182,196 research outputs found
Digital Three-Dimensional Atlas of Quail Development Using High-Resolution MRI
We present an archetypal set of three-dimensional digital atlases of the quail embryo based on microscopic magnetic resonance imaging (”MRI). The atlases are composed of three modules: (1) images of fixed ex ovo quail, ranging in age from embryonic day 5 to 10 (e05 to e10); (2) a coarsely delineated anatomical atlas of the ”MRI data; and (3) an organ systemâbased hierarchical graph linked to the anatomical delineations. The atlas is designed to be accessed using SHIVA, a free Java application. The atlas is extensible and can contain other types of information including anatomical, physiological, and functional descriptors. It can also be linked to online resources and references. This digital atlas provides a framework to place various data types, such as gene expression and cell migration data, within the normal three-dimensional anatomy of the developing quail embryo. This provides a method for the analysis and examination of the spatial relationships among the different types of information within the context of the entire embryo
Coordinates and maps of the Apollo 17 landing site
We carried out an extensive cartographic analysis of the Apollo 17 landing site and determined and mapped positions of the astronauts, their equipment, and lunar landmarks with accuracies of better than ±1 m in most cases. To determine coordinates in a lunar bodyâfixed coordinate frame, we applied least squares (2âD) network adjustments to angular measurements made in astronaut imagery (Hasselblad frames). The measured angular networks were accurately tied to lunar landmarks provided by a 0.5 m/pixel, controlled Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) orthomosaic of the entire TaurusâLittrow Valley. Furthermore, by applying triangulation on measurements made in Hasselblad frames providing stereo views, we were able to relate individual instruments of the Apollo Lunar Surface Experiment Package (ALSEP) to specific features captured in LROC imagery and, also, to determine coordinates of astronaut equipment or other surface features not captured in the orbital images, for example, the deployed geophones and Explosive Packages (EPs) of the Lunar Seismic Profiling Experiment (LSPE) or the Lunar Roving Vehicle (LRV) at major sampling stops. Our results were integrated into a new LROC NACâbased Apollo 17 Traverse Map and also used to generate a series of largeâscale maps of all nine traverse stations and of the ALSEP area. In addition, we provide crater measurements, profiles of the navigated traverse paths, and improved ranges of the sources and receivers of the active seismic experiment LSPE
Interpretable statistics for complex modelling: quantile and topological learning
As the complexity of our data increased exponentially in the last decades, so has our
need for interpretable features. This thesis revolves around two paradigms to approach
this quest for insights.
In the first part we focus on parametric models, where the problem of interpretability
can be seen as a âparametrization selectionâ. We introduce a quantile-centric
parametrization and we show the advantages of our proposal in the context of regression,
where it allows to bridge the gap between classical generalized linear (mixed)
models and increasingly popular quantile methods.
The second part of the thesis, concerned with topological learning, tackles the
problem from a non-parametric perspective. As topology can be thought of as a way
of characterizing data in terms of their connectivity structure, it allows to represent
complex and possibly high dimensional through few features, such as the number of
connected components, loops and voids. We illustrate how the emerging branch of
statistics devoted to recovering topological structures in the data, Topological Data
Analysis, can be exploited both for exploratory and inferential purposes with a special
emphasis on kernels that preserve the topological information in the data.
Finally, we show with an application how these two approaches can borrow strength
from one another in the identification and description of brain activity through fMRI
data from the ABIDE project
An Algorithm for Cellular Reprogramming
The day we understand the time evolution of subcellular elements at a level
of detail comparable to physical systems governed by Newton's laws of motion
seems far away. Even so, quantitative approaches to cellular dynamics add to
our understanding of cell biology, providing data-guided frameworks that allow
us to develop better predictions about and methods for control over specific
biological processes and system-wide cell behavior. In this paper we describe
an approach to optimizing the use of transcription factors in the context of
cellular reprogramming. We construct an approximate model for the natural
evolution of a synchronized population of fibroblasts, based on data obtained
by sampling the expression of some 22,083 genes at several times along the cell
cycle. (These data are based on a colony of cells that have been cell cycle
synchronized) In order to arrive at a model of moderate complexity, we cluster
gene expression based on the division of the genome into topologically
associating domains (TADs) and then model the dynamics of the expression levels
of the TADs. Based on this dynamical model and known bioinformatics, we develop
a methodology for identifying the transcription factors that are the most
likely to be effective toward a specific cellular reprogramming task. The
approach used is based on a device commonly used in optimal control. From this
data-guided methodology, we identify a number of validated transcription
factors used in reprogramming and/or natural differentiation. Our findings
highlight the immense potential of dynamical models models, mathematics, and
data guided methodologies for improving methods for control over biological
processes
- âŠ