Publications by authors named "Federico Iuricich"

Unstructured meshes are characterized by data points irregularly distributed in the Euclidian space. Due to the irregular nature of these data, computing connectivity information between the mesh elements requires much more time and memory than on uniformly distributed data. To lower storage costs, dynamic data structures have been proposed.

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Slowed information processing speed is a defining feature of cognitive aging. Nucleus locus coeruleus (LC) and medial prefrontal regions are targets for understanding slowed processing speed because these brain regions influence neural and behavioral response latencies through their roles in optimizing task performance. Although structural measures of medial prefrontal cortex have been consistently related to processing speed, it is unclear if 1) declines in LC structure underlie this association because of reciprocal connections between LC and medial prefrontal cortex, or 2) if LC declines provide a separate explanation for age-related changes in processing speed.

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Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~ 8.

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The ability to map speech sounds to corresponding letters is critical for establishing proficient reading. People vary in this phonological processing ability, which has been hypothesized to result from variation in hemispheric asymmetries within brain regions that support language. A cerebral lateralization hypothesis predicts that more asymmetric brain structures facilitate the development of foundational reading skills like phonological processing.

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Structural asymmetries in language-related brain regions have long been hypothesized to underlie hemispheric language laterality and variability in language functions. These structural asymmetries have been examined using voxel-level, gross volumetric, and surface area measures of gray matter and white matter. Here we used deformation-based and persistent homology approaches to characterize the three-dimensional topology of brain structure asymmetries within language-related areas that were defined in functional neuroimaging experiments.

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Unstructured data are collections of points with irregular topology, often represented through simplicial meshes, such as triangle and tetrahedral meshes. Whenever possible such representations are avoided in visualization since they are computationally demanding if compared with regular grids. In this work, we aim at simplifying the encoding and processing of simplicial meshes.

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Persistent homology is a fundamental tool in topological data analysis used for the most diverse applications. Information captured by persistent homology is commonly visualized using scatter plots representations. Despite being widely adopted, such a visualization technique limits user understanding and is prone to misinterpretation.

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