Relationship Matrix Research Articles

Output Regularization With Cluster-Based Soft Targets.

While supervised learning of over-parameterized neural networks achieved state-of-the-art performance in image classification, it tends to over-fit the labeled training samples to give inferior generalization ability. Output regularization deals with over-fitting by using soft targets as additional training signals. Although clustering is one of the most fundamental data analysis tools for discovering general-purpose and data-driven structures, it has been ignored in existing output regularization approaches. In this article, we leverage this underlying structural information by proposing Cluster-based soft targets for Output Regularization (CluOReg). This approach provides a unified way for simultaneous clustering in embedding space and neural classifier training with cluster-based soft targets via output regularization. By explicitly calculating a class relationship matrix in the cluster space, we obtain classwise soft targets shared by all samples in each class. Results of image classification experiments under various settings on a number of benchmark datasets are provided. Without resorting to external models or designed data augmentation, we get consistent and significant reductions in classification error compared with other approaches, demonstrating that cluster-based soft targets effectively complement the ground-truth label.

Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation System

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of root mean squared error (RMSE), mean absolute error (MAE) and normalized discounted cumulative gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets.

Assessing different metrics of pedigree and genomic inbreeding and inbreeding effect on growth, fertility, and feed efficiency traits in a closed-herd Nellore cattle population

BackgroundThe selection of individuals based on their predicted breeding values and mating of related individuals can increase the proportion of identical-by-descent alleles. In this context, the objectives of this study were to estimate inbreeding coefficients based on alternative metrics and data sources such as pedigree (FPED), hybrid genomic relationship matrix H (FH), and ROH of different length (FROH); and calculate Pearson correlations between the different metrics in a closed Nellore cattle population selected for body weight adjusted to 378 days of age (W378). In addition to total FROH (all classes) coefficients were also estimated based on the size class of the ROH segments: FROH1 (1–2 Mb), FROH2 (2–4 Mb), FROH3 (4–8 Mb), FROH4 (8–16 Mb), and FROH5 (> 16 Mb), and for each chromosome (FROH_CHR). Furthermore, we assessed the effect of each inbreeding metric on birth weight (BW), body weights adjusted to 210 (W210) and W378, scrotal circumference (SC), and residual feed intake (RFI). We also evaluated the chromosome-specific effects of inbreeding on growth traits.ResultsThe correlation between FPED and FROH was 0.60 while between FH and FROH and FH and FPED were 0.69 and 0.61, respectively. The annual rate of inbreeding was 0.16% for FPED, 0.02% for FH, and 0.16% for FROH. A 1% increase in FROH5 resulted in a reduction of up to -1.327 ± 0.495 kg in W210 and W378. Four inbreeding coefficients (FPED, FH, FROH2, and FROH5) had a significant effect on W378, with reductions of up to -3.810 ± 1.753 kg per 1% increase in FROH2. There was an unfavorable effect of FPED on RFI (0.01 ± 0.0002 kg dry matter/day) and of FROH on SC (-0.056 ± 0.022 cm). The FROH_CHR coefficients calculated for BTA3, BTA5, and BTA8 significantly affected the growth traits.ConclusionsInbreeding depression was observed for all traits evaluated. However, these effects were greater for the criterion used for selection of the animals (i.e., W378). The increase in the genomic inbreeding was associated with a higher inbreeding depression on the traits evaluated when compared to pedigree-based inbreeding. Genomic information should be used as a tool during mating to optimize control of inbreeding and, consequently, minimize inbreeding depression in Nellore cattle.

Extra-pair paternity in two passerine birds breeding in a gradient of urbanisation.

Urbanisation has been increasing worldwide in recent decades, driving environmental change and exerting novel selective pressures on wildlife. Phenotypic differences between urban and rural individuals have been widely documented in several taxa. However, the extent to which urbanisation impacts mating strategies is less known. Here, we investigated extra-pair paternity variation in great tits (Parus major) and blue tits (Cyanistes caeruleus) breeding in nestboxes set in a gradient of urbanisation in Warsaw, Poland, over three breeding seasons. Urbanisation was quantified as the amount of light pollution, noise pollution, impervious surface area (ISA) and tree cover within a 100-m radius around each nestbox. We obtained genotypes for 1213 great tits at 7344 SNP markers and for 1299 blue tits at 9366 SNP markers with a genotyping-by-sequencing method, and inferred extra-pair paternity by computing a genomewide relatedness matrix. We report higher extra-pair paternity in blue tits breeding in more urbanised areas, for example, with higher light pollution and ISA, and lower tree cover. However, no such trend was found in great tits. Late-stage survival of individual nestlings in both species was not associated with paternity or urbanisation proxies, thus we were not able to detect fitness benefits or drawbacks of being an extra-pair offspring in relation to urbanisation. Our results contribute to the growing body of knowledge reporting on the effects of urbanisation on avian ecology and behaviour, and confirm species-specific and population-specific patterns of extra-pair paternity variation.

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

Genetic diversity analysis of Inner Mongolia cashmere goats (Erlangshan subtype) based on whole genome re-sequencing

BackgroundInner Mongolia cashmere goat (IMCG), renowned for its superior cashmere quality, is a Chinese indigenous goat breed that has been developed through natural and artificial selection over a long period. However, recently, the genetic resources of IMCGs have been significantly threatened by the introduction of cosmopolitan goat breeds and the absence of adequate breed protection systems.ResultsIn order to assess the conservation effectiveness of IMCGs and efficiently preserve and utilize the purebred germplasm resources, this study analyzed the genetic diversity, kinship, family structure, and inbreeding of IMCGs utilizing resequencing data from 225 randomly selected individuals analyzed using the Plink (v.1.90), GCTA (v.1.94.1), and R (v.4.2.1) software. A total of 12,700,178 high-quality SNPs were selected through quality control from 34,248,064 SNP sites obtained from 225 individuals. The average minor allele frequency (MAF), polymorphic information content (PIC), and Shannon information index (SHI) were 0.253, 0.284, and 0.530, respectively. The average observed heterozygosity (Ho) and the average expected heterozygosity (He) were 0.355 and 0.351, respectively. The analysis of the identity by state distance matrix and genomic relationship matrix has shown that most individuals’ genetic distance and genetic relationship are far away, and the inbreeding coefficient is low. The family structure analysis identified 10 families among the 23 rams. A total of 14,109 runs of homozygosity (ROH) were identified in the 225 individuals, with an average ROH length of 1014.547 kb. The average inbreeding coefficient, calculated from ROH, was 0.026 for the overall population and 0.027 specifically among the 23 rams, indicating a low level of inbreeding within the conserved population.ConclusionsThe IMCGs exhibited moderate polymorphism and a low level of kinship with inbreeding occurring among a limited number of individuals. Simultaneously, it is necessary to prevent the loss of bloodline to guarantee the perpetuation of the IMCGs’ germplasm resources.

Identification and dynamic evolution of land use conflict potentials in China, 2000–2020

Under the background of rapid urbanization, a series of urgent problems have emerged in China, such as tightening resource constraints and intensifying land use conflicts (LUCs). Based on the perspective of agricultural production – residents’ life – ecological security, this study took China as the study area, applied the multi-criteria evaluation analysis method to diagnose the LUC type based on spatial statistics, coupling relationship matrix, comprehensive conflict index, and spatial autocorrelation model, then divided the LUC risk changes into seven patterns, and analyzed their spatial evolution characteristics. The results demonstrated that the scale of competition for scarce land between agriculture, construction, and ecology increased by 3.4 % over the last 20-year period. High conflict zone was mainly located in the eastern plains, which was also the main distribution zone of agriculture-construction conflict, while the agriculture-ecology conflict zone was mainly located in the west. Over time, LUC risk grade changed in 14.3 % of the study area, with the largest area of transitions from no conflict to moderate conflict (34.78 %). High intensification zone coincided almost entirely with ecologically fragile regions of the Midwest, underscoring the critical importance of LUC perspectives in ecological conservation. We provided a new perspective for the study of LUC changes and a scientific reference for realizing sustainable land use management in China and other regions.

Modeling soybean growth: A mixed model approach.

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.

A moderated mediation model of the relationship between Indian elementary school teachers' autonomy and perceived ease of ICT use

AbstractIn the 21st century, Information and Communication Technology (ICT) is a critical component of innovative teaching and learning. This study looked at the impact of teachers' autonomy on their perceived ease of using ICT and the intricate relationship between the two, involving their self‐efficacy, job satisfaction, and perceived incentives to change. Using a descriptive survey approach, 311 (134 female) Indian elementary school teachers participated in the study and provided self‐reported data. A structural equation model was employed to test the mediating roles of self‐efficacy and job satisfaction, and the moderation of perceived incentives. The results affirmed that teacher autonomy had a direct and moderately positive effect on their perceived ease of ICT use. Self‐efficacy and job satisfaction significantly and partially mediated the indirect relationships between autonomy and ease of ICT use. In the three relationship paths the perceived incentive to change had a significantly positive moderation to catalyze the relationships. Considering Indian elementary school teachers' glaring lack of autonomy, this paper suggests a policy shift involving greater teacher autonomy and the use of incentives for improved efficacy, job satisfaction, and ICT use. The relationship matrix will serve as a reference for researchers and practitioners to gain a deeper understanding of the role of teacher autonomy in addressing the global issue of limited adoption and integration of ICT by school teachers.

Identification of consensus homozygous regions and their associations with growth and feed efficiency traits in American mink

The recent chromosome-based genome assembly and the newly developed 70K single nucleotide polymorphism (SNP) array for American mink (Neogale vison) facilitate the identification of genetic variants underlying complex traits in this species. The objective of this study was to evaluate the association between consensus runs of homozygosity (ROH) with growth and feed efficiency traits in American mink. A subsample of two mink populations (n = 2,986) were genotyped using the Affymetrix Mink 70K SNP array. The identified ROH segments were included simultaneously, concatenated into consensus regions, and the ROH-based association studies were carried out with linear mixed models considering a genomic relationship matrix for 11 growth and feed efficiency traits implemented in ASReml-R version 4. In total, 298,313 ROH were identified across all individuals, with an average length and coverage of 4.16 Mb and 414.8 Mb, respectively. After merging ROH segments, 196 consensus ROH regions were detected and used for genome-wide ROH-based association analysis. Thirteen consensus ROH regions were significantly (P < 0.01) associated with growth and feed efficiency traits. Several candidate genes within the significant regions are known for their involvement in growth and body size development, including MEF2A, ADAMTS17, POU3F2, and TYRO3. In addition, we found ten consensus ROH regions, defined as ROH islands, with frequencies over 80% of the population. These islands harbored 12 annotated genes, some of which were related to immune system processes such as DTX3L, PARP9, PARP14, CD86, and HCLS1. This is the first study to explore the associations between homozygous regions with growth and feed efficiency traits in American mink. Our findings shed the light on the effects of homozygosity in the mink genome on growth and feed efficiency traits, that can be utilized in developing a sustainable breeding program for mink.

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding

With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).

Machine learning for genomic and pedigree prediction in sugarcane.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

Exploring unknown parent groups and metafounders in single-step genomic best linear unbiased prediction: Insights from a simulated cattle population

This study explores how the metafounder (MF) concept enhances genetic evaluations in dairy cattle populations using single-step genomic best linear unbiased prediction (ssGBLUP). By improving the consideration of relationships among founder populations, MF ensures accurate alignment of pedigree and genomic relationships. The research aims to propose a method for grouping MF based on genotypic information, assess different approaches for estimating the gamma matrix, and compare unknown parent groups (UPG) and MF methodologies across various scenarios, including those with low and high pedigree completeness based on a simulated dairy cattle population. In the scenario where unknown ancestors are rare, the impact of UPG or MF on breeding values is minimal but MF still performs slightly better compared with UPG. The scenario with lower genotyping rates and more unknown parents shows significant differences in evaluations with and without UPG and also compared with MF. The study shows that ssGBLUP evaluations where UPG are considered via Quaas-Pollak-transformation in the pedigree-based and genomic relationship matrix (UPG_fullQP) results in double counting and subsequently in a pronounced bias and overdispersion. Another focus is on the estimation of the gamma matrix, emphasizing the importance of crossbred genotypes for accuracy. Challenges emerge in classifying animals into subpopulations and further into MF or UPG, but the method used in this study, which is based on genotypes, results in predictions which are comparable to those obtained using the true subpopulations for the assignment. Estimated validation results using the linear regression method confirm the superior performance of MF evaluations, although differences compared with true validations are smaller. Notably, UPG_fullQP's extreme bias is less evident in routine validation statistics.

Fuzzy Neighborhood-Based Manifold Learning and Feature Weight Matrix for Multilabel Feature Selection

In recent years, it has been difficult to determine neighborhood radii with almost all feature selection methodologies, and the existing multilabel feature selection approaches neglect these correlations among labels, which results in poor classification efficiency. To address these flaws, a fuzzy neighborhood-based manifold learning model and a feature weight matrix are developed to study multilabel feature selection in this article. First, the difference coefficient of the given samples and the new classification margin of each sample are defined to construct an adaptive fuzzy neighborhood radius. While considering the influences of features and labels on the similarity relationships of samples, a novel fuzzy neighborhood similarity relationship is developed by combining it with the label similarity approach that uses the Jaccard similarity coefficient. Second, the least-squares regression model is introduced to design a loss function for learning the mapping relationship between the feature and label spaces. To constrain the objective function, a regularization term based on the fuzzy neighborhood similarity relationship matrix is developed to establish a manifold learning model. To reduce the complexity of the loss function and to reduce the induced structural risk, the final objective function is constructed by incorporating two graph Laplacian matrices. Third, positive and negative confidence values are presented to incorporate the frequency parameter and to form the dependencies between labels into a new label correlation matrix. A new feature weight matrix integrated with the dependencies between labels is defined to learn the correlations between features and labels. Finally, an embedded feature selection method with an objective function is designed, where the objective function is optimized by the gradient descent scheme. Experiments conducted on 12 multilabel datasets reveal that the proposed approach yields better classification results than do other algorithms.

Definition of metafounders based on population structure analysis

BackgroundLimitations of the concept of identity by descent in the presence of stratification within a breeding population may lead to an incomplete formulation of the conventional numerator relationship matrix (A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document}). Combining A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} with the genomic relationship matrix (G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document}) in a single-step approach for genetic evaluation may cause inconsistencies that can be a source of bias in the resulting predictions. The objective of this study was to identify stratification using genomic data and to transfer this information to matrix A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document}, to improve the compatibility of A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} and G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document}.MethodsUsing software to detect population stratification (ADMIXTURE), we developed an iterative approach. First, we identified 2 to 40 strata (k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}) with ADMIXTURE, which we then introduced in a stepwise manner into matrix A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document}, to generate matrix AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} using the metafounder methodology. Improvements in consistency between matrix G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} and AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} were evaluated by regression analysis and through the comparison of the overall mean and mean diagonal values of both matrices. The approach was tested on genotype and pedigree information of European and North American Brown Swiss animals (85,249). Analyses with ADMIXTURE were initially performed on the full set of genotypes (S1). In addition, we used an alternative dataset where we avoided sampling of closely related animals (S2).ResultsResults of the regression analyses of standard A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} on G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} were – 0.489, 0.780 and 0.647 for intercept, slope and fit of the regression. When analysing S1 data results of the regression for AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} on G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} corresponding values were – 0.028, 1.087 and 0.807 for k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}=7, while there was no clear optimum k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}. Analyses of S2 gave a clear optimal k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}=24, with − 0.020, 0.998 and 0.817 as results of the regression. For this k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document} differences in mean and mean diagonal values between both matrices were negligible.ConclusionsThe derivation of hidden stratification information based on genotyped animals and its integration into A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} improved compatibility of the resulting AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} and G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} considerably compared to the initial situation. In dairy breeding populations with large half-sib families as sub-structures it is necessary to balance the data when applying population structure analysis to obtain meaningful results.

YQTL Pipeline: A structured computational workflow for large scale quantitative trait loci discovery and downstream visualization.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.

Evaluating the inhibitors in the growth of high-speed railway in India: A multi-stakeholder perspective

The expansion of the high speed railway (HSR) network has made vital contributions in improving intercity travel around the globe. Despite being promoted by governments across the globe, multiplicity of barriers has prevented the penetration of HSR services in developing economies apart from China. Many studies have emphasized on the advantages of HSR services but have failed to highlight the challenges in developing an efficient HSR network. This study aims to identify and develop interrelationships between the major inhibitors in the growth of HSR in a developing economy such as India considering the perspective of multiple stakeholders. The stakeholders considered for the present study are academic researchers, Indian railway officials and ancillary companies involved in the construction of HSR. Thirteen major inhibitors are identified after consulting all the stakeholders and a blended methodology consisting of Fuzzy theory as well as decision-making trial and evaluation laboratory method (Fuzzy-DEMATEL) is used to develop the inter-relationships. All stakeholders highlighted some common inhibitors such as financial viability of the HSR project, financial health of Indian Railways and the existing infrastructure as the major challenges in growth of HSR in India. Furthermore, strategies have been identified to mitigate these inhibitors to help the policy makers. Strategies have been proposed and a relationship (inhibitor-strategy) matrix has been developed to understand the acceptability of these strategies. The major contribution of the present study is that no study has considered different stakeholder's perspectives in identifying the inhibitors. Furthermore, the policy makers can directly use the findings of this study for HSR growth in developing economies.

Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP.

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

A Robust CoS-PVNet Pose Estimation Network in Complex Scenarios

Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors such as occlusion, noise, weak texture, and lighting changes may affect the accuracy and robustness of object 6D pose estimation. We propose a robust CoS-PVNet (complex scenarios pixel-wise voting network) pose estimation network for complex scenes. By adding a pixel-weight layer based on the PVNet network, more accurate pixel point vectors are selected, and dilated convolution and adaptive weighting strategies are used to capture local and global contextual information of the input feature map. At the same time, the perspective-n-point localization algorithm is used to accurately locate 2D key points to solve the pose of 6D objects, and then, the transformation relationship matrix of 6D pose projection is solved. The research results indicate that on the LineMod and Occlusion LineMod datasets, CoS-PVNet has high accuracy and can achieve stable and robust 6D pose estimation even in complex scenes.

Effects of breed and genomic inbreeding on milk, fat and protein lactation yields and fertility traits in pasture-based dairy cows in Argentina

Abstract The objective of the current study was to evaluate the effects of breed and genomic inbreeding on 305-day lactation yields of milk, fat and protein; and fertility traits of pasture-based dairy cows in Argentina. The genomic inbreeding and heterozygosity of 890 first-lactation cows and 27 bulls were calculated through methods based on the genomic relationship matrix and run of homozygosity using 44 174 single-nucleotide polymorphisms. Cows were classified into four breed groups: Holstein, Holstein crossbred, Holstein–Jersey crossbred and Jersey crossbred. The effect of genomic inbreeding was not significant on production traits, but inbred cows increased 3.0 days calving to conception interval (CCI) per 1% genomic inbreeding. On average, purebred Holstein cows produced 1119 kg milk, 22 kg fat and 30 kg protein more than Jersey crossbred cows. In the case of the fertility traits, Jersey crossbred cows had 45 days shorter CCI than purebred Holstein cows. A possible reason for the non-significant effects of genomic inbreeding of production and fertility traits is that these effects were evaluated in a crossbred population in which rates of heterozygosity would operate to some extent in the opposite direction to rates of genomic inbreeding.