Aegilops tauschii is the diploid progenitor of the D genome of hexaploid wheat 1 (Triticum aestivum, genomes AABBDD) and an important genetic resource for wheat [2][3][4] . The large size and highly repetitive nature of the Ae. tauschii genome has until now precluded the development of a reference-quality genome sequence 5 .Here we use an array of advanced technologies, including orderedclone genome sequencing, whole-genome shotgun sequencing, and BioNano optical genome mapping, to generate a referencequality genome sequence for Ae. tauschii ssp. strangulata accession AL8/78, which is closely related to the wheat D genome. We show that compared to other sequenced plant genomes, including a much larger conifer genome, the Ae. tauschii genome contains unprecedented amounts of very similar repeated sequences. Our genome comparisons reveal that the Ae. tauschii genome has a greater number of dispersed duplicated genes than other sequenced genomes and its chromosomes have been structurally evolving an order of magnitude faster than those of other grass genomes.

Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered for example as movement trajectories on the surface of the earth, are an important special case. We consider an intrinsic principal component analysis for smooth Riemannian manifold-valued functional data and study its asymptotic properties. Riemannian functional principal component analysis (RFPCA) is carried out by first mapping the manifold-valued data through Riemannian logarithm maps to tangent spaces around the time-varying Fréchet mean function, and then performing a classical multivariate functional principal component analysis on the linear tangent spaces. Representations of the Riemannian manifold-valued functions and the eigenfunctions on the original manifold are then obtained with exponential maps. The tangent-space approximation through functional principal component analysis is shown to be well-behaved in terms of controlling the residual variation if the Riemannian manifold has nonnegative curvature. Specifically, we derive a central limit theorem for the mean function, as well as root-n uniform convergence rates for other model components, including the covariance function, eigenfunctions, and functional principal component scores. Our applications include a novel framework for the analysis of longitudinal compositional data, achieved by mapping longitudinal compositional data to trajectories on the sphere, illustrated with longitudinal fruit fly behavior patterns. Riemannian functional principal component analysis is shown to be superior in terms of trajectory recovery in comparison to an unrestricted functional principal component analysis in applications and simulations and is also found to produce principal component scores that are better predictors for classification compared to traditional functional functional principal component scores.

Homology was searched with genes annotated in the Aegilops tauschii pseudomolecules against genes annotated in the pseudomolecules of tetraploid wild emmer wheat, Brachypodium distachyon, sorghum and rice. Similar searches were performed with genes annotated in the rice pseudomolecules. Matrices of collinear genes and rearrangements in their order were constructed. Optical BioNano genome maps were constructed and used to validate rearrangements unique to the wild emmer and Ae. tauschii genomes. Most common rearrangements were short paracentric inversions and short intrachromosomal translocations. Intrachromosomal translocations outnumbered segmental intrachromosomal duplications. The densities of paracentric inversion lengths were approximated by exponential distributions in all six genomes. Densities of collinear genes along the Ae. tauschii chromosomes were highly correlated with meiotic recombination rates but those of rearrangements were not, suggesting different causes of the erosion of gene collinearity and evolution of major chromosome rearrangements. Frequent rearrangements sharing breakpoints suggested that chromosomes have been rearranged recurrently at some sites. The distal 4 Mb of the short arms of rice chromosomes Os11 and Os12 and corresponding regions in the sorghum, B. distachyon and Triticeae genomes contain clusters of interstitial translocations including from 1 to 7 collinear genes. The rates of acquisition of major rearrangements were greater in the large wild emmer wheat and Ae. tauschii genomes than in the lineage preceding their divergence or in the B. distachyon, rice and sorghum lineages. It is suggested that synergy between large quantities of dynamic transposable elements and annual growth habit have been the primary causes of the fast evolution of the Triticeae genomes.

From birth to 5 years of age, brain structure matures and evolves alongside emerging cognitive and behavioral abilities. In relating concurrent cognitive functioning and measures of brain structure, a major challenge that has impeded prior investigation of their time‐dynamic relationships is the sparse and irregular nature of most longitudinal neuroimaging data. We demonstrate how this problem can be addressed by applying functional concurrent regression models (FCRMs) to longitudinal cognitive and neuroimaging data. The application of FCRM in neuroimaging is illustrated with longitudinal neuroimaging and cognitive data acquired from a large cohort (n = 210) of healthy children, 2–48 months of age. Quantifying white matter myelination by using myelin water fraction (MWF) as imaging metric derived from MRI scans, application of this methodology reveals an early period (200–500 days) during which whole brain and regional white matter structure, as quantified by MWF, is positively associated with cognitive ability, while we found no such association for whole brain white matter volume. Adjusting for baseline covariates including socioeconomic status as measured by maternal education (SES‐ME), infant feeding practice, gender, and birth weight further reveals an increasing association between SES‐ME and cognitive development with child age. These results shed new light on the emerging patterns of brain and cognitive development, indicating that FCRM provides a useful tool for investigating these evolving relationships.

The maturation of the myelinated white matter throughout childhood is a critical developmental process that underlies emerging connectivity and brain function. In response to genetic influences and neuronal activities, myelination helps establish the mature neural networks that support cognitive and behavioral skills. The emergence and refinement of brain networks, traditionally investigated using functional imaging data, can also be interrogated using longitudinal structural imaging data. However, few studies of structural network development throughout infancy and early childhood have been presented, likely owing to the sparse and irregular nature of most longitudinal neuroimaging data, which complicates dynamic analysis. Here, we overcome this limitation and investigate through concurrent correlation the co-development of white matter myelination and volume, and structural network development of white matter myelination between brain regions as a function of age, using statistically well-supported methods. We show that the concurrent correlation of white matter myelination and volume is overall positive and reaches a peak at 580 days. Brain regions are found to differ in overall magnitudes and patterns of timevarying association throughout early childhood. We introduce time-dynamic developmental networks based on temporal similarity of association patterns in the levels of myelination across brain regions. These networks reflect groups of brain regions that share similar patterns of evolving intra-regional connectivity, as evidenced by levels of myelination, are biologically interpretable and provide novel visualizations of brain development. Comparing the constructed networks between different maternal education groups, we found that children with higher and lower maternal education differ significantly in the overall magnitude of the time-dynamic correlations.