922 research outputs found
Automated DNA Fragments Recognition and Sizing through AFM Image Processing
No full textThis paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises. The automated approach minimizes processing time with respect to manual and semi-automated DNA sizing. Moreover, the DNA molecule profile recognition can be used to perform further structural analysis. For computer-generated images, the root mean square error incurred by the automated algorithm in the length estimation is 0.6% for a 7.8 nm image pixel size and 0.34% for a 3.9 nm image pixel size. For AFM real images we obtain a distribution of lengths with a standard deviation of 2.3% of mean and a measured average length very close to the real one, with an error around 0.33%
Automated segmentation of tissue images for computerized IHC analysis
No full textThis paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie
Joint co-clustering: co-clustering of genomic and clinical bioimaging data
No full textAbstractFor better understanding the genetic mechanisms underlying clinical observations, and better defining a group of potential candidates for protein-family-inhibiting therapy, it is interesting to determine the correlations between genomic, clinical data and data coming from high resolution and fluorescent microscopy. We introduce a computational method, called joint co-clustering, that can find co-clusters or groups of genes, bioimaging parameters and clinical traits that are believed to be closely related to each other based on the given empirical information. As bioimaging parameters, we quantify the expression of growth factor receptor EGFR/erb-B family in non-small cell lung carcinoma (NSCLC) through a fully-automated computer-aided analysis approach. This immunohistochemical analysis is usually performed by pathologists via visual inspection of tissue samples images. Our fully-automated techniques streamlines this error-prone and time-consuming process, thereby facilitating analysis and diagnosis. Experimental results for several real-life datasets demonstrate the high quantitative precision of our approach. The joint co-clustering method was tested with the receptor EGFR/erb-B family data on non-small cell lung carcinoma (NSCLC) tissue and identified statistically significant co-clusters of genes, receptor protein expression and clinical traits. The validation of our results with the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits and that it is a very promising approach to analyse large-scale biological data and to study multi-factorial genetic pathologies through their genetic alterations
Genital treatment of penile carcinoma.
No full textSquamous penile carcinoma is an uncommon neoplastic disease with an incidence of
one in 100 000 men per year in Western countries. The role of penile-sparing
treatment represents one of the three main issues in management of squamous
carcinoma of the penis. Most authors consider conservative therapy as an
indicated alternative treatment to partial or total penectomy in small size, low
stage and grade tumours. At present, external or interstitial beam radiotherapy
and lasertherapy represent the best available conservative therapeutic
approaches. Another issue is the role of prophylactic inguinal lymphadenectomy in
patients with negative palpable nodes. An early inguinal lymphadenectomy is
indicated especially in patients with a high occult nodal micrometastases risk
(G3 and pT2-4). The third point of discussion is represented by the use of
chemotherapy in patients with metastatic disease. In this stage of disease,
polychemotherapy with cisplatin, methotrexate and bleomycin seems to be more
effective. The small number of patients investigated and the rapid evolution of
the disease make it extremely difficult to carry out suitable perspective
studies
Optimization of wheel characteristic angles by numerical simulation, with verify of Pacejka Formulas
No full textCOMPORTAMENTO DINAMICO DI UN AUTOVEICOLO. INFLUENZA DEGLI ANGOLI CARATTERISTICI DELLE RUOTE
No full text
Optimization of the characteristic angles of front and rear McPherson suspension on a circular track using multibody numerical simulation
No full textThe research reported in this paper aims to simulate the road holding of a virtual vehicle using multibody simulation to estimate the contact forces between tire and ground and the roll motion when cornering. Furthermore, the effect of the characteristic angles on the variation of the forces of the tire in contact with the ground is studied to determine optimal values for these angles. Emphasis is placed on an average-class vehicle, of which both the external dimensions and mass are chosen appropriately, with a McPherson suspension mounted on both the front and rear. The characteristic values of the camber and toe-in angles, in both the front and the rear, are optimized for motion in the curve under constant traction. The results of numerical simulation are compared with results from the theory of stability in the curve (given the vertical configuration of the vehicle)
Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification
No full textRecognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%
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