Components Analysis
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2021 ◽  
Ayoni Ogunbayo ◽  
Moussa Sié ◽  
Glenn B. Gregorio ◽  
David Kolawole Ojo ◽  
Kayode Abiola Sanni ◽  

Abstract Rice is staple food in many countries of Africa and a major part of the diet in many others. However, Africa’s demand for rice exceeds production with the deficit of 40% being imported. One way to improve Africa’s rice production is through breeding high yielding varieties suitable for the different environment conditions. This study was conducted to assess the genetic variability and stability performance of 48 lowland rice genotypes including 37 interspecific (Oryza glaberrima × Oryza sativa ssp. indica) and 11 intraspecific (O. sativa ssp. indica × O. sativa ssp. indica) in 12 environments in Nigeria, Benin Republic and Togo using Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype+ Genotype x Environment (GGE) biplot models. The combined analysis of variance revealed significant differences (P<0.01) among the genotypes, environments, and genotypes x environment interaction. Both the AMMI and GGE models identified NERICA-L8 and NERICA-LI2 as the best genotypes for cultivation across environments. Ouedeme environments in Benin Republic were the most stable and ideal for rice cultivation, while Ibadan sites were the most unstable. TOG 5681 had the least yield and was the most unstable across seasons. Genetic diversity was analyzed using 22 important morpho-agronomic traits and 50 simple sequence repeat (SSR) markers and the results were subjected to principal components analysis (PCA). The results revealed that the first eight PC axes (PC1–8) accounted for 75.13% of the total variation, while PC1–4 accounted for 50.39% of the total variation among rice genotypes. However, 10 of the 50 SSR markers were polymorphic and generated 49 alleles (average = 4.9 alleles per locus), suggesting moderate to substantial genetic diversity among the rice genotypes. The polymorphic information content (PIC) ranged from 0.24 to 0.65, with an average PIC value of 0.45. Two structured populations were observed which clustered into five heterotic groups and an outgroup, respectively. This suggests that heterosis could be exploited in the next hybridization program by crossing one of the genotypes in any SSR marker-defined cluster, with the rice accession TOG 5681 in cluster I. The results of this study suggest that morpho-agronomic traits should be used to compliment SSR data in rice diversity studies, especially if a few polymorphic SSR markers are to be used.

2021 ◽  
Anna Elisabeth Fürtjes ◽  
Ryan Arathimos ◽  
Jonathan RI Coleman ◽  
James H Cole ◽  
Simon R Cox ◽  

The human brain is organised into networks of interconnected regions that have highly correlated volumes. In this study, we aim to triangulate insights into brain organisation and its relationship with cognitive ability and ageing, by analysing genetic data. We estimated general genetic dimensions of human brain morphometry within the whole brain, and nine predefined canonical brain networks of interest. We did so based on principal components analysis (PCA) of genetic correlations among grey-matter volumes for 83 cortical and subcortical regions (Nparticipants = 36,778). We found that the corresponding general dimension of brain morphometry accounts for 40% of the genetic variance in the individual brain regions across the whole brain, and 47-65% within each network of interest. This genetic correlation structure of regional brain morphometry closely resembled the phenotypic correlation structure of the same regions. Applying a novel multivariate methodology for calculating SNP effects for each of the general dimensions identified, we find that general genetic dimensions of morphometry within networks are negatively associated with brain age (rg = -0.34) and profiles characteristic of age-related neurodegeneration, as indexed by cross-sectional age-volume correlations (r = -0.27). The same genetic dimensions were positively associated with a genetic general factor of cognitive ability (rg = 0.17-0.21 for different networks). We have provided a statistical framework to index general dimensions of shared genetic morphometry that vary between brain networks, and report evidence for a shared biological basis underlying brain morphometry, cognitive ability, and brain ageing, that are underpinned by general genetic factors.

2021 ◽  
Vol 13 (21) ◽  
pp. 4241
Ying Tu ◽  
Bin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
Miao Li ◽  

Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.

Stavros Kyriakidis ◽  
Matthew Stevens ◽  
Kristina Karstad ◽  
Karen Søgaard ◽  
Andreas Holtermann

The purpose of our study was to investigate which organizational levels and factors determine the number of resident handlings in eldercare. We conducted a multi-level study, stratified on day and evening shifts, including information on four levels: nursing homes (n = 20), wards within nursing homes (day, n = 120; evening, n = 107), eldercare workers within wards (day, n = 619; evening, n = 382), and within eldercare workers (i.e., days within eldercare workers; day, n = 5572; evening, n = 2373). We evaluated the influence of each level on the number of resident handlings using variance components analysis and multivariate generalized linear mixed models. All four levels contributed to the total variance in resident handlings during day and evening shifts, with 13%/20% at “nursing homes”, 21%/33% at “wards within nursing homes”, 25%/31% at “elder-care workers within wards”, and 41%/16% “within eldercare workers”, respectively. The percentage of residents with a higher need for physical assistance, number of residents per shift, occupational position (only within day shifts), and working hours per week (only within day shifts) were significantly associated with the number of resident handlings performed per shift. Interventions aiming to modify number of resident handlings in eldercare ought to target all levels of the eldercare organization.

Children ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 934
Miroslav Králík ◽  
Ondřej Klíma ◽  
Martin Čuta ◽  
Robert M. Malina ◽  
Sławomir M. Kozieł ◽  

A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requires the interpolation of the interval between points and often an extrapolation of the growth trajectory beyond the range of empirical limits (prediction). This study tested a new approach for fitting a relatively limited number of longitudinal data using the normal variation of human empirical growth curves. First, functional principal components analysis was done for curve phase and amplitude using complete and dense data sets for a reference sample (Brno Growth Study). Subsequently, artificial curves were generated with a combination of 12 of the principal components and applied for fitting to the newly analyzed data with the Levenberg–Marquardt optimization algorithm. The approach was tested on seven 5-points/year longitudinal data samples of adolescents extracted from the reference sample. The samples differed in their distance from the mean age at peak velocity for the sample and were tested by a permutation leave-one-out approach. The results indicated the potential of this method for growth modeling as a user-friendly application for practical applications in pediatrics, auxology and youth sport.

2021 ◽  
Vol 1 ◽  
Douglas N. Rutledge ◽  
Jean-Michel Roger ◽  
Matthieu Lesnoff

A tricky aspect in the use of all multivariate analysis methods is the choice of the number of Latent Variables to use in the model, whether in the case of exploratory methods such as Principal Components Analysis (PCA) or predictive methods such as Principal Components Regression (PCR), Partial Least Squares regression (PLS). For exploratory methods, we want to know which Latent Variables deserve to be selected for interpretation and which contain only noise. For predictive methods, we want to ensure that we include all the variability of interest for the prediction, without introducing variability that would lead to a reduction in the quality of the predictions for samples other than those used to create the multivariate model.

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6776
Radosław Miśkiewicz ◽  
Agnieszka Rzepka ◽  
Ryszard Borowiecki ◽  
Zbigniew Olesińki

The rapid development of innovations in the industry 4.0 era led to new or evolved companies. At the same time, the accepted concept of carbon-free development requires building a new philosophy for the company’s management. The paper aims to analyse the key attributes of teal organisations (as a new type of a company) from the energy sector (as a core sector for carbon-free transformation). The paper summarises the core features of teal organisations and their attributes. In the paper, three hypotheses are tested: innovations and technologies are the most used attributes among teal organisations from the energy sector; organisational and corporate culture are the least used attributes among teal organisations from the energy sector; in the energy sector, the companies that have the attributes of teal organisations primarily work in countries with a high level of innovation and information technologies (as a core indicator of Industry 4.0) and economic development. For testing the hypotheses, the following methods are applied: a Friedman test, a paired-samples t-test, the principal components analysis, a correlation analysis, an ANOVA test (analysis of variance), and a regression analysis. The online survey generates the data for analysis. The object of the research is the workers from the energy sector companies from five countries (Poland, Ukraine, Georgia, Slovakia, and Romania). The findings of the statistical analysis confirm the first and second hypotheses. Companies in the energy sector mostly use innovations and technologies as the attributes of teal organisations. The regression analysis results show that an increase of 1% of patent applications leads to an increased energy efficiency of 1.29%. Additionally, the implemented features of teal organisations in the energy sector allow for improving the country’s energy efficiency, which, as a consequence, then boosts carbon-free development.

2021 ◽  
Vol 17 (1) ◽  
pp. 152-160
Maria Batsikoura ◽  
Sofia Zyga ◽  
Foteini Tzavella ◽  
Athanasios Sachlas ◽  
Andrea Paola Rojas Gil

Aim: The aim of this study was to investigate the relationship between nutritional habits, lifestyle, anxiety, and coping strategies. Background: Anxiety is an underestimated and often undiagnosed subclinical disorder that burdens the general public of modern societies and increases illness suscentibility. Methods: The study group consisted of 693 individuals living in Peloponnese, Greece. A standardized questionnaire that consists of the dietary habits and lifestyle questionnaire, the trait Anxiety STAI-X-2 questionnaire and the brief-COPE questionnaire, was used. Principal components analysis identified the factors from the questionnaires, and stepwise multivariate regression analysis investigated their relationships. Results: Weekly consumption of fruits, tomatoes, salads and lettuce, together with Εmotional/Ιnstrumental support, Denial/Behavioural disengagement, substance use and self-blame, was the most important predictors of anxiety scores. Positive reframing/Humour and Acceptance/Planning are also associated with the Positive STAI factor and decreased anxiety scores. Conclusion: Healthy nutritional habits, comprised of consumption of salads and fruits, together with adaptive coping strategies, such as Positive reframing/Humour and Active problem solving, may provide the most profound improvement in the anxiety levels of a healthy population in Peloponnese, Greece.

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12131
Samuel Adingo ◽  
Jie-Ru Yu ◽  
Liu Xuelu ◽  
Xiaodan Li ◽  
Sun Jing ◽  

Soil microbial carbon utilization efficiency (CUE) is the efficiency with which microorganisms convert absorbed carbon (C) into their own biomass C, also referred to as microorganism growth efficiency. Soil microbial CUE is a critical physiological and ecological parameter in the ecosystem’s C cycle, influencing the processes of C retention, turnover, soil mineralization, and greenhouse gas emission. Understanding the variation of soil microbial CUE and its influence mechanism in the context of global environmental change is critical for a better understanding of the ecosystem’s C cycle process and its response to global changes. In this review, the definition of CUE and its measurement methods are reviewed, and the research progress of soil microbial CUE variation and influencing factors is primarily reviewed and analyzed. Soil microbial CUE is usually expressed as the ratio of microbial growth and absorption, which is divided into methods based on the microbial growth rate, microbial biomass, substrate absorption rate, and substrate concentration change, and varies from 0.2 to 0.8. Thermodynamics, ecological environmental factors, substrate nutrient quality and availability, stoichiometric balance, and microbial community composition all influence this variation. In the future, soil microbial CUE research should focus on quantitative analysis of trace metabolic components, analysis of the regulation mechanism of biological-environmental interactions, and optimization of the carbon cycle model of microorganisms’ dynamic physiological response process.

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1619
Jennifer Irving McGrath ◽  
Wengang Zhang ◽  
Regina Hollar ◽  
Alison Collings ◽  
Roger Powell ◽  

The domestic cat is one of the most popular pets in the world. It is estimated that 89–92% of domestic cats in the UK are non-pedigree Domestic shorthair (DSH), Domestic longhair (DLH), or Domestic semi-longhair cats (DSLH). Despite their popularity, little is known of the UK non-pedigree cats’ population structure and breeding dynamics. Using a custom designed single nucleotide variant (SNV) array, this study investigated the population genetics of 1344 UK cats. Principal components analysis (PCA) and fastSTRUCTURE analysis verified that the UK’s DSH, DLH, and DSLH cats are random-bred, rather than admixed, mix breed, or crossbred. In contrast to pedigree cats, the linkage disequilibrium of these random-bred cats was least extensive and decayed rapidly. Homozygosity by descent (HBD) analysis showed the majority of non-pedigree cats had proportionally less of their genome in HBD segments compared to pedigree cats, and that these segments were older. Together, these findings suggest that the DSH, DLH, and DSLH cats should be considered as a population of random-bred cats rather than a crossbred or pedigree-admixed cat. Unexpectedly, 19% of random-bred cat genomes displayed a higher proportion of HBD segments associated with more recent inbreeding events. Therefore, while non-pedigree cats as a whole are genetically diverse, they are not impervious to inbreeding and its health risks.

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