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2012

Stability analysis of a variant of the Prony method

Rodney Jaramillo, Marianela Lentini

 

Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2012, Article ID 390645

 

Prony type methods are used in many engineering applications to determine the exponential fit corresponding to a dataset. In this paper we study a variant of Prony’s method that was used by Mart´ın-Landrove et al., in a process of segmentation of T2-weighted MRI brain images. We show the equivalence between that method and the classical Prony method and study the stability of the computed solutions with respect to noise in the data set. In particular, we show that the relative error in the calculation of the exponential fit parameters is linear with respect to noise in the data. Our analysis is based on classical results from linear algebra, matrix computation theory, and the theory of stability for roots of polynomials.

Differentiation by +HRMN spectroscopy of Klebsiella pneumoniae, Staphylococcus aureus and Streptococcus agalactiae in culture

N. Polanco, E. Leal, M. Martín-Landrove, S. Pekerar

 

VITAE, Academia Biomédica Digital, No 50, 2012 [article in spanish]

 

In spite of several methods of bacterial identification existing today, problems still persist in the practice of routine clinical laboratory, for this reason, we consider differentiating, in liquid culture, some pathogenic bacteria in humans by means of +HNMR spectroscopy . Strains of Klebsiella pneumoniae, Staphylococcus aureus and Streptococcus agalactiae, were isolated from patients with different infectious diseases and identified by conventional methods. Later were they cultivated in broth brain-heart- infusion (BBHI), during 18-24 h to 37 ° C in 10% of CO2 and analysed by spectroscopy +HNMR. The spectra were processed using the matNMR v. 2.7 software (MATLAB ®). Signals which by their chemical displacement and/or intensity facilitate the differentiation of the species and its culture medium, are found in the aliphatic region. Conclusion: Despite the large number of signals shown by the different species and the BBHI, Klebsiella pneumoniae, Staphylococcus aureus and Streptococcus agalactiae were differentiated by +HNMR spectroscopy, directly in BBHI.

Brain tumors: A scaling analysis approach

Francisco Torres Hoyos, Miguel Martín-Landrove

 

Avances en Simulación Computacional y Modelado Numérico, E. Dávila, G. Uzcátegui, M. Cerrolaza (Eds.) SVMNI 2012

 

A new method, based in scaling analysis, is used to calculate fractal dimension and local roughness exponent to characterize in vivo 3-D tumor growth in brain. Image acquisition was made according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal T1–weighted images, comprising brain volume for further magnetic resonance image (MRI) registration. Image segmentation was performed by application of kmeans procedure upon contrasted images. Tumors analyzed included glioblastomas, astrocytomas, metastases and benign brain tumors. The results show significant variations of the parameters according to tumor stage and histological origin.

3-D in vivo brain tumor geometry study by scaling analysis

F. Torres Hoyos, M. Martín-Landrove

 

Physica A 391 (2012) 1195–1206

 

A new method, based on scaling analysis, is used to calculate fractal dimension and local roughness exponents to characterize in vivo 3-D tumor growth in the brain. Image acquisition was made according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal magnetic resonance T1-weighted images, and comprising the brain volume for image registration. Image segmentation was performed by the application of the k-means procedure upon contrasted images. We analyzed glioblastomas, astrocytomas, metastases and benign brain tumors. The results show significant variations of the parameters depending on the tumor stage and histological origin.

 

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Amino acid fermentation at the origin of the genetic code

Harold P. de Vladar

 

Biology Direct 2012 7:6

 

There is evidence that the genetic code was established prior to the existence of proteins, when metabolism was powered by ribozymes. Also, early proto-organisms had to rely on simple anaerobic bioenergetic processes. In this work I propose that amino acid fermentation powered metabolism in the RNA world, and that this was facilitated by proto-adapters, the precursors of the tRNAs. Amino acids were used as carbon sources rather than as catalytic or structural elements. In modern bacteria, amino acid fermentation is known as the Stickland reaction. This pathway involves two amino acids: the first undergoes oxidative deamination, and the second acts as an electron acceptor through reductive deamination. This redox reaction results in two keto acids that are employed to synthesise ATP via substrate-level phosphorylation. The Stickland reaction is the basic bioenergetic pathway of some bacteria of the genus Clostridium. Two other facts support Stickland fermentation in the RNA world. First, several Stickland amino acid pairs are synthesised in abiotic amino acid synthesis. This suggests that amino acids that could be used as an energy substrate were freely available. Second, anticodons that have complementary sequences often correspond to amino acids that form Stickland pairs. The main hypothesis of this paper is that pairs of complementary proto-adapters were assigned to Stickland amino acids pairs. There are signatures of this hypothesis in the genetic code. Furthermore, it is argued that the proto-adapters formed double strands that brought amino acid pairs into proximity to facilitate their mutual redox reaction, structurally constraining the anticodon pairs that are assigned to these amino acid pairs. Significance tests which randomise the code are performed to study the extent of the variability of the energetic (ATP) yield. Random assignments can lead to a substantial yield of ATP and maintain enough variability, thus selection can act and refine the assignments into a proto-code that optimises the energetic yield. Monte Carlo simulations are performed to evaluate the establishment of these simple proto-codes, based on amino acid substitutions and codon swapping. In all cases, donor amino acids are assigned to anticodons composed of U+G, and have low redundancy (1-2 codons), whereas acceptor amino acids are assigned to the the remaining codons. These bioenergetic and structural constraints allow for a metabolic role for amino acids before their co-option as catalyst cofactors.

 

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Medical Image Rendering and Description Driven by Semantic Annotations

Alexandra La Cruz, Alexander Baranya, Maria-Esther Vidal

International Workshop on Resource Discovery RED 2012: Resource Discovery pp 123-149

 

Image-driven medical applications can aid medical experts to visualize tissues and organs, and thus facilitate the task of identifying anomalies and tumors. However, to ensure reliable results, regions of the image that enclose the organs or tissues of interest have to be precisely visualized. Volume rendering is a technique for visualizing volumetric data by computing a 2D projection of the image. Traditionally, volume rendering generates a semi-transparent image, enhancing the description of the area of interest to be visualized. Particularly during the visualization of medical images, identification of areas of interest depends on existing characterizations of the tissues, their corresponding intensities, and the medical image acquisition modality, e.g., Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). However, a precise classification of a tissue requires specialized segmentation processes to distinguish neighboring tissues that share overlapped intensities. Semantic annotations of ontologies such as, RadLex and the Foundational Model of Anatomy (FMA), conceptually allow the annotation of areas that enclose particular tissues. This may impact on the segmentation process or the volume rendering quality. We survey state-of-the-art approaches that support medical image discovery and visualization based on semantic annotations, and show the benefits of semantically encoding medical images for volume rendering. As a proof of concept, we present ANISE (an ANatomIc SEmantic annotator) a framework for the semantic annotation of medical images. Finally, we describe the improvements achieved by ANISE during the rendering of a benchmark of medical images, enhancing segmented part of the organs and tissues that comprise the studied images.

 

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