals’ ability to produce fpy along the trajectories of THI and DIM. This new option will make it easier to select animals which are more productive, and with better adaptability to heat stress, as well as enabling us to exploit genetic variations in the form of the response to heat stress to be adapted to different production systems.

scholarly journals Assessment of Morphometric Diversity for Yield and Yield Attributing Traits in Rice (Oryza sativa L.) for Tolerance to Heat Stress

2020 ◽  
pp. 29-49
Author(s):  
S. Sandeep ◽  
M. Sujatha ◽  
L. V. Subbarao ◽  
C. N. Neeraja
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Heat Stress ◽  
Oryza Sativa L ◽  
Pollen Viability ◽  
Principal Component ◽  
Component Analysis ◽  
Spikelet Fertility ◽  
Breeding Programmes ◽  
Heat Tolerant

The present investigation entitled “Assessment of morphometric diversity for yield and yield attributing traits in rice (Oryza sativa L.) for tolerance to heat stress” was carried out with objective of assessing genetic divergence in 200 germplasm of rice for eleven characters at ICRISAT, Patencheru, Hyderabad. The genotypes were grouped into fifteen clusters in Tocher’s method, cluster analysis and principal component analysis, out of the 11 characters studied, number of grains per panicle, plant height, pollen viability and spikelet fertility contributed 96.73 per cent of the total divergence and these traits were found to be important potent factors for genetic differentiation in genotypes. Principal component analysis identified five principal components, which contributed for 78.66 percent % of cumulative variance. The overall results of the study revealed that crossing using the genotypes under cluster V and XI and cluster XI and XIII could be exploited by hybridization programme to yield good recombinants because they had maximum inter cluster distance and possessing high genetic diversity for the characters viz. panicle length, number of grains per panicle and single plant yield. The genotypes of cluster I, II, IV, VI, VII, VIII, XI, XII and XIII showed high spikelet fertility percentage. Hence the genotypes of these clusters can be used in breeding programmes for development of heat tolerant varieties. Euclidean2 method indicated that genotypes of cluster III and IX exhibited high spikelet fertility percentage which can be utilized in development of heat tolerant cultivars. The results of principal component analysis revealed that genotypes of cluster I, cluster IV, cluster V, cluster VIII, cluster IX, cluster XI, cluster XII and cluster XV exhibited highest spikelet fertility percentage. Hence, the genotypes of the clusters can be used in breeding programmes for the development of heat tolerant varieties. 

scholarly journals HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS

2018 ◽  
Vol XLII-3 ◽  
pp. 789-794 ◽  
Author(s):  
D. Li ◽  
L. Xu ◽  
J. Peng ◽  
J. Ma
Keyword(s):  
Principal Component Analysis ◽  
Spatial Structure ◽  
Hyperspectral Image ◽  
Negative Impact ◽  
Principal Component ◽  
Component Analysis ◽  
Research Area ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Nonlocal Similarity

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

scholarly journals Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Debabrata Samanta ◽  
M. P. Karthikeyan ◽  
Marimuthu Karuppiah ◽  
Dalima Parwani ◽  
Manish Maheshwari ◽  
...  
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Negative Impact ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Classification Model ◽  
Newborn Jaundice

One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

Genetic parameter estimates and principal component analysis of breeding values of reproduction and growth traits in female Canchim cattle

2013 ◽  
Vol 25 (5) ◽  
pp. 775 ◽  
Author(s):  
M. E. Buzanskas ◽  
R. P. Savegnago ◽  
D. A. Grossi ◽  
G. C. Venturini ◽  
S. A. Queiroz ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Mixed Model ◽  
Growth Traits ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Principal Component ◽  
Component Analysis ◽  
Parameter Estimates ◽  
Breeding Values ◽  
Heritability Estimates

Phenotypic data from female Canchim beef cattle were used to obtain estimates of genetic parameters for reproduction and growth traits using a linear animal mixed model. In addition, relationships among animal estimated breeding values (EBVs) for these traits were explored using principal component analysis. The traits studied in female Canchim cattle were age at first calving (AFC), age at second calving (ASC), calving interval (CI), and bodyweight at 420 days of age (BW420). The heritability estimates for AFC, ASC, CI and BW420 were 0.03 ± 0.01, 0.07 ± 0.01, 0.06 ± 0.02, and 0.24 ± 0.02, respectively. The genetic correlations for AFC with ASC, AFC with CI, AFC with BW420, ASC with CI, ASC with BW420, and CI with BW420 were 0.87 ± 0.07, 0.23 ± 0.02, –0.15 ± 0.01, 0.67 ± 0.13, –0.07 ± 0.13, and 0.02 ± 0.14, respectively. Standardised EBVs for AFC, ASC and CI exhibited a high association with the first principal component, whereas the standardised EBV for BW420 was closely associated with the second principal component. The heritability estimates for AFC, ASC and CI suggest that these traits would respond slowly to selection. However, selection response could be enhanced by constructing selection indices based on the principal components.

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