226 research outputs found
New particle-hole symmetries and the extended interacting boson model
We describe shape coexistence and intruder many-particle-hole (mp-nh)excitations in the extended interacting boson model EIBM and EIBM-2,combining both the particle-hole and the charge degree of freedom.Besides the concept of I-spin multiplets and subsequently multiplets, we touch upon the existence of particle-hole mixed symmetry states. We furthermore describe regular and intrudermany-particle-hole excitations in one nucleus on an equal footing, creating (annihilating) particle-hole pairs using the K-spin operatorand studying possible mixing between these states. As a limiting case,we treat the coupling of two IBM-1 Hamiltonians, each decribing the regular and intruder excitations respectively, in particular lookingat the - dynamical symmetry coupling. We apply such coupling scheme to the Po isotopes
Shape coexistence in atomic nuclei and its spectroscopic fingerprints
In the present discussion we concentrate on shape coexistence asobtained within a deformed single-particle field as well as startingfrom the spherical shell-model, incorporating deformationeffects via the residual proton-neutron quadrupole interaction. Wediscuss in particular the appearance of shape coexisting phenomena inthe Pb region. In a second part then, we present a number ofexperimental fingerprints that allow to recognize the appearance ofshape coexisting phenomena or of shape mixing through the use ofselective experiments (e.g. band structure, spectroscopic factors,static moments, E0 properties and alpha-decay)
Algorithm for defining skeletal structures in biomedical models
La descripción del comportamiento mecánico de tejidos duros mediante el empleo de modelos discretos pasa por diferentes etapas de análisis, desde el procesamiento digital de la imagen hasta la especificación de las propiedades físicas del tejido. Para ello, es necesario tener en cuenta un elemento clave: la descomposición del modelo en sus partes constitutivas. Se realizó un estudio bibliográfico de diversas propuestas para realizar la descomposición y se llegó a la conclusión de la inexistencia de una estrategia única. Existe un cúmulo de propuestas genéricas, pero estas no ofrecen una solución válida a los casos analizados, correspondientes a las articulaciones de la rodilla, la pelvis y el hombro. Por tanto, se propone un algoritmo para realizar la descomposición mediante el análisis de las relaciones espaciales entre los contornos presentes en planos consecutivos, que se basa en 4 etapas: la lectura de los cortes de imágenes de tomografía computarizada; la determinación de los contornos que definen el tejido óseo presente en cada corte; el agrupamiento de los contornos cuya relación espacial cumple un criterio determinado, y la eliminación de los volúmenes no válidos.
Los resultados del algoritmo se compararon con otros obtenidos mediante el empleo de la librería Visualization ToolKit (VTK) y pyFormex, cuyos métodos se utilizan en la visualización y análisis de imágenes médicas y en la modelación de estructuras tridimensionales. Como resultado del algoritmo propuesto tenemos —bajo las mismas condiciones y en un corto tiempo de procesamiento— una descomposición de los modelos anatómicos superior a la realizada por VTK y pyFormex, con aproximadamente el 90% de confianza.Description of mechanical behavior of hard tissues by means of discrete models goes through various stages of analysis, which range from digital image processing to the specification of physical properties of tissue to the discrete model. This requires taking into account a key element: the decomposition of the model into its constituent parts. We conducted a bibliographic study of existing proposals for such decomposition, leading to the conclusion of the absence of a single strategy. There are several generic proposals, but these proved not to give a valid solution applicable to the cases examined corresponding to the articulations of the knee, hip and shoulder. In this paper we propose an algorithm to perform this decomposition by analyzing the spatial relationships between the contours present in consecutive planes. It is based on four stages: reading computer tomography (CT) slices; determining the contours that define bone tissue present on each slice; grouping of contours whose relationship meets a given criterion; and eliminating non-valid volumes.
Results were compared with those obtained by means of Visualization ToolKit (VTK) and pyFormex, widely used in the visualization and analysis of medical imaging and modeling three-dimensional structures. As a main result, proposed algorithm under the same conditions and short processing time performs a better decomposition of anatomical models than the one made by VTK and pyFormex, with about a 90% of confidence.Peer Reviewe
Obtaining foot bone structure applying global and adaptive thresholding
La descripción del comportamiento mecánico de tejidos duros mediante el empleo de modelos discretos pasa por diferentes etapas de análisis, que van desde el procesamiento digital de la imagen hasta la especificación de las propiedades físicas del tejido al modelo discreto. Para lograr un buen resultado es esencial la descomposición de esos modelos en sus partes constitutivas.
En este trabajo se discute un método para la descripción geométrica de los huesos del pie a partir de una secuencia de imágenes (cortes) de tomografía computarizada (TC).
La investigación propone la combinación de la umbralización global y de la adaptativa para la determinación del dominio geométrico de los huesos en cada corte, así como el análisis de las relaciones espaciales entre contornos en planos consecutivos a fin de obtener las isosuperficies de los huesos.
Se propone un algoritmo semiautomático basado en 4 etapas: la lectura de los cortes de imágenes de TC; la determinación de los contornos que definen el tejido óseo presentes en cada corte; la formación de los volúmenes a través del agrupamiento de los contornos cuya relación espacial cumple un criterio determinado; y la eliminación de las isosuperficies no válidas.
Como resultado se obtiene la definición de la mayoría de los huesos del pie cuyo rango de valores en la escala de Hounsfield es [–1.000; 1.383].The description of the mechanical behavior of hard tissues by means of discrete models goes through various stages of analysis, which range from digital image processing to the specification of tissues physical properties to the discrete model. To achieve good results it is essential to decompose these models into their constituent parts.
In this paper we discuss a method for geometrical description of foot bones from a sequence of computed tomography (CT) images.
This research proposes a combination between global and adaptive thresholdings to determine the geometric domain of bones in each slice and the analysis of the spatial relationships between contours in consecutive planes in order to obtain bones’ isosurfaces.
The algorithm proposed is based on 4 stages: the reading of computed tomography (CT) images; the determination of the contours that define the bone tissue present on each slice; the grouping of contours whose relationship meet a given criteria; the elimination of non-valid volumes.
As a result, it is possible to obtain the geometrical domain of a great number of foot bones whose range in the Hounsfield is [–1000; 1383].Peer Reviewe
Proximity at a distance:The relationship between foreign subsidiary co-location and MNC headquarters board interlock formation
Mdct imaging before transcutanous aortic valve implantation: rationale and measurements
Since its introduction in 2002, transcatheter aortic valve implantation (TAVI) has assumed growing importance in the treatment of patients with severe aortic stenosis (AS), because it offers a much less invasive alternative for those in high risk for surgery. Good early results and advances in percutaneous valve technology have led to a substantial increase in procedural success rate and number of patients undergoing this less invasive treatment. Pre-procedural screening of several anatomic factors to assess the feasibility of this technique is important. Multidetector row computed tomography (MDCT) is the technique of choice in assessing these factors. This technical note aims to describe and illustrate the key elements that need to be evaluated before the procedure
Using machine learning to characterize heart failure across the scales
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain 52.7% of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies
Coronary fractional flow reserve measurements of a stenosed side branch: a computational study investigating the influence of the bifurcation angle
Background Coronary hemodynamics and physiology specific for bifurcation lesions was not well understood. To investigate the influence of the bifurcation angle on the intracoronary hemodynamics of side branch (SB) lesions computational fluid dynamics simulations were performed. Methods A parametric model representing a left anterior descending—first diagonal coronary bifurcation lesion was created according to the literature. Diameters obeyed fractal branching laws. Proximal and distal main branch (DMB) stenoses were both set at 60 %. We varied the distal bifurcation angles (40°, 55°, and 70°), the flow splits to the DMB and SB (55 %:45 %, 65 %:35 %, and 75 %:25 %), and the SB stenoses (40, 60, and 80 %), resulting in 27 simulations. Fractional flow reserve, defined as the ratio between the mean distal stenosis and mean aortic pressure during maximal hyperemia, was calculated for the DMB and SB (FFRSB) for all simulations. Results The largest differences in FFRSB comparing the largest and smallest bifurcation angles were 0.02 (in cases with 40 % SB stenosis, irrespective of the assumed flow split) and 0.05 (in cases with 60 % SB stenosis, flow split 55 %:45 %). When the SB stenosis was 80 %, the difference in FFRSB between the largest and smallest bifurcation angle was 0.33 (flow split 55 %:45 %). By describing the ΔPSB−QSB relationship using a quadratic curve for cases with 80 % SB stenosis, we found that the curve was steeper (i.e. higher flow resistance) when bifurcation angle increases (ΔP = 0.451*Q + 0.010*Q 2 and ΔP = 0.687*Q + 0.017*Q 2 for 40° and 70° bifurcation angle, respectively). Our analyses revealed complex hemodynamics in all cases with evident counter-rotating helical flow structures. Larger bifurcation angles resulted in more pronounced helical flow structures (i.e. higher helicity intensity), when 60 or 80 % SB stenoses were present. A good correlation (R2 = 0.80) between the SB pressure drop and helicity intensity was also found. Conclusions Our analyses showed that, in bifurcation lesions with 60 % MB stenosis and 80 % SB stenosis, SB pressure drop is higher for larger bifurcation angles suggesting higher flow resistance (i.e. curves describing the ΔPSB−QSB relationship being steeper). When the SB stenosis is mild (40 %) or moderate (60 %), SB resistance is minimally influenced by the bifurcation angle, with differences not being clinically meaningful. Our findings also highlighted the complex interplay between anatomy, pressure drops, and blood flow helicity in bifurcations
Good Random Matrices over Finite Fields
The random matrix uniformly distributed over the set of all m-by-n matrices
over a finite field plays an important role in many branches of information
theory. In this paper a generalization of this random matrix, called k-good
random matrices, is studied. It is shown that a k-good random m-by-n matrix
with a distribution of minimum support size is uniformly distributed over a
maximum-rank-distance (MRD) code of minimum rank distance min{m,n}-k+1, and
vice versa. Further examples of k-good random matrices are derived from
homogeneous weights on matrix modules. Several applications of k-good random
matrices are given, establishing links with some well-known combinatorial
problems. Finally, the related combinatorial concept of a k-dense set of m-by-n
matrices is studied, identifying such sets as blocking sets with respect to
(m-k)-dimensional flats in a certain m-by-n matrix geometry and determining
their minimum size in special cases.Comment: 25 pages, publishe
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Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset
Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics
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