46 research outputs found

    Scattering of two particles in a one-dimensional lattice

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    This study concerns the two-body scattering of particles in a one-dimensional periodic potential. A convenient ansatz allows for the separation of center-of-mass and relative motion, leading to a discrete Schrodinger equation in the relative motion that resembles a tight-binding model. A lattice Green's function is used to develop the Lippmann-Schwinger equation, and ultimately derive a multiband scattering K matrix which is described in detail in the two-band approximation. Two distinct scattering lengths are defined according to the limits of zero relative quasimomentum at the top and bottom edges of the two-body collision band. Scattering resonances occur in the collision band when the energy is coincident with a bound state attached to another higher or lower band. Notably, repulsive on-site interactions in an energetically closed lower band lead to collision resonances in an excited band

    Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

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    Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)

    Quantification of liver fibrosis—a comparative study

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    Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic

    Ensemble convolutional neural network classification for pancreatic steatosis assessment in biopsy images

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    Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians

    Increased chromosomal radiosensitivity in asymptomatic carriers of a heterozygous BRCA1 mutation

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    Background: Breast cancer risk increases drastically in individuals carrying a germline BRCA1 mutation. The exposure to ionizing radiation for diagnostic or therapeutic purposes of BRCA1 mutation carriers is counterintuitive, since BRCA1 is active in the DNA damage response pathway. The aim of this study was to investigate whether healthy BRCA1 mutations carriers demonstrate an increased radiosensitivity compared with healthy individuals. Methods: We defined a novel radiosensitivity indicator (RIND) based on two endpoints measured by the G2 micronucleus assay, reflecting defects in DNA repair and G2 arrest capacity after exposure to doses of 2 or 4 Gy. We investigated if a correlation between the RIND score and nonsense-mediated decay (NMD) could be established. Results: We found significantly increased radiosensitivity in the cohort of healthy BRCA1 mutation carriers compared with healthy controls. In addition, our analysis showed a significantly different distribution over the RIND scores (p = 0.034, Fisher’s exact test) for healthy BRCA1 mutation carriers compared with non-carriers: 72 % of mutation carriers showed a radiosensitive phenotype (RIND score 1–4), whereas 72 % of the healthy volunteers showed no radiosensitivity (RIND score 0). Furthermore, 28 % of BRCA1 mutation carriers had a RIND score of 3 or 4 (not observed in control subjects). The radiosensitive phenotype was similar for relatives within several families, but not for unrelated individuals carrying the same mutation. The median RIND score was higher in patients with a mutation leading to a premature termination codon (PTC) located in the central part of the gene than in patients with a germline mutation in the 5′ end of the gene. Conclusions: We show that BRCA1 mutations are associated with a radiosensitive phenotype related to a compromised DNA repair and G2 arrest capacity after exposure to either 2 or 4 Gy. Our study confirms that haploinsufficiency is the mechanism involved in radiosensitivity in patients with a PTC allele, but it suggests that further research is needed to evaluate alternative mechanisms for mutations not subjected to NMD

    M3G: Maximum Margin Microarray Gridding

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    <p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M<sup>3</sup>G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M<sup>3</sup>G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p
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