3,149 research outputs found
Fast connected component labeling algorithm: a non voxel-based approach
This paper presents a new approach to achieve connected component labeling on both binary images and volumes by using the Extreme Vertices Model (EVM), a representation model for orthogonal
polyhedra, applied to digital images and volume datasets recently. In contrast with previous techniques, this method does not use a voxel-based approach but deals with the inner sections of the object.Postprint (published version
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Methods and systems for extracting venous pulsation and respiratory information from photoplethysmographs
A system and method for separating a venous component and an arterial component from a red signal and an infrared signal of a PPG sensor is provided. The method uses the second order statistics of venous and arterial signals to separate the venous andarterial signals. After reliable separation of the venous and thearterial component signals,the component signals can be used for different purposes. In a preferred embodiment, the respiratory signal, pattern, and rate are extracted from the separated venous component and a reliable ?ratio of ratios? is extracted for SpO, using only the arterial component of the PPG signals. The disclosed embodiments enable real-time continuous monitoring of respiration pattern/rate and site-independentarterial oxygen saturation.Board of Regents, University of Texas Syste
Mena/VASP and αII-Spectrin complexes regulate cytoplasmic actin networks in cardiomyocytes and protect from conduction abnormalities and dilated cardiomyopathy
BACKGROUND: In the heart, cytoplasmic actin networks are thought to have important roles in mechanical support, myofibrillogenesis, and ion channel function. However, subcellular localization of cytoplasmic actin isoforms and proteins involved in the modulation of the cytoplasmic actin networks are elusive. Mena and VASP are important regulators of actin dynamics. Due to the lethal phenotype of mice with combined deficiency in Mena and VASP, however, distinct cardiac roles of the proteins remain speculative. In the present study, we analyzed the physiological functions of Mena and VASP in the heart and also investigated the role of the proteins in the organization of cytoplasmic actin networks.
RESULTS: We generated a mouse model, which simultaneously lacks Mena and VASP in the heart. Mena/VASP double-deficiency induced dilated cardiomyopathy and conduction abnormalities. In wild-type mice, Mena and VASP specifically interacted with a distinct αII-Spectrin splice variant (SH3i), which is in cardiomyocytes exclusively localized at Z- and intercalated discs. At Z- and intercalated discs, Mena and β-actin localized to the edges of the sarcomeres, where the thin filaments are anchored. In Mena/VASP double-deficient mice, β-actin networks were disrupted and the integrity of Z- and intercalated discs was markedly impaired.
CONCLUSIONS: Together, our data suggest that Mena, VASP, and αII-Spectrin assemble cardiac multi-protein complexes, which regulate cytoplasmic actin networks. Conversely, Mena/VASP deficiency results in disrupted β-actin assembly, Z- and intercalated disc malformation, and induces dilated cardiomyopathy and conduction abnormalities
Temporalized logics and automata for time granularity
Suitable extensions of the monadic second-order theory of k successors have
been proposed in the literature to capture the notion of time granularity. In
this paper, we provide the monadic second-order theories of downward unbounded
layered structures, which are infinitely refinable structures consisting of a
coarsest domain and an infinite number of finer and finer domains, and of
upward unbounded layered structures, which consist of a finest domain and an
infinite number of coarser and coarser domains, with expressively complete and
elementarily decidable temporal logic counterparts.
We obtain such a result in two steps. First, we define a new class of
combined automata, called temporalized automata, which can be proved to be the
automata-theoretic counterpart of temporalized logics, and show that relevant
properties, such as closure under Boolean operations, decidability, and
expressive equivalence with respect to temporal logics, transfer from component
automata to temporalized ones. Then, we exploit the correspondence between
temporalized logics and automata to reduce the task of finding the temporal
logic counterparts of the given theories of time granularity to the easier one
of finding temporalized automata counterparts of them.Comment: Journal: Theory and Practice of Logic Programming Journal Acronym:
TPLP Category: Paper for Special Issue (Verification and Computational Logic)
Submitted: 18 March 2002, revised: 14 Januari 2003, accepted: 5 September
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Helly meets Garside and Artin
A graph is Helly if every family of pairwise intersecting combinatorial balls
has a nonempty intersection. We show that weak Garside groups of finite type
and FC-type Artin groups are Helly, that is, they act geometrically on Helly
graphs. In particular, such groups act geometrically on spaces with convex
geodesic bicombing, equipping them with a nonpositive-curvature-like structure.
That structure has many properties of a CAT(0) structure and, additionally, it
has a combinatorial flavor implying biautomaticity. As immediate consequences
we obtain new results for FC-type Artin groups (in particular braid groups and
spherical Artin groups) and weak Garside groups, including e.g.\ fundamental
groups of the complements of complexified finite simplicial arrangements of
hyperplanes, braid groups of well-generated complex reflection groups, and
one-relator groups with non-trivial center. Among the results are:
biautomaticity, existence of EZ and Tits boundaries, the Farrell-Jones
conjecture, the coarse Baum-Connes conjecture, and a description of higher
order homological and homotopical Dehn functions. As a mean of proving the
Helly property we introduce and use the notion of a (generalized) cell Helly
complex.Comment: Small modifications according to the referee report, updated
references. Final accepted versio
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
Machine learning approaches for early prediction of hypertension.
Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery pressure to diagnose hypertension after its onset. However, this method cannot detect cerebrovascular alterations that lead to adverse events which may occur prior to the onset of hypertension. The early detection and quantification of these cerebral vascular structural changes could help in predicting patients who are at a high risk of developing hypertension as well as other cerebral adverse events. This may enable early medical intervention prior to the onset of hypertension, potentially mitigating vascular-initiated end-organ damage. The goal of this dissertation is to develop a novel efficient noninvasive computer-aided diagnosis (CAD) system for the early prediction of hypertension. The developed CAD system analyzes magnetic resonance angiography (MRA) data of human brains gathered over years to detect and track cerebral vascular alterations correlated with hypertension development. This CAD system can make decisions based on available data to help physicians on predicting potential hypertensive patients before the onset of the disease
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