Hello, and thanks for your question.
In this particular case, the metadata that PLoS supplied for 10.1371/journal.pgen.1009241 had the abstract formatted as follows:
<jats:abstract>
<jats:p><jats:italic>F</jats:italic><jats:sub>ST</jats:sub> and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of <jats:italic>F</jats:italic><jats:sub>ST</jats:sub> and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of <jats:italic>F</jats:italic><jats:sub>ST</jats:sub> to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing <jats:italic>F</jats:italic><jats:sub>ST</jats:sub> and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and <jats:italic>F</jats:italic><jats:sub>ST</jats:sub> when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and <jats:italic>F</jats:italic><jats:sub>ST</jats:sub> estimates.</jats:p>
</jats:abstract>
But, as you’ve noted, when we make that accessible via our XML API, in unixref it looks like this:
<abstract>
<p>
<italic>F</italic>
<sub>ST</sub>
and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of
<italic>F</italic>
<sub>ST</sub>
and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of
<italic>F</italic>
<sub>ST</sub>
to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing
<italic>F</italic>
<sub>ST</sub>
and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and
<italic>F</italic>
<sub>ST</sub>
when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and
<italic>F</italic>
<sub>ST</sub>
estimates.
</p>
</abstract>
in unixsd, it’s similar, but not exactly the same
<jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1">
<jats:p>
<jats:italic>F</jats:italic>
<jats:sub>ST</jats:sub>
and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of
<jats:italic>F</jats:italic>
<jats:sub>ST</jats:sub>
and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of
<jats:italic>F</jats:italic>
<jats:sub>ST</jats:sub>
to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing
<jats:italic>F</jats:italic>
<jats:sub>ST</jats:sub>
and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and
<jats:italic>F</jats:italic>
<jats:sub>ST</jats:sub>
when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and
<jats:italic>F</jats:italic>
<jats:sub>ST</jats:sub>
estimates.
</jats:p>
</jats:abstract>
So, basically, there were no spaces or indents in the original data that the publisher deposited. (sometimes there are - it varies by publisher). We added those in for the XML output, but the same doesn’t apply to the JSON output.
Does that answer your question?