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 $SU(4)$ 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 $U(5)$-$SU(3)$ dynamical symmetry coupling. We apply such coupling scheme to the Po isotopes

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

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

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

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

BACKGROUND: Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards. METHODS: High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically. RESULTS: A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice. CONCLUSIONS: Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts